speech on cyber bullying in english

1 Minute Speech on Cyber Bullying In English

A very good morning to one and all present here. Today, I’ll be giving a small speech on the topic ‘Cyberbullying’. 

Cyber Bullying is a serious criminal offense punishable under the law. Cyberbullying involves invading someone’s privacy virtually in the digital world and robbing one of their mental health thus. It essentially is to harass, threaten, or intimidate someone on the internet. 

Cyber Bullying is the next step for mean bullies- bullying as adults. It is a cheap and vulgar act to do, to hide behind masks and intrude into their personal space, 

Some acts of cyberbullying include sending unwanted messages to someone without their consent, spreading false information and rumours about them, commenting hateful things about them, hacking into one’s accounts, and impersonating them.

Let us all work against cyberbullying for a better future. Report any crimes that you come across immediately to the Anti-Bullying helpline. Say no to cyberbullying!

Thank you. 

Related Posts:

• Random Job Generator [List]
• How to Summarize a Presentation: 5 Easy Steps
• Howl Poem By Allen Ginsberg Summary, Notes and Line by Line Explanation in English
• Mirza Ghalib Shayari on God
• Essay on Internet in English
• Random Compound Word Generator

• High contrast
• Press Centre

Search UNICEF

Cyberbullying: what is it and how to stop it, what teens want to know about cyberbullying..

• Available in:

We brought together UNICEF specialists, international cyberbullying and child protection experts, and teamed up with Facebook, Instagram, Snapchat, TikTok and X to answer some of the most common questions about online bullying and give advice on ways to deal with it. 

What is cyberbullying?

Cyberbullying is bullying with the use of digital technologies. It can take place on social media, messaging platforms, gaming platforms and mobile phones. It is repeated behaviour, aimed at scaring, angering or shaming those who are targeted. Examples include:

• spreading lies about or posting embarrassing photos or videos of someone on social media
• sending hurtful, abusive or threatening messages, images or videos via messaging platforms
• impersonating someone and sending mean messages to others on their behalf or through fake accounts.

Face-to-face bullying and cyberbullying can often happen alongside each other. But cyberbullying leaves a digital footprint – a record that can prove useful and provide evidence to help stop the abuse.

If you are worried about your safety or something that has happened to you online, you can seek help by calling your national helpline . If your country does not have a helpline, please urgently speak to an adult you trust or seek professional support from trained and experienced carers.

The top questions on cyberbullying

• Am I being bullied online? How do you tell the difference between a joke and bullying?
• What are the effects of cyberbullying?
• How can cyberbullying affect my mental health?
• Who should I talk to if someone is bullying me online? Why is reporting important?
• I’m experiencing cyberbullying, but I’m afraid to talk to my parents about it. How can I approach them?
• How can I help my friends report a case of cyberbullying especially if they don’t want to do it?
• How do we stop cyberbullying without giving up access to the internet?
• How do I prevent my personal information from being used to manipulate or humiliate me on social media?
• Is there a punishment for cyberbullying?
• Technology companies don’t seem to care about online bullying and harassment. Are they being held responsible?
• Are there any online anti-bullying tools for children or young people?

1. Am I being bullied online? How do you tell the difference between a joke and bullying?

Unicef: .

All friends joke around with each other, but sometimes it’s hard to tell if someone is just having fun or trying to hurt you, especially online. Sometimes they’ll laugh it off with a “just kidding,” or “don’t take it so seriously.” 

But if you feel hurt or think others are laughing at you instead of with you, then the joke has gone too far. If it continues even after you’ve asked the person to stop and you are still feeling upset about it, then this could be bullying.

And when the bullying takes place online, it can result in unwanted attention from a wide range of people including strangers. Wherever it may happen, if you are not happy about it, you should not have to stand for it.

Call it what you will – if you feel bad and it doesn’t stop, then it’s worth getting help. Stopping cyberbullying is not just about calling out bullies, it’s also about recognizing that everyone deserves respect – online and in real life.

> Back to top

2. What are the effects of cyberbullying?

When bullying happens online it can feel as if you’re being attacked everywhere, even inside your own home. It can seem like there’s no escape. The effects can last a long time and affect a person in many ways:

• Mentally – feeling upset, embarrassed, stupid, even afraid or angry 
• Emotionally – feeling ashamed or losing interest in the things you love
• Physically – tired (loss of sleep), or experiencing symptoms like stomach aches and headaches 

The feeling of being laughed at or harassed by others, can prevent people from speaking up or trying to deal with the problem. In extreme cases, cyberbullying can even lead to people taking their own lives. 

Cyberbullying can affect us in many ways. But these can be overcome and people can regain their confidence and health.

3. How can cyberbullying affect my mental health?

When you experience cyberbullying you might start to feel ashamed, nervous, anxious and insecure about what people say or think about you. This can lead to withdrawing from friends and family, negative thoughts and self-talk, feeling guilty about things you did or did not do, or feeling that you are being judged negatively. Feeling lonely, overwhelmed, frequent headaches, nausea or stomachaches are also common.

You can lose your motivation to do the things that you usually enjoy doing and feel isolated from the people you love and trust. This can perpetuate negative feelings and thoughts which can adversely affect your mental health and well-being.

Skipping school is another common effect of cyberbullying and can affect the mental health of young people who turn to substances like alcohol and drugs or violent behaviour to deal with their psychological and physical pain. Talking to a friend, family member or school counsellor you trust can be a first step to getting help.

The effects of cyberbullying on mental health can vary depending on the medium through which it happens. For example, bullying via text messaging or through pictures or videos on social media platforms has proven to be very harmful for adolescents.   

Cyberbullying opens the door to 24-hour harassment and can be very damaging. That’s why we offer in-app mental health and well-being support through our feature “ Here For You .” This Snapchat portal provides resources on mental health, grief, bullying, harassment, anxiety, eating disorders, depression, stress, and suicidal thoughts. It was developed in partnership with leading international advocacy and mental health organizations to help Snapchatters contend with some very real issues. Still, our foundational piece of guidance for any well-being issue is to talk to someone: a friend, parent, caregiver, trusted adult – anyone whom you trust to listen.

At Snap, nothing is more important than the safety and well-being of our community.  Reach out and tell us how we might be able to help.    

Cyberbullying has the potential of having a negative impact on people's mental health. It's why it's so important that you reach out to someone you trust – whether it's a parent, teacher, friend or caregiver – and let them know what you're going through so that they can help you.

The well-being of our community matters hugely to us, and we recognise that cyberbullying can have an adverse impact on people's mental health. As well as taking strong action against content or behaviour that seeks to shame, bully or harass members of our community, we have partnered with experts to develop our well-being guide to help people learn more about improving their well-being, and keep TikTok a safe and inclusive home for our community.

4. Who should I talk to if someone is bullying me online? Why is reporting important?

If you think you’re being bullied, the first step is to seek help from someone you trust such as your parents, a close family member or another trusted adult.

In your school you can reach out to a counsellor, the sports coach or your favourite teacher – either online or in person.

And if you are not comfortable talking to someone you know, search for a helpline in your country to talk to a professional counsellor.

If the bullying is happening on a social platform, consider blocking the bully and formally reporting their behaviour on the platform itself. Social media companies are obligated to keep their users safe.

For bullying to stop, it needs to be identified and reporting it is key.

It can be helpful to collect evidence – text messages and screen shots of social media posts – to show what’s been going on.

For bullying to stop, it needs to be identified and reporting it is key. It can also help to show the bully that their behaviour is unacceptable.

If you are in immediate danger, then you should contact the police or emergency services in your country.

Facebook/Instagram

At Meta, we take bullying and harassment situations seriously. Bullying and harassment is a unique challenge and one of the most complex issues to address because context is critical. We work hard to enforce against this content while also equipping our community with tools to protect themselves in ways that work best for them.

If you're experiencing bullying online, we encourage you to talk to a parent, teacher or someone else you can trust – you have a right to be safe and supported.

We also make it easy to report bullying directly within Facebook or Instagram. You can send our team a report from a post, comment, story or direct message (DM). Your report is anonymous; the account you reported won’t see who reported them. We have a team who reviews these reports 24/7 around the world in 70+ languages and we will remove anything that violates our policies.

Meta’s Family Center offers resources, insights and expert guidance to help parents, guardians and trusted adults support their teen’s online experiences across our technologies. Additionally, the Meta Safety Center provides bullying prevention resources that can help teens seeking support for issues related to bullying like what to do if you or a friend is being bullied or if you've been called a bully. For educators , we have expert-backed tips on how to proactively handle and work to implement bullying prevention strategies

Bullying is something no one should have to experience, either in person or online. 

Snapchat’s Community Guidelines clearly and explicitly prohibit bullying, intimidation, and harassment of any kind. We don’t want it on the platform; it’s not in keeping with why Snapchat was created and designed. Learn more here .

Letting us know when you experience or witness someone breaking our rules allows us to take action, which helps to protect you and other members of our community. In addition to reporting violating content or behaviour to Snapchat, speak with a friend, parent, caregiver, or other trusted adult. Our goal is for everyone to stay safe and have fun!

Everyone has the right to feel safe and to be treated with respect and dignity. Bullying and harassment are incompatible with the inclusive environment we aim to foster on TikTok. 

If you ever feel someone is bullying you or otherwise being inappropriate, reach out to someone you trust - for example, a parent, a teacher or a caregiver – who can provide support.

We deploy both technology and thousands of safety professionals to help keep bullying off TikTok. We also encourage our community members to make use of the easy in-app reporting tools to alert us if they or someone they know has experienced bullying. You can report videos, comments, accounts and direct messages so that we can take appropriate action and help keep you safe. Reports are always confidential. 

You can find out more in our Bullying Prevention guide for teens, caregivers, and educators on how to identify and prevent bullying, and provide support.

Being the target of bullying online is not easy to deal with. If you are being cyberbullied, the most important thing to do is to ensure you are safe. It’s essential to have someone to talk to about what you are going through. This may be a teacher, another trusted adult, or a parent. Talk to your parents and friends about what to do if you or a friend are being cyberbullied.

We encourage people to report accounts to us that may break our  rules . You can do this on our  Help Center  or through the in-post reporting mechanism by clicking on the “Report a post” option.

Last updated: January 2022.

5. I’m experiencing cyberbullying, but I’m afraid to talk to my parents about it. How can I approach them?

If you are experiencing cyberbullying, speaking to a trusted adult – someone you feel safe talking to – is one of the most important first steps you can take.

Talking to parents isn’t easy for everyone. But there are things you can do to help the conversation. Choose a time to talk when you know you have their full attention. Explain how serious the problem is for you. Remember, they might not be as familiar with technology as you are, so you might need to help them to understand what’s happening.

They might not have instant answers for you, but they are likely to want to help and together you can find a solution. Two heads are always better than one! If you are still unsure about what to do, consider reaching out to other trusted people . There are often more people who care about you and are willing to help than you might think!

6. How can I help my friends report a case of cyberbullying especially if they don’t want to do it?

Anyone can become a victim of cyberbullying. If you see this happening to someone you know, try to offer support.

It is important to listen to your friend. Why don’t they want to report being cyberbullied? How are they feeling? Let them know that they don’t have to formally report anything, but it’s crucial to talk to someone who might be able to help.

Anyone can become a victim of cyberbullying.

Remember, your friend may be feeling fragile. Be kind to them. Help them think through what they might say and to whom. Offer to go with them if they decide to report. Most importantly, remind them that you’re there for them and you want to help.

If your friend still does not want to report the incident, then support them in finding a trusted adult who can help them deal with the situation. Remember that in certain situations the consequences of cyberbullying can be life threatening.

Doing nothing can leave the person feeling that everyone is against them or that nobody cares. Your words can make a difference.

We know that it can be hard to report bullying, but everyone deserves to feel safe online. If your friend is experiencing cyberbullying, encourage them to talk to a parent, a teacher or an adult they trust.

Reporting content or accounts to Facebook or Instagram is anonymous and can help us better keep our platforms safe. Bullying and harassment are highly personal by nature, so in many instances, we need a person to report this behaviour to us before we can identify or remove it. You can report something you experience yourself, but it’s also just as easy to submit a report for one of your friends. You can find more information on how to report something on our How to Report Bullying section  at the Meta Safety Center.

You and your friends may be reluctant to report to a technology platform for any number of reasons, but it’s important to know that reporting on Snapchat is confidential and easy. And remember: You can report Snaps (photos and videos), Chats (messages) and accounts – about your own experiences or on behalf of someone else. 

In the more public places of Snapchat, like Stories and Spotlight, simply press and hold on the piece of content and a card with “Report Tile” (as one option) will appear in red. Click that link and our reporting menu will appear. Bullying and harassment are among the first categories in the reporting list. Just follow the prompts and provide as much information as you can about the incident. We appreciate you doing your part to help us protect the Snapchat community!  

If you believe another member of the TikTok community is being bullied or harassed, there are ways you can provide support. For example, you can make a confidential report on TikTok so that we take appropriate action and help keep your friend safe. 

If you know the person, consider checking in with them and encourage them to read our Bullying Prevention guide so they can find out more information about how to identify bullying behaviour and take action.

If your friends are experiencing cyberbullying, encourage them to talk to a parent, a teacher or an adult they trust.

If a friend of yours does not want to report their experience, you can submit a bystander report  on their behalf. This can include reports of private information , non -consensual nudity  or impersonation.

7. How do we stop cyberbullying without giving up access to the Internet?

Being online has so many benefits. However, like many things in life, it comes with risks that you need to protect against.

If you experience cyberbullying, you may want to delete certain apps or stay offline for a while to give yourself time to recover. But getting off the Internet is not a long-term solution. You did nothing wrong, so why should you be disadvantaged? It may even send the bullies the wrong signal — encouraging their unacceptable behaviour. 

We need to be thoughtful about what we share or say that may hurt others.

We all want cyberbullying to stop, which is one of the reasons reporting cyberbullying is so important. But creating the Internet we want goes beyond calling out bullying. We need to be thoughtful about what we share or say that may hurt others. We need to be kind to one another online and in real life. It's up to all of us!

We’re continuously developing new technologies  to encourage positive interactions and take action on harmful content, and launching new tools to help people have more control over their experience. Here are some tools you can use:

• Comment warnings: When someone writes a caption or a comment that our AI detects as potentially offensive or intended to harass, we will show them an alert that asks them to pause and reflect on whether they would like to edit their language before it’s posted.
• Comment and message controls: Comments with common offensive words, phrases or emojis, and abusive messages or messages from strangers can be automatically hidden or filtered out with the ‘ Hidden words ’ setting, which is defaulted on for all people. If you want an even more personalized experience, you can create a custom list of emojis, words or phrases you don’t want to see, and comments containing these terms won’t appear under your posts and messages will be sent to a filtered inbox. All Instagram accounts have the option to switch off DMs from people they don’t follow. Messenger also gives you the option to ignore a conversation and automatically move it out of your inbox, without having to block the sender.
• Block and Mute: You can always  block  or  mute  an account that is bullying you, and that account will not be notified. When you block someone on Instagram, you’ll also have the option to block other accounts they may have or create, making it more difficult for them to interact with you.
• Restrict: With ‘Restrict,’ you can protect your account from unwanted interactions in a quieter, or more subtle way. Once Restrict is enabled, comments on your posts from a person you have restricted will only be visible to that person. You can choose to view the comment by tapping “See Comment”; approve the comment so everyone can see it; delete it; or ignore it. You won’t receive any notifications for comments from a restricted account.
• Limits:  You can automatically hide comments and DM requests from people who don’t follow you, or who only recently followed you. If you’re going through an influx of unwanted comments or messages — or think you may be about to — you can turn on Limits and avoid it.

Our priority is to foster a welcoming and safe environment where people feel free to express themselves authentically. Our Community Guidelines make clear that we do not tolerate members of our community being shamed, bullied or harassed. 

We use a combination of technology and moderation teams to help us identify and remove abusive content or behaviour from our platform. 

We also provide our community with an extensive range of tools to help them better control their experience – whether it's control over exactly who can view and interact with your content or filtering tools to help you stay in control of comments. You can find out about them on our Safety Centre . 

Since hundreds of millions of people share ideas on X every day, it’s no surprise that we don’t all agree with each other all the time. That’s one of the benefits of a public conversation in that we can all learn from respectful disagreements and discussions.

But sometimes, after you’ve listened to someone for a while, you may not want to hear them anymore. Their right to express themselves doesn’t mean you’re required to listen. If you see or receive a reply you don’t like, unfollow  and end any communication with that account. If the behaviour continues, it is recommended that you block the account . If you continue receiving unwanted, targeted and continuous replies on X, consider reporting the behaviour to X here .

We are also working proactively to protect people using our service through a combination of human review and technology. Learn more about how to feel safer on X here .

8. How do I prevent my personal information from being used to manipulate or humiliate me on social media?

Think twice before posting or sharing anything on digital platforms – it may be online forever and could be used to harm you later. Don’t give out personal details such as your address, telephone number or the name of your school.

Learn about the privacy settings of your favourite social media apps. Here are some actions you can take on many of them: 

• You can decide who can see your profile, send you direct messages or comment on your posts by adjusting your account privacy settings. 
• You can report hurtful comments, messages, photos and videos and request they be removed.
• Besides ‘unfriending’, you can completely block people to stop them from seeing your profile or contacting you.
• You can also choose to have comments by certain people to appear only to them without completely blocking them.
• You can delete posts on your profile or hide them from specific people. 

On most of your favourite social media, people aren't notified when you block, restrict or report them.

9. Is there a punishment for cyberbullying?

Most schools take bullying seriously and will take action against it. If you are being cyberbullied by other students, report it to your school.

People who are victims of any form of violence, including bullying and cyberbullying, have a right to justice and to have the offender held accountable.

Laws against bullying, particularly on cyberbullying, are relatively new and still do not exist everywhere. This is why many countries rely on other relevant laws, such as ones against harassment, to punish cyberbullies.

In countries that have specific laws on cyberbullying, online behaviour that deliberately causes serious emotional distress is seen as criminal activity. In some of these countries, victims of cyberbullying can seek protection, prohibit communication from a specified person and restrict the use of electronic devices used by that person for cyberbullying, temporarily or permanently.

However, it is important to remember that punishment is not always the most effective way to change the behaviour of bullies. Sometimes, focusing on repairing the harm and mending the relationship can be better.

On Facebook, we have a set of  Community Standards , and on Instagram, we have  Community Guidelines . We take action when we are aware of content that violates these policies, like in the case of bullying or harassment, and we are constantly improving our detection tools so we can find this content faster.

Bullying and harassment can happen in many places and come in many different forms from making threats and releasing personally identifiable information to sending threatening messages and making unwanted malicious contact. We do not tolerate this kind of behavior because it prevents people from feeling safe and respected on our apps.

Making sure people don’t see hateful or harassing content in direct messages can be challenging, given they’re private conversations, but we are taking steps to take tougher action when we become aware of people breaking our rules. If someone continues to send violating messages, we will disable their account. We’ll also disable new accounts created to get around our messaging restrictions and will continue to disable accounts we find that are created purely to send harmful messages.

On Snapchat, reports of cyberbullying are reviewed by Snap’s dedicated Trust & Safety teams, which operate around the clock and around the globe. Individuals found to be involved in cyberbullying may be given a warning, their accounts might be suspended or their accounts could be shut down completely. 

We recommend leaving any group chat where bullying or any unwelcome behaviour is taking place and please report the behaviour and/or the account to us.  

Our Community Guidelines define a set of norms and common code of conduct for TikTok and they provide guidance on what is and is not allowed to make a welcoming space for everyone. We make it clear that we do not tolerate members of our community being shamed, bullied or harassed. We take action against any such content and accounts, including removal.

We strongly enforce our rules to ensure all people can participate in the public conversation freely and safely. These rules specifically cover a number of areas including topics such as:

• Child sexual exploitation
• Abuse/harassment
• Hateful conduct
• Suicide or self-harm
• Sharing of sensitive media, including graphic violence and adult content

As part of these rules, we take a number of different enforcement actions when content is in violation. When we take enforcement actions, we may do so either on a specific piece of content (e.g., an individual post or Direct Message) or on an account.

You can find more on our enforcement actions here .

10. Technology companies don’t seem to care about online bullying and harassment. Are they being held responsible?

Technology companies are increasingly paying attention to the issue of online bullying.

Many of them are introducing ways to address it and better protect their users with new tools, guidance and ways to report online abuse.

But it is true that more is needed. Many young people experience cyberbullying every day. Some face extreme forms of online abuse. Some have taken their own lives as a result.

Technology companies have a responsibility to protect their users especially children and young people.

It is up to all of us to hold them accountable when they’re not living up to these responsibilities.

11. Are there any online anti-bullying tools for children or young people?

Each social platform offers different tools (see available ones below) that allow you to restrict who can comment on or view your posts or who can connect automatically as a friend, and to report cases of bullying. Many of them involve simple steps to block, mute or report cyberbullying. We encourage you to explore them.

Social media companies also provide educational tools and guidance for children, parents and teachers to learn about risks and ways to stay safe online.

Also, the first line of defense against cyberbullying could be you. Think about where cyberbullying happens in your community and ways you can help – by raising your voice, calling out bullies, reaching out to trusted adults or by creating awareness of the issue. Even a simple act of kindness can go a long way.

The first line of defense against cyberbullying could be you.

If you are worried about your safety or something that has happened to you online, urgently speak to an adult you trust. Many countries have a special helpline you can call for free and talk to someone anonymously. Visit  United for Global Mental Health to find help in your country.

We have a number of anti-bullying tools across Facebook and Instagram:

• You can block people, including any existing and new accounts they might create.
• You can  mute  an account and that account will not be notified.
• You can limit unwanted interactions for a period of time by automatically hiding comments and message requests from people who don’t follow you, or who only recently followed you.
• You can use ‘ Restrict ’ to discreetly protect your account without that person being notified.
• You can  moderate comments  on your own posts.
• You can  modify your settings  so that only people you follow can send you a direct message.
• We will notify someone when they’re about to post something that might cross the line, encouraging them to reconsider.
• We automatically filter out comments and message requests that don’t go against our Community Guidelines but may be considered inappropriate or offensive. You can also create your own custom list of emojis, words or phrases that you don’t want to see.

For more tips and ideas, visit Instagram’s Safety page and Facebook’s Bullying Prevention Hub . We also offer resources, insights and expert guidance for parents and guardians on our Family Center .

We want teens and young adults to be aware of the blocking and removal functions on Snapchat. Clicking on the person’s avatar will bring up a three-dot menu in the upper right-hand corner. Opening that menu offers the option of “Manage Friendship,” which, in turn, offers the ability to Report, Block or Remove the person as a friend. Know that if you block someone, they will be told that their Snaps and Chats to you will be delivered once the relationship is restored.  

It’s also a good idea to check privacy settings to ensure they continue to be set to the default setting of “Friends Only.” This way, only people you’ve added as Friends can send you Snaps and Chats.  

We also recommend reviewing your Friends’ list from time to time to ensure it includes those people you still want to be friends with on Snapchat.  

Alongside the work that our safety teams do to help keep bullying and harassment off our platform, we provide an extensive range of tools to help you control your TikTok experience. You can find these in full on our Safety Centre . Here are a few highlights:

• You can restrict who comments on your videos to no one, just friends or everyone (for those aged under 16, the everyone setting is not available)
• You can filter all comments or those with specific keywords that you choose. By default, spam and offensive comments are hidden from users when we detect them.
• You can delete or report multiple comments at once, and you can block accounts that post bullying or other negative comments in bulk too, up to 100 at a time.
• A comment prompt asks people to reconsider posting a comment that may be inappropriate or unkind, reminding them of our Community Guidelines and allowing them to edit their comments before sharing.

We want everybody to be safe on X. We continue to launch and improve tools for people to feel safer, be in control and manage their digital footprint. Here are some safety tools anyone on X can use: 

• Select who can reply to your posts  – either everyone, only people you follow or only people you mention
• Mute – removing an account's posts from your timeline without unfollowing or blocking that account
• Block – restricting specific accounts from contacting you, seeing your posts, and following you
• Report – filing a report about abusive behaviour
• Safety mode  – a feature that temporarily blocks accounts for using potentially harmful language or sending repetitive and uninvited replies or mentions.

With special thanks to:  Meta, Snap, TikTok and X (formerly known as Twitter). Last updated: February 2024.

To anyone who has ever been bullied online: You are not alone

TikTok stars Charli and Dixie D'Amelio open up about their personal experience of being bullied and share tips on how to make the internet a better place.

Reporting abuse and safety resources

Facebook instagram kik snapchat, tiktok tumblr wechat whatsapp youtube x, take action to stop cyberbullying.

The consequences of cyberbullying can be devastating. Youth can take action to stop it

5 ways to support your mental health online

Tips on how to look after yourself and others

Contribute to Kindly - help stop cyberbullying

Kindly - A UNICEF initiative to end cyberbullying — one message at a time

Mental health and well-being

Tips and resources to help you support your child and yourself

• Contributors
• Mission and Values
• Submissions
• The Regulatory Review In Depth

Cyberbullying and the Limits of Free Speech

Jamison chung , aaron kaufman , and brianna rauenzahn.

Schools and policymakers confront balancing the protection of cyberbullying victims with free speech.

Bullying poses a pervasive threat to students in primary and secondary schools. This aggressive behavior , which involves a power imbalance between the bully and the victim, can have serious mental, social, and physical health consequences. For example, victims of bullying are at a higher risk of developing anxiety and depression. In severe cases, bullying is even associated with suicidal ideation in victims. Victims of cyberbullying in particular have a higher likelihood of self-harm.

Cyberbullying is bullying through the use of digital devices such as computers and smartphones. Unlike other forms of bullying, the online nature of cyberbullying permits attacks at any time, creates a permanent online record that can impact victims for years, and can be difficult for parents and schools to notice . As internet-based communication continues to rise, the prevalence of cyberbullying is expected to increase .

Currently, no federal laws directly address bullying of any kind. State laws, however, protect individuals against bullying in all 50 states, many of which specifically grapple with the issue of cyberbullying. Despite the existence of state laws, the National Center for Education Statistics reported an increase in cyberbullying in recent years. Cyberbullying often takes place off school grounds and is typically limited to speech, which is more difficult for schools to regulate effectively.

This week’s Saturday Seminar focuses on the challenges associated with legal strategies to address cyberbullying in primary and secondary schools.

• The development of the internet brought massive technological advancements but upset the delicate balance between freedom of speech and freedom from harm, Qasim Rashid writes in the Stetson Law Review . He traces the history of free speech in the United States and how U.S. Supreme Court decisions on hate speech and obscenity restrictions have shaped the current framework for cyberbullying legislation. He argues that physical proximity is an outdated method to assess the harms from free speech and proposes legislative modifications that would criminalize certain intentional online statements “that result in foreseeable proximate harm.” He suggests that these changes could help bridge the gap between America’s “dangerously archaic” free speech model and the realities of the internet.
• Cyberbullying can create a serious threat to the health and safety of victims, Philip Lee of the University of the District of Columbia David A. Clarke School of Law argues in an article in the Utah Law Review . In fact, cyberbullying is so dangerous that it justifies reduced First Amendment free speech protections to aid primary and secondary schools that seek to prevent it, Lee writes . Schools have some flexibility in regulating speech on school grounds, but Lee notes that cyberbullies can target their classmates off school property with increasing ease. To regulate cyberbullying more effectively without giving schools unlimited power to limit students’ free speech, Lee advocates the use of a “foreseeability approach,” which allows schools to regulate speech “if it is reasonably foreseeable that the off-campus speech will reach campus.”
• Most state anti-bullying laws do not allow schools to address fully the complexities of cyberbullying, writes Emily Suski of the University of South Carolina School of Law in the Louisiana Law Review . Although bullying is associated with significant emotional and physical ramifications for victims, many state laws only give schools the power to suspend, expel, or exclude bullies from school, Suski observes . By analyzing cases from the Supreme Court that address student speech and the First Amendment, Suski concludes that the Court’s student speech jurisprudence highlights the inadequacies of current anti-bullying laws but also provides a framework that offers schools more freedom to suppress bullying in the form of speech.
• In an article published in the Akron Law Review , University of Illinois College of Media’s Benjamin Holden notes that courts are split over whether schools have authority to punish cyber-speech, even when it causes a disruption to the learning environment. In addition, the constitutional right to anonymity makes it difficult for minor victims of online bullying to seek legal redress outside of the school system, he explains . Holden proposes a new legal test for revealing the identity of cyberbullies who target minors with “school-related harassment.” If the victim can show that anonymous cyberbullies could be disciplined under applicable law if their identities were known, Holden argues that a court should be able to force an internet service provider to reveal the bullies’ identities.
• In an essay published in the Cornell Law Review , Northeastern University School of Law’s Ari Ezra Waldman concludes that anti-bullying laws alone cannot significantly reduce “bullying, cyberbullying, and suicidal thoughts among” LGBTQ teenagers. He finds that state laws that ban discrimination and promote LGBTQ inclusion more effectively reduce LGBTQ bullying in schools more than anti-bullying laws. Waldman suggests that existing anti-bullying laws are only “one part of a larger socio-legal approach to combating bullying in schools and online.” To reduce bullying, he recommends implementing state laws that protect the equality of LGBTQ individuals.
• Pervasive internet access and the rise of social media use among schoolchildren have led to an increase in suicides attributed to cyberbullying, according to Ronen Perry of the University of Haifa in a forthcoming UC Irvine Law Review article . He analyzes how school administrators are limited in their ability to regulate students’ online conduct both on and off campus by constitutional constraints and federal legislation such as Title VI of the Civil Rights Act . He proposes that increasing civil liability for education supervisors, when paired with technological advancements that allow supervisors to collect and analyze digital information, is an underused regulatory tool that could help address the cyberbullying epidemic in schools and help reduce teen suicide.

The Saturday Seminar is a weekly feature that aims to put into written form the kind of content that would be conveyed in a live seminar involving regulatory experts. Each week,  The Regulatory Review  publishes a brief overview of a selected regulatory topic and then distills recent research and scholarly writing on that topic.

Related Essays

Does the First Amendment Protect AI Generated Speech?

Regulating artificial intelligence disinformation could test the First Amendment’s limits.

The Battle Over Student Rights and Race

Scholar suggests that public school students have a right to receive critical race theory education.

The Limits of Deplatforming

Scholars warn of the potential difficulties of using deplatforming to curb disinformation online.

• Type 2 Diabetes
• Heart Disease
• Digestive Health
• Multiple Sclerosis
• COVID-19 Vaccines
• Occupational Therapy
• Healthy Aging
• Health Insurance
• Public Health
• Patient Rights
• Caregivers & Loved Ones
• End of Life Concerns
• Health News
• Thyroid Test Analyzer
• Doctor Discussion Guides
• Hemoglobin A1c Test Analyzer
• Lipid Test Analyzer
• Complete Blood Count (CBC) Analyzer
• What to Buy
• Editorial Process
• Meet Our Medical Expert Board

Cyberbullying: Everything You Need to Know

• Cyberbullying
• How to Respond

Cyberbullying is the act of intentionally and consistently mistreating or harassing someone through the use of electronic devices or other forms of electronic communication (like social media platforms).

Because cyberbullying mainly affects children and adolescents, many brush it off as a part of growing up. However, cyberbullying can have dire mental and emotional consequences if left unaddressed.

This article discusses cyberbullying, its adverse effects, and what can be done about it.

FangXiaNuo / Getty Images

Cyberbullying Statistics and State Laws

The rise of digital communication methods has paved the way for a new type of bullying to form, one that takes place outside of the schoolyard. Cyberbullying follows kids home, making it much more difficult to ignore or cope.

Statistics 

As many as 15% of young people between 12 and 18 have been cyberbullied at some point. However, over 25% of children between 13 and 15 were cyberbullied in one year alone.

About 6.2% of people admitted that they’ve engaged in cyberbullying at some point in the last year. The age at which a person is most likely to cyberbully one of their peers is 13.

Those subject to online bullying are twice as likely to self-harm or attempt suicide . The percentage is much higher in young people who identify as LGBTQ, at 56%.

Cyberbullying by Sex and Sexual Orientation

Cyberbullying statistics differ among various groups, including:

• Girls and boys reported similar numbers when asked if they have been cyberbullied, at 23.7% and 21.9%, respectively.
• LGBTQ adolescents report cyberbullying at higher rates, at 31.7%. Up to 56% of young people who identify as LGBTQ have experienced cyberbullying.
• Transgender teens were the most likely to be cyberbullied, at a significantly high rate of 35.4%.

State Laws 

The laws surrounding cyberbullying vary from state to state. However, all 50 states have developed and implemented specific policies or laws to protect children from being cyberbullied in and out of the classroom.

The laws were put into place so that students who are being cyberbullied at school can have access to support systems, and those who are being cyberbullied at home have a way to report the incidents.

Legal policies or programs developed to help stop cyberbullying include:

• Bullying prevention programs
• Cyberbullying education courses for teachers
• Procedures designed to investigate instances of cyberbullying
• Support systems for children who have been subject to cyberbullying 

Are There Federal Laws Against Cyberbullying?

There are no federal laws or policies that protect people from cyberbullying. However, federal involvement may occur if the bullying overlaps with harassment. Federal law will get involved if the bullying concerns a person’s race, ethnicity, national origin, sex, disability, or religion.

Examples of Cyberbullying 

There are several types of bullying that can occur online, and they all look different.

Harassment can include comments, text messages, or threatening emails designed to make the cyberbullied person feel scared, embarrassed, or ashamed of themselves.

Other forms of harassment include:

• Using group chats as a way to gang up on one person
• Making derogatory comments about a person based on their race, gender, sexual orientation, economic status, or other characteristics
• Posting mean or untrue things on social media sites, such as Twitter, Facebook, or Instagram, as a way to publicly hurt the person experiencing the cyberbullying  

Impersonation

A person may try to pretend to be the person they are cyberbullying to attempt to embarrass, shame, or hurt them publicly. Some examples of this include:

• Hacking into someone’s online profile and changing any part of it, whether it be a photo or their "About Me" portion, to something that is either harmful or inappropriate
• Catfishing, which is when a person creates a fake persona to trick someone into a relationship with them as a joke or for their own personal gain
• Making a fake profile using the screen name of their target to post inappropriate or rude remarks on other people’s pages

Other Examples

Not all forms of cyberbullying are the same, and cyberbullies use other tactics to ensure that their target feels as bad as possible. Some tactics include:

• Taking nude or otherwise degrading photos of a person without their consent
• Sharing or posting nude pictures with a wide audience to embarrass the person they are cyberbullying
• Sharing personal information about a person on a public website that could cause them to feel unsafe
• Physically bullying someone in school and getting someone else to record it so that it can be watched and passed around later
• Circulating rumors about a person

How to Know When a Joke Turns Into Cyberbullying

People may often try to downplay cyberbullying by saying it was just a joke. However, any incident that continues to make a person feel shame, hurt, or blatantly disrespected is not a joke and should be addressed. People who engage in cyberbullying tactics know that they’ve crossed these boundaries, from being playful to being harmful.

Effects and Consequences of Cyberbullying 

Research shows many negative effects of cyberbullying, some of which can lead to severe mental health issues. Cyberbullied people are twice as likely to experience suicidal thoughts, actions, or behaviors and engage in self-harm as those who are not.

Other negative health consequences of cyberbullying are:

• Stomach pain and digestive issues
• Sleep disturbances
• Difficulties with academics
• Violent behaviors
• High levels of stress
• Inability to feel safe
• Feelings of loneliness and isolation
• Feelings of powerlessness and hopelessness

If You’ve Been Cyberbullied 

Being on the receiving end of cyberbullying is hard to cope with. It can feel like you have nowhere to turn and no escape. However, some things can be done to help overcome cyberbullying experiences.

Advice for Preteens and Teenagers

The best thing you can do if you’re being cyberbullied is tell an adult you trust. It may be challenging to start the conversation because you may feel ashamed or embarrassed. However, if it is not addressed, it can get worse.

Other ways you can cope with cyberbullying include:

• Walk away : Walking away online involves ignoring the bullies, stepping back from your computer or phone, and finding something you enjoy doing to distract yourself from the bullying.
• Don’t retaliate : You may want to defend yourself at the time. But engaging with the bullies can make matters worse.
• Keep evidence : Save all copies of the cyberbullying, whether it be posts, texts, or emails, and keep them if the bullying escalates and you need to report them.
• Report : Social media sites take harassment seriously, and reporting them to site administrators may block the bully from using the site.
• Block : You can block your bully from contacting you on social media platforms and through text messages.

In some cases, therapy may be a good option to help cope with the aftermath of cyberbullying.

Advice for Parents

As a parent, watching your child experience cyberbullying can be difficult. To help in the right ways, you can:

• Offer support and comfort : Listening to your child explain what's happening can be helpful. If you've experienced bullying as a child, sharing that experience may provide some perspective on how it can be overcome and that the feelings don't last forever.
• Make sure they know they are not at fault : Whatever the bully uses to target your child can make them feel like something is wrong with them. Offer praise to your child for speaking up and reassure them that it's not their fault.
• Contact the school : Schools have policies to protect children from bullying, but to help, you have to inform school officials.
• Keep records : Ask your child for all the records of the bullying and keep a copy for yourself. This evidence will be helpful to have if the bullying escalates and further action needs to be taken.
• Try to get them help : In many cases, cyberbullying can lead to mental stress and sometimes mental health disorders. Getting your child a therapist gives them a safe place to work through their experience.

In the Workplace 

Although cyberbullying more often affects children and adolescents, it can also happen to adults in the workplace. If you are dealing with cyberbullying at your workplace, you can:

• Let your bully know how what they said affected you and that you expect it to stop.
• Keep copies of any harassment that goes on in the workplace.
• Report your cyberbully to your human resources (HR) department.
• Report your cyberbully to law enforcement if you are being threatened.
• Close off all personal communication pathways with your cyberbully.
• Maintain a professional attitude at work regardless of what is being said or done.
• Seek out support through friends, family, or professional help.

Effective Action Against Cyberbullying

If cyberbullying continues, actions will have to be taken to get it to stop, such as:

• Talking to a school official : Talking to someone at school may be difficult, but once you do, you may be grateful that you have some support. Schools have policies to address cyberbullying.
• Confide in parents or trusted friends : Discuss your experience with your parents or others you trust. Having support on your side will make you feel less alone.
• Report it on social media : Social media sites have strict rules on the types of interactions and content sharing allowed. Report your aggressor to the site to get them banned and eliminate their ability to contact you.
• Block the bully : Phones, computers, and social media platforms contain options to block correspondence from others. Use these blocking tools to help free yourself from cyberbullying.

Help Is Available

If you or someone you know are having suicidal thoughts, dial  988  to contact the  988 Suicide & Crisis Lifeline  and connect with a trained counselor. To find mental health resources in your area, contact the  Substance Abuse and Mental Health Services Administration (SAMHSA) National Helpline  at  800-662-4357  for information.

Cyberbullying occurs over electronic communication methods like cell phones, computers, social media, and other online platforms. While anyone can be subject to cyberbullying, it is most likely to occur between the ages of 12 and 18.

Cyberbullying can be severe and lead to serious health issues, such as new or worsened mental health disorders, sleep issues, or thoughts of suicide or self-harm. There are laws to prevent cyberbullying, so it's essential to report it when it happens. Coping strategies include stepping away from electronics, blocking bullies, and getting.

Alhajji M, Bass S, Dai T. Cyberbullying, mental health, and violence in adolescents and associations with sex and race: data from the 2015 youth risk behavior survey . Glob Pediatr Health. 2019;6:2333794X19868887. doi:10.1177/2333794X19868887

Cyberbullying Research Center. Cyberbullying in 2021 by age, gender, sexual orientation, and race .

U.S. Department of Health and Human Services: StopBullying.gov. Facts about bullying .

John A, Glendenning AC, Marchant A, et al. Self-harm, suicidal behaviours, and cyberbullying in children and young people: systematic review .  J Med Internet Res . 2018;20(4):e129. doi:10.2196/jmir.9044

Cyberbullying Research Center. Bullying, cyberbullying, and LGBTQ students .

U.S. Department of Health and Human Services: StopBullying.gov. Laws, policies, and regulations .

Wolke D, Lee K, Guy A. Cyberbullying: a storm in a teacup? . Eur Child Adolesc Psychiatry. 2017;26(8):899-908. doi:10.1007/s00787-017-0954-6

U.S. Department of Health and Human Services: StopBullying.gov. Cyberbullying tactics .

Garett R, Lord LR, Young SD. Associations between social media and cyberbullying: a review of the literature . mHealth . 2016;2:46-46. doi:10.21037/mhealth.2016.12.01

Nemours Teens Health. Cyberbullying .

Nixon CL. Current perspectives: the impact of cyberbullying on adolescent health . Adolesc Health Med Ther. 2014;5:143-58. doi:10.2147/AHMT.S36456

Nemours Kids Health. Cyberbullying (for parents) .

By Angelica Bottaro Angelica Bottaro is a professional freelance writer with over 5 years of experience. She has been educated in both psychology and journalism, and her dual education has given her the research and writing skills needed to deliver sound and engaging content in the health space.

Cyber Bullying Essay for Students and Children

500+ words essay on cyber bullying.

Cyber Bullying Essay: In today’s world which has been made smaller by technology, new age problems have been born. No doubt technology has a lot of benefits; however, it also comes with a negative side. It has given birth to cyberbullying. To put it simply, cyberbullying refers to the misuse of information technology with the intention to harass others.

Subsequently, cyberbullying comes in various forms. It doesn’t necessarily mean hacking someone’s profiles or posing to be someone else. It also includes posting negative comments about somebody or spreading rumors to defame someone. As everyone is caught up on the social network, it makes it very easy for anyone to misuse this access.

In other words, cyberbullying has become very common nowadays. It includes actions to manipulate, harass and defame any person. These hostile actions are seriously damaging and can affect anyone easily and gravely. They take place on social media, public forums, and other online information websites. A cyberbully is not necessarily a stranger; it may also be someone you know.

Cyber Bullying is Dangerous

Cyberbullying is a multi-faced issue. However, the intention of this activity is one and the same. To hurt people and bring them harm. Cyberbullying is not a light matter. It needs to be taken seriously as it does have a lot of dangerous effects on the victim.

Moreover, it disturbs the peace of mind of a person. Many people are known to experience depression after they are cyberbullied. In addition, they indulge in self-harm. All the derogatory comments made about them makes them feel inferior.

It also results in a lot of insecurities and complexes. The victim which suffers cyberbullying in the form of harassing starts having self-doubt. When someone points at your insecurities, they only tend to enhance. Similarly, the victims worry and lose their inner peace.

Other than that, cyberbullying also tarnishes the image of a person. It hampers their reputation with the false rumors spread about them. Everything on social media spreads like wildfire. Moreover, people often question the credibility. Thus,  one false rumor destroys people’s lives.

Get the huge list of more than 500 Essay Topics and Ideas

How to Prevent Cyber Bullying?

Cyberbullying prevention is the need of the hour. It needs to be monitored and put an end to. There are various ways to tackle cyberbullying. We can implement them at individual levels as well as authoritative levels.

Firstly, always teach your children to never share personal information online. For instance, if you list your home address or phone number there, it will make you a potential target of cyberbullying easily.

Secondly, avoid posting explicit photos of yourself online. Also, never discuss personal matters on social media. In other words, keep the information limited within your group of friends and family. Most importantly, never ever share your internet password and account details with anyone. Keep all this information to yourself alone. Be alert and do not click on mysterious links, they may be scams. In addition, teach your kids about cyberbullying and make them aware of what’s wrong and right.

In conclusion, awareness is the key to prevent online harassment. We should make the children aware from an early age so they are always cautious. Moreover, parents must monitor their children’s online activities and limit their usage. Most importantly, cyberbullying must be reported instantly without delay. This can prevent further incidents from taking place.

FAQs on Cyber Bullying

Q.1 Why is Cyberbullying dangerous?

A.1 Cyberbullying affects the mental peace of a person. It takes a toll on their mental health. Moreover, it tarnishes the reputation of an individual.

Q.2 How to prevent cyberbullying?

A.2 We may prevent cyberbullying by limiting the information we share online. In addition, we must make children aware of the forms of cyberbullying and its consequences.

Customize your course in 30 seconds

Which class are you in.

• Travelling Essay
• Picnic Essay
• Our Country Essay
• My Parents Essay
• Essay on Favourite Personality
• Essay on Memorable Day of My Life
• Essay on Knowledge is Power
• Essay on Gurpurab
• Essay on My Favourite Season
• Essay on Types of Sports

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Download the App

How To Write An Impactful Speech On Bullying (Sample Speech Included)

Hrideep barot.

• Speech Writing

If you attended an educational institution, chances are that you are familiar with the word ‘bullying’. Even if you were not the one bullied, maybe you witnessed someone else being bullied. Or maybe you’ve simply heard the term mentioned by your teachers or other people in authority during an anti-bullying campaign or a speech of some sort.

Whatever the context, most people are familiar with the term bullying and what it entails. And yet, statistics are proof that simply possessing the knowledge that bullying is real does not necessarily mean that people will–or are–doing anything about it.

One out of five students has reported being bullied. 70% of school staff have seen bullying. The number of anti-bullying campaigns might be on the rise, yes, but as you can see, the number of students being bullied remains just as abhorrently high.

If you’re going to be delivering a speech against bullying, then it’s important for you to know these statistics. It’s only when you realize this that you will understand that simply giving a speech against bullying is not enough.

Instead, you must strive to deliver your speech in such a manner that it actually impacts other people & results in tangible changes.

Sounds tough, I know. But it’s not as difficult as it sounds.

By keeping in mind a few things like keeping your audience & occasion in mind, incorporating stories & videos, varying your speech pattern, and having a powerful opening as well as closing, you can deliver an impactful speech on bullying.

Tips For Delivering A Speech On Bullying

1. Keep The Occasion & Audience In Mind

What is the occasion? Are you delivering your speech for a school assembly, or is it for a professional campaign? Is your audience going to be comprised of bullies/bullying victims, or are they simply ordinary individuals wanting to know more about bullying?

The answer to these questions is going to alter how you should go about structuring your speech. For example, if you’re delivering your speech to school-going children, then you’re going to have to alter your speech to fit their understanding level.

2. Make It A Perfomance, Not Just A Speech

A speech connotates something that revolves around the words and the act of speaking. However, a performance is so much more than a speech: think of it as speech leveled up by multiple levels.

A performance includes speech, yes, but it also includes other important things like your voice modulation, expressions, gestures, body language, emotions, and storytelling, to name a new. A performance is a wholesome experience.

By providing your audience a wholesome experience instead of simply delivering a speech–something that they’ve probably heard multiple times before–you increase the chances that they will actually take an action to do something about it.

Our article, A Guide To Making Your Speech Interesting , has more tips on how to make your speech intriguing to the audience.

3. Tell Stories

Storytelling is an absolute must for any speech. It becomes even more important to include stories when you’re talking about something as sensitive as bullying. By telling stories, you make your speech–and the experience of bullying–more real to your audience.

You make your audience empathize with you as well as your topic. You make them realize that the victims and survivors of bullying are not some nameless humans that the audience doesn’t care about. You make the bullying survivors–and the bullies themselves– real .

You make them relate an abstract concept to real life, and to see things that are probably happening around them, but they’d never seen before.

4. Use Props

Props are another element that you must definitely incorporate in any speech or presentation. Props, like stories, can make your topic more tangible and easy to understand for the audience. They can also add a touch of uniqueness to your speech, and make it more memorable for the people attending.

However, before choosing your prop, you must ensure that it is relevant to the topic. Don’t just add a prop to your speech for the sake of adding it.

5. Change Your Speech Pattern

It’s not just the content of your speech that matters. The way you deliver your speech plays just as internal of a role in the impact you’ll make on your audience as the actual speech itself. Speech pattern is key to making an emotional impact on your audience’s mind.

You don’t want to sound like a robot while delivering your speech. Instead, mix up your speech pattern. If you’re going to be delivering an impactful quote, pause for a moment. If you’re reaching a serious point in your story, slow down your cadence. Vary your speech pattern.

6. Show Videos

Videos are an excellent way to make a connection with the audience. Videos will allow you to tell your story without resorting to just words. Videos can capture your audience’s attention & enhances your narrative to another level.

You can include short videos that you can easily find online. Alternatively, if you want to take up the creativity another notch, you can customize a video on your own & include it in your speech.

7. Have A Dynamic Opening & End

The way you open your speech–and how you close it–play a key role in determining the kind of impact you will make on your audience’s mind.

If your opening isn’t interesting enough, then you’ll end up losing your audience’s attention even before you have it. Alternatively, if your speech ending isn’t impactful enough, then your audience will probably forget about it the moment they leave–which is definitely something that no speaker wants.

For some inspiration on how to close your speech, check out our article on 10 Of The Best Things To Say In Closing Remarks.

5 Ways To Open Your Speech on Bullying

1. Make Them Imagine

Imagination is one of the strongest tools in your arsenal as a public speaker. By channeling the power of imagination right in the beginning of your speech, you can make your audience form a personal connection with the topic right off the bat.

By making your audience imagine being in a scenario related to bullying, you can make them empathize with your topic better. This is key if you wish for them to take actual steps to stop bullying.

For example: Imagine if we lived in a world that was actually free…

2. Ask Them A Rhethorical Question

Questions are an excellent way to get your audience thinking. Questions can act as a cognitive ‘wake-up’ for your audience & get their thoughts flowing. By asking your audience a question right in the beginning, you prime them for the rest of your speech.

So, pose a question to your audience at the beginning of your speech. Rhetorical questions are great speech openers. Because, unlike a regular question that most likely has a straightforward answer, rhetorical questions make your audience think more deeply.

For example: If you met someone who’d bullied you 15 years ago in high-school, what would you do?

3. Tell A Personal Story

Another great way to begin your speech is by telling them a personal story. Stories–especially if they’re personal–can make the audience form an instant connection with the speaker and the topic.

Have you been bullied in the past? Or did you witness someone get bullied–or stand up for themself in the most awesome way imaginable?

Now would be the time to include them.

For example: I was bullied for over three years during my…

4. Make A Bold Statement

Surprising your audience is a great way to begin your speech. By making a bold statement, you not only achieve this, but you also make your audience see you as a more confident & respectable figure. This increases the chances that they will perceive your speech in a positive light.

So, start off your speech with a bold statement.

For example : I wish bullies were treated the same as murderers.

5. Use Facts & Statistics

Statistics and facts are an age-old way to have a foolproof beginning. Statistics and facts can add shock value to your speech opening, and awaken your audience. They might also cause the audience to see your speech in a different light.

However, one thing to keep in mind while incorporating facts or statistics is to ensure that they’re not too complicated or include a lot of numbers. You want to keep your facts simple, and relevant to the topic at hand.

For example: 1 in 5 children reports being bullied during their high school…

For more ideas on how to open your speech, check out our article on 10 Of The Best Things To Say In Opening Remarks.

Sample Speech On Bullying

Bullying: It’s More Than Getting Punched

“Why don’t you just kill yourself?” This is the gift that arrived in my inbox on the morning of my fourteenth birthday. A fourteen year old girl–statements like these were a common part of my daily life. I’d listened to them every single day since I entered high-school. In fact, they were precisely the reason why I begged my parents to home school me in the first place. When I began my home-schooling journey, I did so with a lot of hope. Hope that I would finally be able to get away from the words that had been hurled at me every single day for the last two years. And yet, here we were. Not even a week had passed since I left the concrete halls of my high-school for the comfort and safety of my home, and yet as it turned out, home wasn’t safe either. Nothing was. Not in this new, techonology-driven world where people don’t need to be standing in front of you to communicate with you–or bully you. Or threaten your life. A few quick thrusts on the keypad, a couple of clicks, and it’s done. When people think of bullying, they often picture giant, violent figures towering over tiny, sobbing ones. Or hordes of people screaming insults at cowering figures in the hallway. Or pushing them against walls and banging their heads against toilet seats. While the incidents I’ve described still happen–and too often–bullying is so much more than that. Bullying, in the modern world, is like a hydra monster from the Greek Myths: it doesn’t have one face but ten, and every time you shack off one head, another one pops up in its place. We all know what to do if we’re bullied–or see someone else get bullied. We’ve heard it before, or maybe seen in the pamphlets on bulletin boards or in videos shown in classrooms. But before we take steps to stop bullying, we need to first learn how to identify it. Because unless and until we can recognize bullying when it happens to us–or to someone else–how will it matter whether we know the ways to stop it or not? Bullying can come in many forms. Bullying can be whispered insults when you think no one else is listening. Bullying can be deliberately pulling someone down on their happiest day. Bullying can be starting rumors about someone. Bullying can be tiny actions with no consequences–not for you, at least. It can be little jokes made by your ‘friends’–or little ‘bits of advice to lose weight or gain weight.’ Bullying can happen on the internet, through a string of messages that you hurl behind the mask of anonimity. Bullying can happen in the workplace, or in your college. Bullying can take the shape of prejudice, in the form of stealing opportunties from someone. Bullying can even happen in your own houseold, in your own relationship–and not just romantic ones. Bullying is not just physical. It has more than one dimension. Bullying can be emotional, social, spiritual…and many more things. And yet it is only one aspect of bullying that we tackle, the only one that gets talked about. It is a common misconception. If you hold it, I don’t blame you. After all, even I–a victim myself–held for a long, long time. In fact, in the beginning I didn’t even realize that I was getting bullied at all. After all, nobody ever physically punched me. I was never shoved against the lockers or punched in the face. By conventional definitions of bullying, I was never bullied. And I’m not the only one–a study showed that 64 % of bullying victoms never speak up about their bullying. It was only the day that the message arrived in my inbox that I realized that bullying can come in more than one shape or form. And most of them are forms that we’re not familiar with–at least, not yet. But we need to be. We need to recognize bullying–and we need to get better at doing it. Look at the people around you. You might not know it–they might not know it yet–but they may be getting bullied. And if you want to stop it, you must learn to see it first. They asked me why couldn’t I kill myself. I ask you: do you have the ability to recognize who they are?

To sum up, writing a speech on bullying is simple, and no different than any other speech. Keep in mind a few things like keeping your audience & occasion in mind, incorporating stories & videos, varying your speech pattern, and having a powerful opening as well as closing, and you can deliver an impactful speech on bullying.

Enroll in our transformative 1:1 Coaching Program

Schedule a call with our expert communication coach to know if this program would be the right fit for you

How to Negotiate: The Art of Getting What You Want

10 Hand Gestures That Will Make You More Confident and Efficient

Interrupted while Speaking: 8 Ways to Prevent and Manage Interruptions

• [email protected]
• +91 98203 57888

Get our latest tips and tricks in your inbox always

Copyright © 2023 Frantically Speaking All rights reserved

Kindly drop your contact details so that we can arrange call back

Select Country Afghanistan Albania Algeria AmericanSamoa Andorra Angola Anguilla Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bosnia and Herzegovina Botswana Brazil British Indian Ocean Territory Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Colombia Comoros Congo Cook Islands Costa Rica Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Faroe Islands Fiji Finland France French Guiana French Polynesia Gabon Gambia Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Latvia Lebanon Lesotho Liberia Liechtenstein Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Monaco Mongolia Montenegro Montserrat Morocco Myanmar Namibia Nauru Nepal Netherlands Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island Northern Mariana Islands Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Romania Rwanda Samoa San Marino Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands South Africa South Georgia and the South Sandwich Islands Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Tajikistan Thailand Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Wallis and Futuna Yemen Zambia Zimbabwe land Islands Antarctica Bolivia, Plurinational State of Brunei Darussalam Cocos (Keeling) Islands Congo, The Democratic Republic of the Cote d'Ivoire Falkland Islands (Malvinas) Guernsey Holy See (Vatican City State) Hong Kong Iran, Islamic Republic of Isle of Man Jersey Korea, Democratic People's Republic of Korea, Republic of Lao People's Democratic Republic Libyan Arab Jamahiriya Macao Macedonia, The Former Yugoslav Republic of Micronesia, Federated States of Moldova, Republic of Mozambique Palestinian Territory, Occupied Pitcairn Réunion Russia Saint Barthélemy Saint Helena, Ascension and Tristan Da Cunha Saint Kitts and Nevis Saint Lucia Saint Martin Saint Pierre and Miquelon Saint Vincent and the Grenadines Sao Tome and Principe Somalia Svalbard and Jan Mayen Syrian Arab Republic Taiwan, Province of China Tanzania, United Republic of Timor-Leste Venezuela, Bolivarian Republic of Viet Nam Virgin Islands, British Virgin Islands, U.S.

Search form

• Study break

Cyberbullying – let's fight it together

Cyberbullying is a problem that everyone who uses the internet needs to be aware of. This video was created for schoolchildren in the UK to watch to warn them about the dangers of cyberbullying. 

Instructions

Do the preparation task first. Then watch the video and do the exercise. Remember you can read the transcript at any time.

Preparation

The music throughout the film is a song by Ben Folds called Still Fighting It. You can listen to it here: http://youtu.be/kqPwR39VMh0

JOE:  Um … Hi. My name’s Joe. I don’t really have anyone to talk to, so … I thought I’d tell my story like this. When it all started, I just tried to laugh it off. But it just went on and on.

Text on mobile phone screen: No Number YOU LITTLE KISSASS! Message deleted

Text on computer screen: Anon5446: HEY FREAK joebpruett: hu’s dat Anon5446: YOUR WORST NIGHTMARE Anon5446: LOSER! joebpruett: is dat u Kim? Anon5446: TEACHERS PET. Anon5446: TOMORROW U BETTER WATCH OUT Anon5446: GONNA GET KILLED. Block

Email on screen: Date: Tue, 10 Jul 2007 16:36:31 + 0100 (BST) From: [email protected];  Subject: To: [email protected] http://www.joeisaloser.co.uk

Text on screen: YOU LITTLE KISSASS! LOSER! GET KILLED.

JOE:  Well, that’s it. I just can’t take it anymore. JOE: ( on video camera ) I thought they were supposed to be my friends, but they’re all laughing at me. I’ve got to get them to take notice. ( Joe’s mother looks up and walks off .) Text on screen:  Cyberbullying is bullying It ruins lives Cyberbullying Let’s fight it together

Ben Folds - Still Fighting It - Lyrics

Good morning, son, I am a bird wearing a brown polyester shirt You want a coke? Maybe some fries? The roast beef combo's only dollar 9.95 It's okay, you don't have to pay, I've got all the change

Everybody knows it hurts to grow up and everybody does It's so weird to be back here let me tell you what The years go on and we're still fighting it, we're still fighting it And you're so much like me, I'm sorry

Good morning, son in twenty years from now Maybe we'll both sit down and have a few beers And I can tell you 'bout today and how I picked you up And everything changed it was pain, sunny days And rain I knew you'd feel the same things

Everybody knows it sucks to grow up and everybody does It's so weird to be back here let me tell you what The years go on and we're still fighting it, we're still fighting it You'll try and try and one day you'll fly away from me

Good morning, son, I am a bird It was pain, sunny days and rain I knew you'd feel the same things

Everybody knows it hurts to grow up and everybody does It's so weird to be back here, let me tell you what The years go on and we're still fighting it, we're still fighting it Oh, we're still fighting it, we're still fighting it And you're so much like me, I'm sorry

© Childnet International

Check your understanding: reordering

Worksheets and downloads.

How do you stay safe online? What would you do if you found yourself in a similar situation to Joe? 

Sign up to our newsletter for LearnEnglish Teens

We will process your data to send you our newsletter and updates based on your consent. You can unsubscribe at any time by clicking the "unsubscribe" link at the bottom of every email. Read our privacy policy for more information.

• Expert Advisory Panel
• Our partners
• Become a partner
• Advice for parents and carers
• Advice for professionals
• Connecting Safely Online
• Fostering Digital Skills
• UKCIS Vulnerable Users Working Group
• Online hate
• Online grooming
• Fake news and misinformation
• Screen time
• Inappropriate content
• Cyberbullying
• Online reputation
• Online Pornography
• Radicalisation
• Privacy and identity theft
• Report issue
• Pre-school (0-5)
• Young Children (6-10)
• Pre-teen (11-13)
• Teens ( 14+)
• Social media privacy guides
• Gaming platforms and devices
• Smartphones and other devices
• Broadband & mobile networks
• Entertainment & search engines
• Get smart about smartphones
• My Family’s Digital Toolkit
• Navigating teens’ online relationships
• Online gaming advice hub
• Social media advice hub
• Press Start for PlayStation Safety
• Guide to apps
• Digital resilience toolkit
• Online money management guide
• The dangers of digital piracy
• Guide to buying tech
• UKCIS Digital Passport
• Online safety leaflets & resources
• Digital wellbeing research programme
• Parent Stories
• Expert opinion
• Press releases
• Our expert panel
• Free digital stories and lessons
• Early years
• Primary school
• Secondary school
• Connect school to home
• Professional guidance
• Online safety issues
• Cyberbullying advice hub
• Cyberbullying conversation starter guide

Expert tips to help you talk about cyberbullying with your child

Download Workbook

• To receive personalised online safety guidance in the future, we’d like to ask for your name and email. Simply fill your details below. You can choose to skip, if you prefer.
• First name *
• Last name *
• Email Address *
• I am a * Parent/Carer Teacher Professional
• Organisation name
• Skip and download
• Phone This field is for validation purposes and should be left unchanged.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Psychol

Cyberbullying and Cyberhate as Two Interlinked Instances of Cyber-Aggression in Adolescence: A Systematic Review

Giovanni fulantelli.

1 Institute for Educational Technology, National Research Council of Italy, Palermo, Italy

Davide Taibi

Lidia scifo, veronica schwarze.

2 Institute of Computer Science, Institute of Positive Computing, University of Applied Sciences Ruhr West, Bottrop, Germany

Sabrina C. Eimler

Associated data.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

In this paper we present the results of a systematic review aimed at investigating what the literature reports on cyberbullying and cyberhate, whether and to what extent the connection between the two phenomena is made explicit, and whether it is possible to identify overlapping factors in the description of the phenomena. Specifically, for each of the 24 selected papers, we have identified the predictors of cyberbullying behaviors and the consequences of cyberbullying acts on the victims; the same analysis has been carried out with reference to cyberhate. Then, by comparing what emerged from the literature on cyberbullying with what emerged from the literature on cyberhate, we verify to what extent the two phenomena overlap in terms of predictors and consequences. Results show that the cyberhate issue related to adolescents is less investigated than cyberbullying, and most of the papers focusing on one of them do not refer to the other. Nevertheless, by comparing the predictors and outcomes of cyberbullying and cyberhate as reported in the literature, an overlap between the two concepts emerges, with reference to: the parent-child relationship to reduce the risk of cyber-aggression; the link between sexuality and cyber-attacks; the protective role of the families and of good quality friendship relationships; the impact of cyberbullying and cyberhate on adolescents' individuals' well-being and emotions; meaningful analogies between the coping strategies put in practice by victims of cyberbullying and cyberhate. We argue that the results of this review can stimulate a holistic approach for future studies on cyberbullying and cyberhate where the two phenomena are analyzed as two interlinked instances of cyber-aggression. Similarly, prevention and intervention programs on a responsible and safe use of social media should refer to both cyberbullying and cyberhate issues, as they share many predictors as well as consequences on adolescents' wellbeing, thus making it diminishing to afford them separately.

Systematic Review Registration

http://www.crd.york.ac.uk/PROSPERO , identifier: CRD42021239461.

Introduction

Social media have become online environments in which face-to-face activities of everyday life are transferred in the network mediated world with a wider audience (potentially millions of users) and no time constraints (they are open 24 h a day). According to the Digital 2020 July Global Statshot report 1 , July 2020 can be considered a milestone in the history of the internet, since for the first time more than half of the world's total population was using social media, with a total number of 3.96 billion active social media users (Kemp, 2020 ). Due to the coronavirus pandemic lockdowns, this number increased up to 4.33 billion active social media users in April 2021 (55.1% of world population, with an annual increase close to 14%) (Kemp, 2021a ) 2 .

The share of adolescents contributing to these numbers is impressive: 90% of US teens aged 13-17 years use social media (AACAP, 2018 ) 3 ; a similar percentage concerns Europe, where 87% of people aged 16 to 24 years use social networks, ranging from 79% in Italy up to 97% in Denmark (Eurostat, 2020 4 ; research from GWI shows that 99.6% of South-East Asian internet users aged 16–24 years use social media (Kemp, 2021b ).

According to these statistics, adolescents are among the most frequent users of social media. Following Shapiro and Margolin ( 2014 ), the main motivations for adolescents using social media are “to stay in touch with friends, make plans, get to know people better, and present oneself to others”.

Despite these perceived or real benefits, social media can become a place in which antisocial behaviors such as bullying, harassment and hate speech, proliferate and evolve leveraging the peculiarity of the online world (ElSherief et al., 2018 ). As a consequence, participation in social media exposes adolescents more to the risks associated with their use. In particular, several studies have pointed out the relationship between social media use and cyber-violence, broadly defined as violent acts perpetrated through the social media (Peterson and Densley, 2017 ; Backe et al., 2018 ; Nagle, 2018 ), and how adolescents can become victims or perpetrators of aggressive behaviors (Chisholm, 2006 ; O'Keeffe et al., 2011 ; Peterson and Densley, 2017 ). Furthermore, some authors have highlighted the difficulties in classifying and defining the spectrum and diversity of online violent behaviors, their specificity compared to similar offline behaviors, and the limits that result from a lack of clear definitions of online violent behaviors (Grigg, 2010 ; Pyzalski, 2012 ; Peterson and Densley, 2017 ).

It is precisely from the difficulties in defining the concept of cyberbullying that Grigg ( 2010 ) comes to the conclusion that it is necessary to move to a concept at a higher level of abstraction that includes all the online behaviors characterized by a high level of aggression, thus introducing the concept of cyber-aggression: “The study examined current definitions and concepts of cyberbullying and how these differ in its findings; and considered different ways to foster positive online behavior for the context of practitioners. The concept of cyber-aggression is used to describe a wide range of behaviors other than cyberbullying. The findings indicate that there is a need to include a broader definition in line with the current trend of a range of behaviors that are common with internet and mobile phone usage” (p. 143). Following Grigg's idea of cyber-aggression, Corcoran et al. ( 2015 ) argue that, in order to overcome the problems related to the variations across definitions of cyberbullying, it is necessary to consider the broader issue of cyber-aggression.

In addition to cyberbullying, cyberhate is another important example of online aggressive behavior which is more and more involving young victims and perpetrators. This is related to two main factors: firstly, the amount of online hate material (such as hateful messages and, more in general, content that harm the reputation of or instigate violence against groups or individual as member of groups) is rapidly increasing, and the risk for adolescents to be exposed to hateful online material is increasing accordingly (Hawdon et al., 2019 ; Harriman et al., 2020 ); secondly, adolescents are becoming one of the preferred target for online recruitment by organized hate groups and individuals (Smith, 2009 ; Costello et al., 2020 ). Similar to cyberbullying, several authors consider cyberhate as a subset of cyber-aggression (Mardianto et al., 2019 ; Tennakoon, 2021 ; Bedrosova et al., 2022 ).

As mentioned before, adolescents are particularly vulnerable to cyber-aggression, not only because of the time spent online: US statistics in 2018 showed that nearly all teens aged 13–17 years (95%) have access to a smartphone and 45% of them reported that they were online “almost constantly” (Anderson and Jiang, 2018 ); adolescents are at high risk also because most of them have fewer psychological tools than the majority of adults to defend themselves against cyber-aggression, such as resilience, competence, literacy, critical thinking, experiences.

Cyberbullying and cyberhate play a dramatic role in the relationships between adolescents' well-being and use of social media. In fact, cyberbullying is the most frequent form of cyber-aggression involving adolescents, while cyberhate is the form of cyber-aggression that is spreading most rapidly among young people. Furthermore, the two phenomena are not totally distinct, rather there are some overlaps between them (Goerzig et al., 2019 ; Bedrosova et al., 2022 ). However, to the best of our knowledge, there is no approach addressing cyberbullying and cyberhate as two distinct but interlinked phenomena which makes it hard to evaluate them in empirical research settings. As a consequence, analyzing the two phenomena not in a separate manner, but on the contrary assuming that there may be important links between them, can help to develop models for the identification of predictor variables, to broaden the assessments of the possible impacts they have on the lives of adolescents, and to put in practice more effective and efficient prevention strategies. This approach can offer a concrete model which would be of interest to academia to explain how theoretical models can help to derive practical interventions to limit the spread of these phenomena. In the long term, the review can support practitioners in the school contexts in developing intervention measures aimed at avoiding toxic dynamics on the internet.

Accordingly, in this paper we present the results of a systematic review aimed at investigating what the literature reports on cyberbullying and cyberhate, whether and to what extent the connection between the two phenomena is made explicit, and whether it is possible to identify what exactly the overlapping factors are. Specifically, for each of the analyzed papers, we have identified the predictors of cyberbullying behaviors and the consequences of cyberbullying acts on the victims; the same analysis has been carried out with reference to cyberhate. Then, by comparing what emerged from the literature on cyberbullying with what emerged from the literature on cyberhate, we verify to what extent the two phenomena—as reported in the literature—overlap in terms of predictors and consequences.

Cyberbullying and Cyberhate as Two Interconnected Phenomena

In order to reflect on the relationship between cyberbullying and cyberhate in terms of differences and similarities, we focus on the distinguishing characteristics of each of them, being aware that the definition of the two concepts is not an objective of this review. In both cases, we focus on the comparisons between these two forms of cyber-aggression and their equivalent forms in face-to-face contexts: bullying and hate speech.

Cyberbullying does not simply refer to the transition from 'traditional' bullying in face-to-face contexts to bullying in online contexts, where, according to the Centers for Disease Control and Prevention, bullying is defined as “ any unwanted aggressive behavior(s) by another youth or group of youths who are not siblings or current dating partners that involves an observed or perceived power imbalance and is repeated multiple times or is highly likely to be repeated .” 5 In fact, the characteristics of social media lead to a re-interpretation of the concepts of aggression, repetition and the imbalance of power (Whittaker and Kowalski, 2015 ). Firstly, the characteristics of physical aggression related to vocal tone and facial expression assume a different value in the virtual world, in which other shapes of aggression come into the scene through hate speech or online harassment, humiliation or exclusion. Concerning repetition, in an online environment a harassing statement can be potentially viewed, “liked” and shared by other users multiple times, therefore the repetition of the act over time is not more crucial, only one shared content humiliating a victim could have a destructive effect on the victim's self-esteem. Finally, power imbalance is difficult to detect in a virtual environment in which power can be expressed in a multitude of ways. For example, users with a high level of digital knowledge can conduct cyberattacks by using sophisticated tools, so that power imbalance might also reflect differences in technological expertise (Whittaker and Kowalski, 2015 ).

Other characteristics of the virtual environment affect the proliferation of cyberbullying and differentiate cyberbullying from traditional bullying. In the virtual environment cyberbullies can use anonymous accounts to attack their victims. There is another relevant difference between cyberbullying and bullying as stated by Englander ( 2017 ) who state that cyberbullying is connected to the widespread use of digital devices, thus leading cyberbullying to happen mainly outside of school, whereas traditional bullying most often happens in school.

Moving to the cyberhate concept, there are not universally accepted definitions of hate speech and cyberhate (MacAvaney et al., 2019 ). Specifically, to hate speech, differences amongst the definitions concern several aspects. Firstly, the authors and the spreaders of the hate messages can be individuals, organized groups or a combination of individuals and organized groups (Blaya and Audrin, 2019 ). Then, the target or victim is one of the most variable concepts in the definitions of hate speech, and it strongly reflects the differences in the contexts of use and in the historical period of definition. Some of the definitions consider only specific groups, such as the one proposed by the Council of Europe in the Additional Protocol to the Convention on Cybercriminality (Council of Europe, 2003 ) that states that individuals or communities become target of attacks because of their “race, color, descent or national or ethnic origin, as well as religion if used as a pretext for any of these factors”; the constraints posed to the potential target groups of hate speech is due to the origin of this document, conceived as a normative instrument to contrast racism and xenophobia. On the other end of the spectrum of definitions, the Center for Equal Opportunities and Opposition to Racism in Brussels lists sex, sexual orientation or political or religious beliefs, in addition to skin color, supposed race, ethnic origin, as reasons to unleash the haters.

A further distinction concerns the purpose of haters. The Council of Europe states that hate speech aims at advocating, promoting or inciting hatred, discrimination or violence (Council of Europe, 2003 ). Blaya and Audrin ( 2019 ) clarify that the purpose of haters is to attract new members toward their ideals, thus building and strengthening group identity to counter and reject others' collective identity. The mechanisms of propaganda, insulting and discrimination are therefore central to the hate speech definitions (Council of Europe, 2003 ; Anti-Defamation League, 2010 ; Blaya and Audrin, 2019 ). One of the definition that better resumes and mediates the different perspectives to hate speech is the one proposed in 1997 by the Council of Europe, which refer to hate speech as “all forms of expression which spread, incite, promote or justify racial hatred, xenophobia, anti-Semitism or other forms of hatred based on intolerance, including intolerance expressed by aggressive nationalism and ethnocentrism, discrimination and hostility toward minorities, migrants and people of immigrant origin” (Council of Europe, 1997 ).

Moving to the differences between hate speech and online hate speech or cyberhate, the most evident difference is the media used for disseminating hate content and messages. The Council of Europe ( 2003 ) provides a comprehensive view of hate speech material, which includes “any written material, any image or any other representation of ideas or theories”. Drawing on this definition, Blaya and Audrin ( 2019 ) adopt the term “cyberhate” to refer to all hateful online forms of expression (text, images, videos, pictures, graphic representations) to generate hatred against people and communities. Cyberhate is based on the spreading of hateful material through electronic communication technologies (e.g., websites, social media, blogs, online games, instant messaging services, e-mail). As for cyberbullying, the use of Internet-based communication media amplifies the effect of cyberhate, and exacerbates the negative consequences of hate speech.

Differences between cyberbullying and cyberhate are a direct consequence of the definitions provided so far. Firstly, as already mentioned, the final purpose of cyberhate is to promote or incite hatred, discrimination or violence against a community or group in order to disaggregate social cohesion and mine democracy; instead, the final aim of a cyberbullying is to harm an individual.

Bullying is known with its repetitive act to the same individual, unlike hate speech which is more general and not necessarily intended to hurt a specific individual (Al-Hassan and Al-Dossari, 2019 ). As Chetty and Alathur ( 2018 ) claim, hate speech may harm the victims directly or indirectly. Direct hate speech is similar to bullying, since the victims are injured immediately by hate speech content. However, in an indirect hate speech, the harm perpetrated by the original hater is only a part of the final goal, since the hater incites other people or organized groups to attack the victims, and a delayed harm is perpetrated by the latters, not by an original actor. In a typical racist hate speech scenario, hateful content on racism in (real or online) public settings might motivate other people to initiate harassment, intimidation, violence against ethnical minorities (Seglow, 2016 ). This introduces a second difference: one of the aims of the cyberhaters is to involve as many people as possible as active agents in the attack; this is not a priority for the perpetrator of cyberbullying, who is normally an individual or a small group of peers. Consequently, the online services used by the two categories of haters are different, even if some overlaps exist.

Strictly related to the previous one, another difference is that cyberhate targets communities more than individuals, while cyberbullying victims are individuals, usually young individuals in the setting of a particular community, like a school (López and López, 2017 ). Following Blaya and Audrin ( 2019 ), cyberhate can also harm individuals and affect them emotionally, but the main negative consequences are on whole communities. Another difference is that the perpetrator of cyberbullying usually personally knows his/her victim, which is often unknown to most of the cyberhaters.

Finally, the idea of victimization changes accordingly: specifically to cyberhate, Machackova et al. ( 2020 ) distinguish between cyberhate exposure and cyberhate victimization, and they define the former as “the experience of encountering hateful content online but not necessarily feeling victimized by it”. This distinction is not necessary in cyberbullying, since it is always possible to identify a victim.

Despite the several differences between the concepts of cyberbullying and cyberhate, they share important characteristics that lead to a partial overlap between the two concepts and promote the study of similar solutions. As mentioned before, in both cases the use of Internet-based technologies amplifies the consequences of the attacks, both in terms of geographical space and persistence over time. Secondly, the lack of face-to-face contact between the perpetrator and the victim, both in cyberbullying and in cyberhate, makes online forms of expression (text, images, videos, pictures, graphic representations) the common language used by the perpetrators. Then, school-aged children, adolescents and young people are particularly exposed to cyberbullying and cyberhate, both as victims and perpetrators: Li et colleagues ( 2021 ) have illustrated that cyberbullying perpetration among school-aged children is a transnational phenomenon; likewise, organized hate groups specifically target adolescents as new recruits (Lee and Leets, 2002 ; Gerstenfeld et al., 2003 ), so that they are particularly vulnerable to online media activist groups due to their presence in social media, and the particular stage of their development who makes them sensitive to feelings of personal commitment, of social utility and of belonging they can provide (Atran and Ginges, 2015 ); finally, the concept of victimization is central to both, cyberbullying and cyberhate, in particular by taking into account that a large number of victims and perpetrators are adolescents.

In addition to these general factors that characterize the nature of the two phenomena, the aim of this review is, as anticipated in the introduction, to analyze in more detail the individual factors that—based on the results of the empirical studies presented in the selected papers—allow us to understand the level of overlap between cyberbullying and cyberhate. This will make it possible to better target interventions aimed at preventing the phenomena of cyber-aggression committed by or directed against adolescents.

Materials and Methods

The PRISMA method was followed for the review methodology and data extraction (Liberati et al., 2009 ; Moher et al., 2009 ; Page et al., 2021 ). A protocol for this review was registered on PROSPERO in April 2021 (PROSPERO registration number CRD42021239461. The registration number is available at http://www.crd.york.ac.uk/PROSPERO ).

Participants and Procedure

To identify the literature, the following databases were searched: PsycInfo, Scopus, PubMed, APA PsycArticles, EBSCO. The search for electronic literature databases was dated from January 2021 to February 2021. In each database, the following terms were searched: social media, adolescents, boys, girls, young adults, teenagers, cyberbullying, cybermobbing, online hate speech, cyberhate, cyber victimization. The search strategy was carried out combining these keywords with boolean operators as AND, OR, NOT. Of the selected empirical studies, the chronological age of research participants between 10 and 24 years were considered. Moreover, the broad age range of research participants was a forced choice because several studies included both adolescents and young adults. The selection of the studies was a process of evaluation of synonyms and related keywords, as the scientific literature concerning the topic of the review is vast and articulated.

In order to ensure that no relevant studies were missed, additional studies were identified by hand-searching the reference lists of reviews and research papers. Missing papers were requested from study authors by email.

The papers/records included in the review and analyzed are 24.

Inclusion and Exclusion Criteria

All the records were independently screened by four review authors to identify studies that potentially met the inclusion and exclusion criteria as outlined below.

The following inclusion criteria were adopted:

• studies described different types of cyberbullying and cyberhate in social media
• quantitative study and empirical papers published between 2000 and 2021
• studies were published in the English language
• empirical studies, experimental and quasi-experimental design
• peer-reviewed studies
• people aged between the ages of 10 and 24.

In the exclusion criteria, duplicates and irrelevant records have been eliminated.

In particular, the following exclusion criteria were adopted:

• qualitative studies
• studies were published before 2000
• studies were not published in the English language
• cross-sectional design, single case
• studies were not peer-reviewed
• gray literature, e.g., dissertations, conference abstracts, research reports, chapter(s) from a book, Ph.D. theses, reports on ID guidelines.

Data Analysis

The initial database search identified a total of 1296 records, after a careful selection, according to the PRISMA checklist and the inclusion and exclusion criteria, 24 papers were analyzed for the data extraction. The flow diagram following the models of Page et al. ( 2021 ) is included as Figure 1 .

PRISMA flow chart of the search and selection process.

Papers were firstly reviewed through the title and abstract to determine if they could be included or excluded. The final papers were organized for the data extraction according to the following variables: digital object ID, article title, abstract, journal title, journal year, authors, social media as the subject of investigation, which kind of cyberbullying/cyberhate, content of the studies, method and procedure participants, age participants, sex, stage of life, country, aim of the study, result.

The synthesis of the main variables considered essential to the topic of the systematic review process is reported in Table 1 .

Main characteristics of the papers included in the review.

To systematically report and compare the heterogeneous findings of the articles included, a coding scheme was created, with categories derived from the reported results. Since some papers contained a large number of different worth reporting findings, the results were not exclusively assigned to one category, but are presented in the context of different dimensions. Given that the focus of this review was on cyberbullying and cyberhate as two interlinked instances of cyber-aggression, both phenomena contributed to the main distinction of the results, with 19 papers referring to cyberbullying and 5 papers referring to cyberhate. Next, the results were categorized according to whether they related to the predictors or the impact of cyberbullying or cyberhate. Thus, a 2 × 2 matrix was created, which served as a grid for the analysis of the results. The grid was further divided into subcategories as follows: as predictors, we considered (1) socio-demographic variables (e.g., age/school grade level, gender, race/ethnic background), (2) individual and contextual factors (e.g., empathy, sexuality, appearance/overweight, school performance, relationships with friends and family) and (3) the overlap between traditional and cyberbullying as well as the overlap of cyberhate with other forms of aggressive behavior. Similarly, the effects of both phenomena were contrasted and considered categories such as effects on health and well-being (e.g., psychological distress, depressive symptoms, somatic symptoms, suicidal) and coping strategies.

Predictors of Cyberbullying

Socio-demographic variables.

Numerous studies investigated the potential impact of various socio-demographic variables on cyberbullying behaviors. Age seems to play a substantial role in the prevalence of these behaviors. For example, Ybarra et al. ( 2011 ) found evidence for increasing age being predictive for exposure to and experience of online harassment, surveying 10 to 15 year old over three years. In this sense, Ševčíková and Šmahel ( 2009 ) found that of the older adolescents (16–19 years) 14.1% were in the role of the target, while of the younger adolescents (12–15 years) 7.6% experienced the target perspective. However, the situation was reversed for aggressors, as the higher proportion of aggressors was among 12–15 year old's (1.8%) rather than 16-to-19 years old's (1.2%). Mishna et al. ( 2010 ) reported an increased likelihood for older girls (grades 10 and 11) being exposed to cyberbullying compared to older boys, although this difference was not seen between girls and boys in lower grades (grades 6 and 7). Contrarily, while Ortega-Ruiz et al. ( 2009 ) indicate a significant peak in victimization at the age of 14, they also indicate that victimization decreased significantly between the ages of 12 and 17. Regarding cyberbullying, Schneider et al. ( 2012 ) report similar findings, with this phenomenon decreasing slightly from 9th grade to 12th grade (from 17.2 to 13.4%). Likewise, Waasdorp et al. ( 2018 ) observed that compared to middle school students, high school students were less likely to be affected by the various forms of victimization with the exception of cyberbullying.

Age (school grade level) also seemed to moderate the association between cyberbullying victimization and students' engagement. While a positive association between cyberbullying victimization and emotional engagement was stronger for high school students than for middle school students, a negative association between cyberbullying victimization and cognitive-behavioral engagement was stronger for middle school students than for high school students (Yang et al., 2020 ).

Gender was also hypothesized to play a role in frequency and type of cyberbullying behaviors students encountered. Gradinger et al. ( 2009 ) observed that 8% of male students reported having sent mean text messages, e-mails, videos or photographs, while only 3% of female students reported doing so. Similarly, Baldry et al. ( 2019 ) observed that boys tended to be only bullies and bullies/victims, while girls tended to be uninvolved and only victims. These results are in line with those found by Ortega-Ruiz et al. ( 2009 ) who found that more females reported being victims of cyberbullying, both via mobile phone (6.3% females vs. 2.4% males) and via the Internet (9.1% females vs. 6% males) and Schneider et al. ( 2012 ) who found that the victims of cyberbullying are more often girls than boys (11.1% vs. 7.6%). Contrarily, Low and Espelage ( 2013 ) found that female middle school students had higher levels of cyberbullying, with the extent decreasing over time. That girls are not only victims of cyberbullying, but also take on the role of perpetrators, was also shown by Mishna et al. ( 2010 ). Here, however, the form of cyberbullying was a relevant factor. According to this, boys were more likely to be victims or perpetrators of direct bullying (e.g., threatening), while girls were more likely to be victims or perpetrators of indirect bullying (e.g., spreading rumors). In this study 3% of participants believed they were bullied because of their gender and 1% indicated bullying others because of their target's gender. In addition, Mehari and Farrell ( 2018 ) observed that aggressive behaviors did not show different patterns of relations according to gender and Longobardi et al. ( 2020 ) found that gender had no significant effect on any of the variables in their study.

The ethnic background also seems to play a role in online victimization and harassment. At the first of three measurement time points, for example, Low and Espelage ( 2013 ) showed that African American youth reported higher levels of endorsement of cyberbullying compared to White participants. These findings are contrasted by other researchers, who found no significant differences in overall reporting of cyberbullying by race or ethnicity (Schneider et al., 2012 ). These results are in line with those found by Mishna et al. ( 2010 ), in which Canadian students who did not speak English at home were not at a higher risk of being bullied. In addition, no differences in the prevalence of cyberbullying were found when the language spoken at home was considered. However, it was found that students who spoke English at home were more likely to spread rumors online than students who did not speak English at home. In this study 6% of the participants believed they were bullied online because of their race, while 3% of participants reported online bullying because of the target's race. Concerning the role of race, Ybarra et al. ( 2011 ) assessed the relative likelihood of reporting experiences of violence, such as bullying victimization, where the minority race (Black/African American) was found to be protective of all victimization experiences.

Individual and Contextual Factors

Other predictors for cyberbullying included personal traits and attitudes. For instance, Ang and Goh ( 2010 ) documented a three-way interaction in which high affective empathy moderated the effects of low cognitive empathy on cyberbullying for girls compared to boys. With regard to potential risk and/or protective factors, Schultze-Krumbholz and Scheithauer ( 2009 ) also considered empathy in their study. They found that cyberbullies and cybervictims showed less empathy and higher levels of relational aggression compared to students who did not engage in cyberbullying.

Barlett et al. ( 2019 ) examined the relationship between positive cyberbullying attitudes and subsequent cyberbullying perpetration in a longitudinal study of 3,000 Singaporean adolescents over a three-year period. They found that children with stable high or increasingly positive attitudes toward cyberbullying behaviors were also more likely to engage in such acts.

Using Data from a school-based census of about 20,400 youth, Schneider et al. ( 2012 ) showed that cyberbullying is far more frequent among nonheterosexual youth (33.1%), compared to heterosexual youth (14.5%). In their study, Mishna et al. ( 2010 ) asked the more than 2,100 participants whether they thought sexuality led to their bullying (2%) or was the reason they bullied others (2%).

In their study, including data from more than 43,200 adolescents from middle and high schools, Waasdorp et al. ( 2018 ) showed that overweight youth were more likely to report being a victim of cyberbullying (obese youth had even a 66% higher risk of being victims of cyberbullying). The findings of their study are supported by the work of Mishna et al. ( 2010 ), in which more than one in ten (11%) felt they have been victims of cyberbullying because of their appearance. According to this study, other characteristics that participants felt led to their bullying were disability (2%), family (2%) and school performance (5%).

School performance was also part of the investigation in other studies, with particular respect to the relationship between lower school performance and online victimization. While Bossler et al. ( 2012 ) reported that lower school performance is a predictor of online victimization, in the study of Schneider et al. ( 2012 ) no causality relationship between these two factors was analyzed, thus considering cyberbullying victimization as a potential predictor for school performance and vice versa. Referring to school as an important place for adolescents, a lower school attachment was also found to increase the likelihood of victimization online (Schneider et al., 2012 ) and, contrary to expectations, the negative influences of cyberbullying victimization on cognitive-behavior engagement were actually enhanced when students perceived a positive school climate. At the same time, the positive relationship between cyberbullying victimization and emotional engagement was mitigated by perceptions of a positive school climate (Yang et al., 2020 ).

Considering cyberbullying from the perpetrator's point of view, again appearance seems to be an important factor. Thus, further in the study of Mishna et al. ( 2010 ) 6% of the perpetrators stated that the appearance of the victim was the reason for their attacks. According to this study, other characteristics that participants stated as a reason for bullying others were disability (1%), school performance (3%) and family (2%). That cyberbullying occurs between parties who are familiar with each other or would even consider each other friends could also be demonstrated by Mishna et al. ( 2010 ). Here, friends (52%) were the most frequent targets of cyberbullying behavior. The influence of friends was also highlighted by Bossler et al. ( 2012 ), who positively associated a higher percentage of friends misbehaving on the computer with victimization. Besides the situation with friends, factors that an adolescent faces at home, and especially the quality of the caregiver-child relationship, seem to influence the likelihood of cyberbullying (Ang and Goh, 2010 ). In this context, parental monitoring and also the use of protective software seem to be associated with higher levels of cyberbullying (Bossler et al., 2012 ; Low and Espelage, 2013 ). At the same time, however, general use of technology also appears to be an indicator of an increased likelihood of being exposed to and experiencing violent media (Ybarra et al., 2011 ).

Overlap Between Traditional Bullying and Cyberbullying

The overlap between traditional bullying and cyberbullying has been investigated in several studies included in this review, revealing both similarities and differences between the two phenomena. Students' tendency not to report cyberbullying and their reasons for doing so are consistent with findings from studies examining traditional bullying. According to Mishna et al. ( 2010 ), these reasons include fear of retaliation or that the bullying could get worse. However, that young people also fear losing Internet or cell phone privileges seems to be rather a concern that occurs only in the context of disclosing cyberbullying.

Looking at the victims' perspective, the different forms of bullying often seemed to occur in parallel. Mitchell et al. ( 2011 ), analyzing data from more than 2,000 adolescents ages 10 to 17, found that 96% of youth who experienced online victimization also reported offline victimization during the same time period. Here, the offline victimizations linked most closely to online victimization were sexual victimizations (e.g., sexual harassment) and psychological and emotional abuse. The findings are in line with those found by Gradinger et al. ( 2009 ), who also found that most of the cyberbullying victims were also victims of traditional bullying at the same time.

In contrast, other studies, such as by Schultze-Krumbholz and Scheithauer ( 2009 ), concluded that cyberbullying is more common compared to traditional bullying. That cyberbullying and traditional bullying differ in frequency was also shown by Ortega-Ruiz et al. ( 2009 ). According to their study, however, the two phenomena are inversely related: significantly more adolescents were targeted by traditional bullying (two in ten) than by cyberbullying (one in ten). One in five participants reported being affected by both types of bullying.

Likewise, the consequences for victims of both forms appear to have similarities. For example, Schneider et al. ( 2012 ) found that the level of distress was highest for victims of both cyberbullying and school bullying. Additionally, Ortega-Ruiz et al. ( 2009 ) observed similar emotional responses to cyberbullying via the Internet and indirect bullying as a special type of traditional bullying (e.g., threats or insults). Emotions cited by victims included anger, stress, or fear. Although Low and Espelage ( 2013 ) also mention that cyberbullying seems to have significant overlaps with non-physical bullying (i.e., verbal and relational bullying), longitudinal analyses showed, according to them, less overlap between the different forms.

A connection between bullying at school (in the sense of traditional bullying) and cyberbullying is also assumed by Ševčíková and Šmahel ( 2009 ), who hypothesize that the reason for this could be the non-anonymous relationship between perpetrators and victims.

Predictors of Cyberhate

In their study involving more than 700 Finnish youth aged 15–18 who used Facebook as a social medium, Oksanen et al. ( 2014 ) analyzed the extent to which this age group is exposed to and victimized by online hate material. Two-thirds, and thus the majority, of the youths stated that they had already encountered online hate material, with 21% of the respondents having been victims of online hate material themselves. Furthermore, the authors observed that 70% of the participants more accidentally came across the online hate material, while 22% of the youth intentionally searched for this type of content. As a result of their analysis, they state that none of the sociodemographic variables (e.g., age, gender, living with parents) were found to be significant predictors of exposure to online hate material. Further, they link victimization by online hate material to various social and psychological factors, such as negative offline experiences.

These results are consistent with those found by Wachs et al. ( 2021 ), who reported neither significant differences in gender between girls (19%) and boys (15.4%) as related to cyberhate victimization, nor age differences between victims and non-victims of cyberhate.

Although socio-demographic data do not appear to be influential in the context of cyberhate, it is worth noting that, first, in the study by Oksanen et al. ( 2014 ) online hate material most frequently targeted at ethnicity/nationality (50%) and religious belief/faith (43%), and second, victims of online hate material were more likely to report material in which sex/gender was targeted compared to non-victims.

Study findings presented by Oksanen et al. ( 2014 ) suggest that certain states of agitation may increase the likelihood to be a victim of online hate material. For example, in contrast to those who did not perceive themselves as victims of online hate material, youth who did perceive themselves as victims were more likely to report being worried. This aspect was taken up by the research group in a later paper based on the same data set (Räsänen et al., 2016 ). In this, they concluded that worrying about becoming a victim of online hate material increases the likelihood of actually becoming a victim online.

According to the results of Oksanen et al. ( 2014 ), the hate material that victims saw online often directed at sexual orientation (68%), physical appearance (61%) and disability (31%). In addition, the authors observed that adolescents who were exposed to hate material online were more active online, not studying and their attachment to family was low. Specifically to the role played by being very active online, Ybarra et al. ( 2011 ) had achieved similar findings, and suggest that general technology use is an important factor in predicting risk for violent exposures (eg, hate sites) and experiences online.

The role of the family was again addressed in the study by Räsänen et al. ( 2016 ), but the analysis of the effects of covariates in the proposed model excluded the possibility to prove a correlation between living with parents and cyberhate victimization (even though basic statistics showed that not living with parents doubled the risk of online victimization).

Moreover, the authors report that the likelihood of becoming a victim of hate online is higher among youth who visit harmful sites on the Internet, the likelihood increases for those who deliberately search for this type of content, and the odds of victimization are almost four times higher for those producing hate materials. In line with this, Pauwels and Schils ( 2016 ) found that measures of extremism through new social media are associated with self-reported political violence. This relationship is most pronounced when users actively search for extremist content, with self-reported political violence being linked to various offline associations (e.g., racist and delinquent peers).

Looking at individual and contextual factors, Wachs et al. ( 2020 ) analyzed the influence of family affluence on cyberhate victimization. As the results of their study show, no differences were found between victims of cyberhate with low family affluence (33.9%), middle family affluence (31.7%), and high family affluence (35%).

Finally, Räsänen et al. ( 2016 ) found that, in contrast to the hypothesis that the number of friends on Facebook would increase exposure to hate material, this factor seems not to increase the likelihood of victimization. Slightly different is the influence of friends on cyberhate exposure, since Oksanen et al. ( 2014 ) found that 8% of the interviewed adolescents encountered hate material via a link from a friend.

Overlap of Cyberhate With Other Forms of Aggressive Behavior

As Wachs et al. ( 2020 ) showed, adolescents use similar coping strategies for dealing with cyberhate as they do for dealing with cyberbullying. Therefore, the authors not only assume conceptual and empirical overlaps between the two phenomena, but also that both forms have a similar impact on adolescents' behavior and emotions.

Based on the results of their study involving 1,900 French students, Blaya et al. ( 2020 ) noted that percentages of young people involved as victims or perpetrators were much higher offline than online.

In addition, they found that as victimization at schools (offline behavior) increased, the likelihood of exposure to hate material online (online behavior) also increased. Furthermore, their findings indicate that students who insult or threaten others at school also spread hate messages against others online. The way cyberhate is related to other forms of aggressive behavior is shown by the further results of the study. Thus, a weaker relationship was observed between cyberhate victimization and cyberhate perpetration, a moderate relationship was observed between school bullying victimization and cyberhate perpetration, and a moderate relationship was observed between cyberhate victimization and school bullying perpetration.

That exposure to hate material can be associated with offline physical victimization was also noted by Oksanen et al. ( 2014 ). In their later work, Räsänen et al. ( 2016 ) again consider this factor, stating that the odds of online hate victimization are higher if the user has already experienced online victimization.

Impact of Cyberbullying

Victimization through cyberbullying can result in very different effects. For example, some studies linked online victimization to psychological distress (Mitchell et al., 2011 ; Schneider et al., 2012 ), with the odds of distress appearing to remain constant over time (Ybarra et al., 2011 ). Other studies have shown that victims reported depressive symptoms (Ortega-Ruiz et al., 2009 ; Schneider et al., 2012 ; Low and Espelage, 2013 ), which, like somatic symptoms (e.g., bellyaches and stomach cramps), were most common when victims experienced both traditional bullying and cyberbullying compared with non-victims (Gradinger et al., 2009 ). According to Mitchell et al. ( 2011 ) greater trauma symptomatology, including depression in addition to anger and anxiety, was slightly but significantly linked to online victimization.

Stress is also a reaction that was observed associated with cyberbullying. For instance, Ortega-Ruiz et al. ( 2009 ) found that victims who were more affected by cyberbullying (both via the Internet and mobile phone) felt more stressed than occasional victims. In the case of cyberbullying via the Internet, more women (13.7%) than men (2%) reported feeling stressed. This finding is in line with the conclusion drawn by Baldry et al. ( 2019 ) who describe post-traumatic stress as a psychophysiological condition “resulting from stressful traumatic events such as school bullying and cyberbullying”.

In the context of cyberbullying, it was noted that some adolescents do not seem to be bothered by online attacks. This was shown, for example, in the studies by Ortega-Ruiz et al. ( 2009 ) and Mishna et al. ( 2010 ), the latter also finding a gender difference between males (55.6%) and females (28.6%), but only for cyberbullying via mobile phone. At the same time, however, various negative emotions have been reported that appear to be the result of cyberbullying. Across different studies, victims referred to feeling afraid and/or scared (Ortega-Ruiz et al., 2009 ; Mishna et al., 2010 ; Patchin and Hinduja, 2010 ). Others reported feeling alone, defenseless and worried, with females (30.6%) more likely than males (5.6%) to be worried by cyberbullying via cell phone (Ortega-Ruiz et al., 2009 ). Longobardi et al. ( 2020 ) observed a negative correlation of cyber victimization and perceived subjective happiness, which is consistent with the findings of Mishna et al. ( 2010 ), in which victims reported feeling sad. Feelings of embarrassment and upset (Ortega-Ruiz et al., 2009 ; Mishna et al., 2010 ) were also reported, with the number who felt very or extremely angry as a result of victimization remaining constant over a 36-month observation period (Ybarra et al., 2011 ). In addition, adolescents expressed feeling angry, as seen in the studies by Ortega-Ruiz et al. ( 2009 ) and Mishna et al. ( 2010 ), the latter group showing that more females (37%) than males (18%) reported feeling angry when bullied via the Internet.

As studies show, adolescents who experienced multiple forms of bullying and victimization appear to be at higher risk for poor adjustment. According to Gradinger et al. ( 2009 ), perpetrators who performed bullying online and offline were at highest risk for externalizing adjustment problems (e.g., reactive or instrumental aggression), whereas victims who experienced bullying online and offline were at highest risk for internalizing adjustment problems (e.g., depressive and somatic symptoms). In addition, adolescents who performed and experienced bullying online and offline were at highest risk for both externalizing and internalizing adjustment problems. In this context, it is worth mentioning the study by Waasdorp et al. ( 2018 ), which links victimization (including the experience of cyberbullying) to adjustment and social-emotional problems in addition to childhood obesity. And also the study by Mitchell et al. ( 2011 ) can be highlighted here, in which online victimization was strongly associated with delinquency (e.g., physically harming other children or adults, intentionally damaging things that belong to others, cheating on tests, skipping school) during the period studied.

Similarities between perpetrators and victims of cyberbullying do not only seem to exist with regard to adjustment problems. Thus, Patchin and Hinduja ( 2010 ) found a moderate relationship between both low self-esteem and cyberbullying offending and between low self-esteem and cyberbullying victimization. At the same time, however, victims of cyber- and/or school bullying are the ones who show an increased risk for suicidal behaviors. Zaborskis et al. ( 2018 ), analyzing data from a cross-national survey conducted in 2013 and 2014, showed that victims were at higher risk of suicidal thoughts, plans, and attempts regardless of the type of bullying they experienced. Among young people from Lithuania and Luxembourg (in addition, young people from Israel were interviewed), the association between cyberbullying and suicidal behavior was even greater compared to bullying that happened at school. Consistent with these findings Schneider et al. ( 2012 ) report suicide attempts of among victims of online and offline bullying, with cyberbullying victims (9.4%) more affected than school bullying victims (4.2%).

Impact of Cyberhate

According to Oksanen et al. ( 2014 ), there is an inverse relationship between cyberhate victimization and general psychological well-being, with victims of hate material more likely to be unhappy. In the context of emotional health, which can thus be affected by cyberhate victimization, coping strategies emerge into focus. In their study, Wachs et al. ( 2020 ) addressed the question of how adolescents deal with cyberhate. Taking into account differences in gender, age, socioeconomic status, and victimization status of the youth, six different coping strategies were confirmed. To mitigate the negative effects of cyberhate, adolescents primarily used constructive coping strategies, namely Technical coping (i.e., blocking a person), Assertiveness (telling the person to stop), and Close support (distracting oneself by spending time with friends). According to the authors, the fact that young people responded in this way indicates high levels of digital literacy which they know how to use, as well as high levels of self-efficacy. The remaining three strategies included Helplessness/Self-blame (not knowing what to do), Retaliation (do it back), and Distal advice (go to the police). Considering gender and age, girls were more likely to use all coping strategies (except Retaliation), and younger adolescents were more likely to use Technical coping strategies than older adolescents. Socioeconomic status was relevant to the extent that Distal advice and Technical coping were more common among adolescents with lower socioeconomic status than among peers with higher socioeconomic status.

The papers selected for this review provide precise indications on the factors that characterize the phenomena of cyberbullying and cyberhate, both in terms of predictive variables and their impact on adolescents. As the focus of this review was on cyberbullying and cyberhate as two interlinked instances of cyber-aggression, we compare the predictors and effects of one or the other phenomenon, and show how many of these factors characterize both cyberbullying and cyberhate, thus highlighting the level of overlap between them.

We have decided to analyse the predicting variables and consequences of the two phenomena separately, instead of framing them under the broader umbrella of cyber-aggression. The comparison follows this analysis. In such a way, we have the possibility to catch a new perspective on the investigation of these two phenomena.

Before presenting the results of this comparison, it should also be pointed out that the selected articles do not always provide a whole picture of the individual factors, and for this reason it has been necessary to enrich the description of some of them with additional sources of literature.

Overlapping Between Predictors of Cyberbullying and Cyberhate for Adolescents

Amongst the common factors that can predict perpetration and victimization in both cyberbullying and cyberhate, the role of the family has drawn the attention of many scholars. Specifically, the role of the family has been analyzed from different perspectives, sometimes favoring the aspects related to the emotional parent-child relationship, sometimes considering the family as a proxy of the social guardianship that can be a deterrent to cyber-aggression. The latter is particularly present in papers presenting studies that have borrowed the Routine Activity Theory (Cohen and Felson, 1979 ), one of the main theories of criminology, to explain the phenomena of cyber-aggression.

The importance of the parent-child relationship is highlighted both in the literature on cyberhate, with Oksanen et al. ( 2014 ) who found that weak attachment to family significantly predicts exposure to online hate content, and in the literature on cyberbullying, with Ang and Goh ( 2010 ) who highlighted the importance of positive caregiver-child relationships in reducing cyberbullying behavior among adolescents. These findings are consistent with similar studies on bullying and cyberbullying; among others, Murphy et al. ( 2017 ) found that attachment to parents is a deterrent to both becoming bullies and becoming victims of bullying, factors; Wang et al. ( 2009 ) found that bullying and cyber-bullying were similarly related to low parental support.

Surprisingly, the protective role of the families against the risks of cyberbullying, which is highlighted in numerous studies in the literature, including the impressive work by Li et al. ( 2021 ) with almost 215,000 school-aged children across 41 countries, has not been explicitly targeted in the papers on cyberbullying selected for this review, with the only exception of the paper by Low and Espelage ( 2013 ), who found a positive association between parental monitoring and higher levels of cyber-bullying perpetration (only for white adolescents). The guardianship offered by the parents against the risks of online hate exposure and victimization has been analyzed in Oksanen et al. ( 2014 ) and Räsänen et al. ( 2016 ). These studies did not find a correlation between family guardianship and online hate material exposure (Oksanen et al., 2014 ), nor was it possible to prove a correlation between living with parents and cyberhate victimization (Räsänen et al., 2016 ).

Although this would seem to indicate that the protective role of the family is a further overlapping factor between the phenomena of cyberbullying and cyberhate, as no significant link with either phenomenon was found in the literature reviewed, these results should be further commented on in light of the fact that they seem to contradict numerous studies on cyber-aggression (Li et al., 2021 ). Of particular significance, Räsänen et al. state in their paper: “Therefore, simply living with one's parents does not appear to ensure guardianship. Thus, it is difficult to interpret the lack of significance of this variable” (p. 14).

A possible interpretation could be found by investigating the mode and quality of guardianships exercised by parents. In this regard, it is worth mentioning the recent study by Wachs et al. ( 2021 ) that has furtherly analyzed the relationships between cyberhate victimization and the form of parental mediation, and found that instructive parental mediation is negatively associated with cyberhate victimization, while restrictive parental mediation determines the opposite effect. This confirms, moreover, the results of a previous study on the protective role of the family (Papatraianou et al., 2014 ), where the authors highlight the importance of instructive parental mediation: “Strong family relationships within the context of a young person's home can also help young people overcome online adversity, along with family permissions to use technology in a safe way”.

Closely related to the role of adolescent-family relations on cyberbullying and cyberhate, and with similar outcomes, is the role played by relations with friends. The literature analyzed does not provide consistent results, nor is it possible to give an unambiguous interpretation by extending the literature analysis to articles not included in this review. Specifically, Mishna et al. ( 2010 ) found that friends are the most frequent targets of cyberbullying attacks (in 52% of the cases studied), and this percentage increases to 84% in an earlier study by Ybarra and Mitchell ( 2004 ). Although less evident, the negative role of friends is also confirmed in relation to cyberhate, where Oksanen et al. ( 2014 ) found that friends are one of the sources from which adolescents receive links to hate material (in 8% of the cases analyzed), thus favoring their exposure to cyberhate. If these results were generalisable, it could be assumed that as the number of friends increases, the risks of Cyberbullying victimization or cyberhate exposure and victimization should increase. However, Räsänen et al. ( 2016 ) found that an increase in the number of friends on Facebook did not correspond to an increase in the risk of cyberhate exposure; the same result was reached by Kaakinen et al. ( 2018 ). Similarly, Wang et al. ( 2009 ) found that having more friends is not associated with cyberbullying.

The synthesis of these results could be found by prioritizing the analysis of the quality of relationships with friends or of the behaviors usually carried out by friends, rather than focusing on the number of friends. Bossler et al. ( 2012 ) underline that friends who caused most online harassment were those who committed various forms of computer deviance. The quality of the relationship with friends in relation to the phenomena of cyberbullying is underlined by subsequent works (not selected for this review), which recognize the protective role of friends against cyber-aggression: Papatraianou et al. ( 2014 ) have pointed out how strong and supportive friend relationships can support female adolescents' resilience toward online risks and aggression; similarly, Zych et al. ( 2019 ) have verified that the quality of relationships with friends is a strong protective factor against cyberbullying. Nevertheless, other scholars have only partially confirmed these results. For example, Bedrosova et al. ( 2022 ), who analyzed these aspects with samples of adolescents in the Czech Republic, Poland and Slovakia, found that friendship support was negatively related to cyberhate in the Czech Republic and Poland, but not in Slovakia and, even more surprisingly, friendship support was negatively related to cyberbullying only in the Czech Republic. Similar results were found by Kaakinen et al. ( 2018 ) who analyzed, with samples of American, British, German and Finnish adolescents and young adults, how cognitive social capital in the offline context (i.e., trust and sense of belonging in a group of friends) influences cyberhate victimization. In addition to the finding that the number of Facebook friends was not associated with online hate victimization reported above, the authors found that trust and sense of belonging in a group of friends was negatively associated with online hate victimization in all samples, but not for the Finnish one. With everything considered, we therefore encourage further studies on the role of friends in relation to cyberbullying and cyberhate.

The constructs related to sexuality (sexual orientation; sexual identification; etc.) represent a further element of overlapping between cyberbullying and cyberhate, being predictors of perpetration, victimization and exposure to online hate material (Mishna et al., 2010 ; Schneider et al., 2012 ; Oksanen et al., 2014 ). This is not surprising, given that the sexual sphere has always been a reason for discrimination, both at an individual level and with regard to groups that feel the need to unite in order to fight against discriminatory stereotypes that societies cannot ignore (Russell et al., 2001 ; Robin et al., 2002 ; Williams et al., 2003 ).

Overlapping Between the Impact of Cyberbullying and Cyberhate on Adolescents

The dimension that offers the major number of insights on the overlap between the consequences of cyberbullying and cyberhate on adolescents' individual well-being and emotions. In fact, the negative effects of cyber-aggression on emotional perception was found by authors who analyzed overall subjective happiness, direct emotional responses to experiences and long term emotional states induced by cyberbullying victimization and perpetration, as well as by cyberhate victimization or exposure (Ortega-Ruiz et al., 2009 ; Mishna et al., 2010 ; Ybarra et al., 2011 ; Longobardi et al., 2020 ; Wachs et al., 2020 ). In particular, this confirms Wachs et al. ( 2021 ) argument that the impact of cyberhate and cyberbullying on adolescents' emotions may be similar. Specifically to this point, it is worth mentioning Catherine Blaya, one of the authors of the EU report on the relation between cyberhate and kids (Machackova et al., 2020 ), who points out that “the emotional consequences are significant not only for victims but also for witnesses even though they are not targeted by the posted hateful contents. Both groups report experiencing anger and hate following their exposure or victimization” (as reported in Bedrosova, 2020 ). This confirms that the boundary between exposure to cyberhate and cyberhate victimization regarding its impact on users' emotions is extremely blurred (Machackova et al., 2020 ).

Strictly related to the effects of cyberbullying and cyberhate on adolescents' emotions, many studies have reported negative effects of cyberbullying victimization and perpetration on individuals' wellbeing, mainly consisting in depressive symptoms, somatic symptoms, post-traumatic stress symptoms and psychological distress (Gradinger et al., 2009 ; Ortega-Ruiz et al., 2009 ; Mitchell et al., 2011 ; Schneider et al., 2012 ; Fales et al., 2018 ). Although the selected literature does not provide similar information for adolescents who were exposed to or victims of cyberhate, an online survey administered to 1,512 adolescents (13–18 years.) in 2016 in UK revealed that young people who had been exposed to online hate content reacted to it with anger (37%), sadness (34%) and shock (30%) feelings (UK Safer Internet Centre, 2016 ). A distinct study involving young people in six countries slightly older than adolescents (18–25 years.) achieved similar results; respondents who had been exposed to online hate speech content reported almost the same negative emotional feelings as the adolescents in the UK survey: anger, sadness and shame (Reichelmann et al., 2020 ). Hence, findings from both studies on the consequences of cyberhate exposure and victimization identified symptoms which are common to the ones reported by the literature on cyberbullying, thus highlighting a further area of overlapping between cyberbullying and cyberhate.

Adolescents' coping strategies for cyberhate have been analyzed by Wachs et al. ( 2020 ), while the literature on cyberbullying selected for this review does not adress the issue of how adolescents deal with cyberbullying attacks. Nevertheless, the overlap has been highlighted by Wachs et al., who found out that adolescents use similar coping strategies for dealing with cyberhate as they do for dealing with cyberbullying. Specifically, the conclusions achieved by the authors are similar to those pointed out by other authors who have studied adolescents' coping strategies for cyberbullying (Livingstone et al., 2011 ; Machackova et al., 2013 ; Sticca et al., 2015 ).

Papers in this review underline the role of online activities in cyberbullying and cyberhate phenomena. As expected, the more adolescents spend their time online, the more they are involved in cyberbullying and cyberhate exposure (Ybarra et al., 2011 ; Oksanen et al., 2014 ; Räsänen et al., 2016 ). In this sense, the frequency in using Internet online tools is a predictor for both cyberbullying and cyberhate experiences. Ybarra et al. ( 2011 ) extend this concept, confirming that technology use in general is a predictor of both cyberbullying experiences and cyberhate exposure.

Cyberbullying and Cyberhate: Distinguishing Features

The analysis of the literature shows that the concepts of cyberbullying and cyberhate are in part overlapping, but have some characteristics that distinguish them from each other. In particular, by examining the results related to adjustment problems and the ideation of suicide, some important differences can be observed.

Adjustment problems and suicide have been targeted by some of the papers on cyberbullying selected for this review. Specifically, Waasdorp et al. ( 2018 ) found that adolescents who have been victims of cyberbullying appear to be more likely to experience adjustment problems; Gradinger et al. ( 2009 ) revealed that both bullies and victims are at high risk of adjustment problems, especially if they are involved in both face-to-face and cyberbullying experiences; Schneider et al. ( 2012 ) and Zaborskis et al. ( 2018 ) identified attempted suicide as a consequence of cyberbullying.

These themes are not present in the literature on cyberhate, probably because they reflect a deep psychological discomfort that can lead to extreme gestures such as suicide, a discomfort that emerges when the victim of the attack is the individual adolescent rather than a group of people (even if the adolescent identifies with the group). Previous research on the consequences of discrimination (online and offline) on adolescents' mental well-being can support this assertion. Discrimination is, in fact, a transversal theme to cyberbullying and cyberhate, where in the first case it is a tool aimed at hurting the individual, while in the second case it is a manifestation of hatred against a group of individuals (based on gender, race, religion, etc.), with which the adolescent may or may not recognize himself. Studies analyzing the effects of discrimination against individuals confirm how the risk of adjustment problems and the number of suicidal ideations and attempts increase for adolescents who have been discriminated against (Sinclair et al., 2012 ). The results are different if the discrimination is directed at a group. Particularly relevant is the work of Tynes et al. ( 2008 ) who present the results of a cross-sectional survey with 264 US high school students aged 14–18 years old to examine the impact of online racial discrimination on adolescents' psychological well-being. The authors distinguish between individual and vicarious discrimination: the former includes acts of discrimination that are explicitly directed at the individual, similar to what occurs in cyberbullying. Vicariuos discrimination refers to discrimination acts directed at same-race adults and peers in the adolescent's life, similar to what occurs in cyberhate. This study confirmed that individual racial discrimination is significantly related to depression and anxiety. However, vicarious discrimination does not correlate with measures of psychological adjustment, thus confirming our assertion that attacks on groups of individuals typical of cyberhate are experienced less dramatically by adolescents than attacks experienced by cyberbullying victims.

Another aspect that differentiates the two types of cyber-aggression analyzed in this review is that certain individual and personal characteristics often related to physical appearance (obesity, overweight, disability) can be predictors of cyberbullying victimization (Mishna et al., 2010 ; Waasdorp et al., 2018 ), but are not present among the predictors of cyberhate victimization. This is inherent in the defining characteristics of the two phenomena, since cyberbullying is an aggressive behavior against a person, whereas cyberhate is against a group of individuals, and therefore individual and personal physical factors have much less relevance. Exceptions are those physical traits that can be associated with ethnic groups (e.g., skin color; eye shape, etc.) and are often used as the basis for discriminatory phenomena. This would seem to include the physical appearance which Oksanen et al. ( 2014 ) include among the predictors of cyberhate victimization, although they do not specify which specific traits of physical appearance they refer to.

The analysis of the literature has shown that gender is a predictor for cyberbullying perpetration and victimization (Gradinger et al., 2009 ; Ortega-Ruiz et al., 2009 ; Mishna et al., 2010 ; Schneider et al., 2012 ; Low and Espelage, 2013 ), with a few exceptions (Mehari and Farrell, 2018 ; Longobardi et al., 2020 ). In contrast, gender does not appear to be a predictor for cyberhate exposure and victimization (Oksanen et al., 2014 ; Räsänen et al., 2016 ; Wachs et al., 2020 ). This result seems surprising in light of the fact that the gender is one of the categories targeted by haters (such as race, religion, etc.), but at the same time it confirms the lack of consistent findings in the literature on the relationships between gender and cyberhate perpetration, exposure and victimization (Bauman et al., 2021 ).

Similarly, it was not possible to determine an overlap between cyberbullying and cyberhate with regard to race/ethnicity, cultural context, language spoken at home (here considered as a proxy of ethnicity). In fact, the literature on cyberhate clearly indicates that race is a predictor of cyberhate exposure and victimization (Oksanen et al., 2014 ), as could be expected, since race appears as one of the discriminating factors that lead haters to attack groups of people on the basis of the color of their skin, their ethnicity, and their culture. On the contrary, the analysis of the selected literature (Mishna et al., 2010 ; Ybarra et al., 2011 ; Schneider et al., 2012 ; Low and Espelage, 2013 ) does not clearly show a causal link between race and cyberbullying experiences. This confirms what has already been pointed out in previous studies. In particular, in a previous review in which the relationships between cyberbullying, race/ethnicity and mental health outcomes were analyzed, the authors indicated that young whites are bullied more than their non-white peers, but clarify that it is not possible to establish whether this is a direct relationship (consequence of race and ethnicity), or rather is due to other factors that differentiate youth of color and their white peers in terms of technology ownership, social media preferences, and socioeconomic backgrounds (Edwards et al., 2016 ).

Finally, the impact of self-esteem, empathy and high levels of relational aggression on cyberhate experiences has not been sufficiently analyzed in the literature, so that it is not possible to make comparisons with what emerged for cyberbullying (Schultze-Krumbholz and Scheithauer, 2009 ; Ang and Goh, 2010 ; Patchin and Hinduja, 2010 ).

Conclusions

The first and most evident results from this review are that the cyberhate issue related to adolescents is less investigated than cyberbullying, and most of the papers dealing with one or the other phenomenon lacks of a holistic perspective, rooted in the broader concept of cyber-aggression, which makes it possible to approach cyberbullying and cyberhate as two distinct but often interconnected phenomena. In particular, the literature on cyberbullying lacks references to cyberhate, whereas the papers on cyberhate sometimes refer to literature on cyberbullying.

Nevertheless, by comparing the predictors and outcomes of cyberbullying and cyberhate, important overlapping factors between the two concepts emerge.

The most evident overlapping factors, as highlighted by this review, are the importance of the parent-child relationship to reduce the risk of cyber-aggression; the constructs related to sexuality (sexual orientation; sexual identification; etc.) as predictors of both phenomena; the protective role of the families against cyberbullying and cyberhate attacks, provided that parents offer instructive mediation while restrictive parental mediation determines the opposite effect; the role of good quality friendship relationships as deterrent against cyberbullying and cyberhate attacks; the impact of cyberbullying and cyberhate on adolescents' emotions as well as their consequences on individuals' wellbeing, mainly consisting in depressive symptoms, somatic symptoms, post-traumatic stress symptoms and psychological distress; the same coping strategies put in practice by victims of the two phenomena.

In addition to the factors common to cyberbullying and cyberhate, the literature highlights some of the characteristics that distinguish each of the two phenomena. In particular, differences concern the adjustment problems and the ideation of suicide, which have been found in studies on cyberbullying but not on cyberhate; individual and personal characteristics, often related to physical appearance (obesity, overweight, disability), as predictors of cyberbullying victimization only; the gender as a predictor for cyberbullying perpetration and victimization, while it does not appear to be a predictor for cyberhate exposure and victimization; the lack of a well-defined overlap between cyberbullying and cyberhate with regard to race/ethnicity, cultural context, language spoken at home (here considered as a proxy of ethnicity); the impact of self-esteem, empathy and high levels of relational aggression on cyber-aggression, even though this issue has not been sufficiently analyzed in the literature on cyberhate.

We argue that the results of this review can stimulate future research on cyberbullying and cyberhate where the two phenomena are analyzed as two interlinked instances of cyber-aggression, while respecting their distinctive features. Moreover, further research should investigate the effectiveness of prevention and intervention programs based on the shared commonalities and reciprocal influence of cyberbullying and cyberhate (e.g., the same coping strategies should be assessed against their capacity to empower adolescents regarding cyberhate and cyberbullying), according to a holistic approach to the general problem of cyber-aggression in adolescence.

Data Availability Statement

Author contributions.

GF: conceptualization, formal analysis, and paper drafting and revising. DT: conceptualization and paper drafting and revising. LS: conceptualization, formal analysis, data curation, methodology, and paper drafting. VS: formal analysis, data curation, methodology, and paper drafting and revising. SE: conceptualization and paper revising. All authors contributed to the article and approved the submitted version.

This work has been developed in the framework of the project COURAGE—A social media companion safeguarding and educating students (No. 95567), funded by the Volkswagen Foundation in the topic Artificial Intelligence and the Society of the Future.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors are particularly grateful to Lena Hilbig, Dr. Johanna Schäwel and Jonathan Mehl for their suggestions and advice.

1 https://datareportal.com/reports/digital-2020-july-global-statshot

2 https://datareportal.com/reports/digital-2021-april-global-statshot

3 https://www.aacap.org/AACAP/Families_and_Youth/Facts_for_Families/FFF-Guide/Social-Media-and-Teens-100.aspx

4 https://ec.europa.eu/eurostat/web/products-eurostat-news/-/edn-20210630-1 )

5 https://www.cdc.gov/violenceprevention/youthviolence/bullyingresearch/fastfact.html

• AACAP. (2018). The American Academy of Child and Adolescent Psychiatry. Social Media and Teens . Available online at: https://www.aacap.org/AACAP/Families_and_Youth/Facts_for_Families/FFF-Guide/Social-Media-and-Teens-100.aspx (accessed March 2018).
• Al-Hassan A., Al-Dossari H. (2019). Detection of hate speech in social networks: a survey on multilingual corpus . In: Computer Science and Information Technology (CS and IT) . India: AIRCC Publishing Corporation. p. 84–100. 10.5121/csit.2019.90208 [ CrossRef ] [ Google Scholar ]
• Anderson M., Jiang J. (2018). Teens, social media and technology 2018 . Pew Res. Center . 31 , 1673–1689. [ Google Scholar ]
• Ang R. P., Goh D. H. (2010). Cyberbullying among adolescents: The role of affective and cognitive empathy, and gender . Child Psychiatry Human Develop . 41 , 387–397. 10.1007/s10578-010-0176-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Anti-Defamation League. (2010). Responding to Cyberhate, Toolkit for Action (Preprint) . Available at: https://www.adl.org/sites/default/files/documents/assets/pdf/combating-hate/ADL-Responding-to-Cyberhate-Toolkit.pdf (accessed June 15, 2021).
• Atran S., Ginges J. (2015). Devoted actors and the moral foundations of intractable intergroup conflict in The moral brain: a multidisciplinary perspective , eds. Decety J., Wheatley T. (Boston Review; ), 69–85. [ Google Scholar ]
• Backe E. L., Lilleston P., McCleary-Sills J. (2018). Networked individuals, gendered violence: a literature review of cyberviolence . Violence Gender . 5 , 135–146. 10.1089/vio.2017.0056 [ CrossRef ] [ Google Scholar ]
• Baldry A. C., Sorrentino A., Farrington D. P. (2019). Post-traumatic stress symptoms among Italian preadolescents involved in school and cyber bullying and victimization . J. Child Family Stud . 28 , 2358–2364. 10.1007/s10826-018-1122-4 [ CrossRef ] [ Google Scholar ]
• Barlett C. P., Gentile D. A., Dongdong L., Khoo A. (2019). Predicting cyberbullying behavior from attitudes: A 3-year longitudinal cross-lagged analysis of Singaporean youth . J. Media Psychol. 31. 10.1027/1864-1105/a000231 [ CrossRef ] [ Google Scholar ]
• Bauman S., Perry V. M., Wachs S. (2021). The rising threat of cyberhate for young people around the globe . Child Adolesc. Online Risk Exposure . 149–175. 10.1016/B978-0-12-817499-9.00008-9 [ CrossRef ] [ Google Scholar ]
• Bedrosova M. (2020). European children's experiences of cyberhate . Parenting for a Digital Future. Blog Entry. Published by LSE—The London School of Economics and Political Science . Available online at: https://blogs.lse.ac.uk/parenting4digitalfuture/2020/06/24/euko-cyberhate/ (accessed June 24, 2020).
• Bedrosova M., Machackova H., Šerek J., Smahel D., Blaya C. (2022). The relation between the cyberhate and cyberbullying experiences of adolescents in the Czech Republic, Poland, and Slovakia . Comput. Human Behav . 126 , 107013. 10.1016/j.chb.2021.107013 [ CrossRef ] [ Google Scholar ]
• Blaya C., Audrin C. (2019). Toward an Understanding of the Characteristics of Secondary School Cyberhate Perpetrators . Front. Educ . 4 , 46. 10.3389/feduc.2019.00046 [ CrossRef ] [ Google Scholar ]
• Blaya C., Audrin C., Skrzypiec G. (2020). School Bullying, perpetration, and cyberhate: Overlapping issues . Contemp. Sch. Psychol. 1–9. 10.1007/s40688-020-00318-5 [ CrossRef ] [ Google Scholar ]
• Bossler A. M., Holt T. J., May D. C. (2012). Predicting online harassment victimization among a juvenile population . Youth Soc . 44 , 500–523. 10.1177/0044118X11407525 [ CrossRef ] [ Google Scholar ]
• Chetty N., Alathur S. (2018). Hate speech review in the context of online social networks . Aggression Violent Behav. 40 , 108–118. 10.1016/j.avb.2018.05.003 [ CrossRef ] [ Google Scholar ]
• Chisholm J. F. (2006). Cyberspace violence against girls and adolescent females . Annals N. Y. Acad. Sci . 1087 , 74–89. 10.1196/annals.1385.022 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cohen L., Felson M. (1979). Social change and crime rate trends: a routine activity approach . Am. Sociol. Rev . 44 , 588–608. 10.2307/2094589 [ CrossRef ] [ Google Scholar ]
• Corcoran L., Guckin C. M., Prentice G. (2015). Cyberbullying or cyber aggression?: A review of existing definitions of cyber-based peer-to-peer aggression . Societies . 5 , 245–255. 10.3390/soc5020245 [ CrossRef ] [ Google Scholar ]
• Costello M., Barrett-Fox R., Bernatzky C., Hawdon J., Mendes K. (2020). Predictors of viewing online extremism among America's youth . Youth Soc . 52 , 710–727. 10.1177/0044118X18768115 [ CrossRef ] [ Google Scholar ]
• Council of Europe. (1997). RECOMMENDATION No. R (97) 20 OF THE COMMITTEE OF MINISTERS TO MEMBER STATES ON HATE SPEECH . Available online at: https://rm.coe.int/1680505d5b (accessed June 15, 2021).
• Council of Europe. (2003). Additional Protocol to the Convention on Cybercrime, concerning the criminalisation of acts of a racist and xenophobic nature committed through computer systems. 28. European Treaty Series—No. 189 . Available online at: https://rm.coe.int/168008160f (accessed June 15, 2021).
• Edwards L., Kontostathis A. E., Fisher C. (2016). Cyberbullying, race/ethnicity and mental health outcomes: a review of the literature . Media Commun . 4 , 71–78. 10.17645/mac.v4i3.525 [ CrossRef ] [ Google Scholar ]
• ElSherief M., Nilizadeh S., Nguyen D., Vigna G., Belding E. (2018). Peer to Peer Hate: Hate Speech Instigators and Their Targets . Proc. Intern. AAAI Conf. Web Social Media . 12. [ Google Scholar ]
• Englander E. (2017). Defining Cyberbullying . Pediatrics . 140 , S148–S151. 10.1542/peds.2016-1758U [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Eurostat (2020). Community Survey on ICT Usage in Households and by Individuals . Available online at: https://ec.europa.eu/eurostat/web/products-eurostat-news/-/edn-20210630-1 (2020).
• Fales J. L., Rice S., Aaron R. V., Palermo T. M. (2018). Traditional and cyber-victimization among adolescents with and without chronic pain . Health Psychol. 37 , 291–300. 10.1037/hea0000569 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gerstenfeld P. B., Grant D. R., Chiang C. P. (2003). Hate online: a content analysis of extremist Internet sites . Anal. Social Issues Public Policy . 3 , 29–44. 10.1111/j.1530-2415.2003.00013.x [ CrossRef ] [ Google Scholar ]
• Goerzig A., Wachs S., Wright M. (2019). Cyberhate and cyberbullying: joint propensity and reciprocal amplification. Full panel: victims and perpetrators of hate speech . In: Annual Scientific Meeting of the International Society of Political Psychology . Lisbon (2019). (Unpublished). [ Google Scholar ]
• Gradinger P., Strohmeier D., Spiel C. (2009). Traditional bullying and cyberbullying: Identification of risk groups for adjustment problems . Z. Psychol. 217 , 205–213. 10.1027/0044-3409.217.4.205 [ CrossRef ] [ Google Scholar ]
• Grigg D. W. (2010). Cyber-aggression: Definition and concept of cyberbullying . Aust. J. Guid. Couns. 20 , 143–156. 10.1375/ajgc.20.2.143 [ CrossRef ] [ Google Scholar ]
• Harriman N., Shortland N., Su M., Cote T., Testa M. A., Savoia E., Youth (2020). Exposure to hate in the online space: an exploratory analysis . Int. J. Environ. Res. Public Health. 17 , 8531. 10.3390/ijerph17228531 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hawdon J., Bernatzky C., Costello M. (2019). Cyber-routines, political attitudes, and exposure to violence-advocating online extremism . Social Forces . 98 , 329–354. 2014-ZA-BX-0014 10.1093/sf/soy115 [ CrossRef ] [ Google Scholar ]
• Kaakinen M., Keipi T., Oksanen A., Räsänen P. (2018). How does social capital associate with being a victim of online hate? Survey evidence from the United States, the United Kingdom, Germany, and Finland . Policy Internet . 10 , 302–323. 10.1002/poi3.173 [ CrossRef ] [ Google Scholar ]
• Kemp S. (2020). Digital 2020: July Global Statshot Report Hootsuite and We Are Social . Available at: https://datareportal.com/reports/digital-2020-july-global-statshot (accessed September 13, 2020).
• Kemp S. (2021a). Digital 2021: April Global Statshot Report Hootsuite and We Are Social . Available online at: https://datareportal.com/reports/digital-2021-april-global-statshot (accessed June 10, 2021).
• Kemp S. (2021b). The Social Media Habits Of Young People In South-East Asia . Available online at: https://datareportal.com/reports/digital-youth-in-south-east-asia-2021 (accessed July 28, 2021).
• Lee E., Leets L. (2002). Persuasive storytelling by hate groups online: Examining its effects on adolescents . Am. Behav. Sci. 45 , 927–957. 10.1177/0002764202045006003 [ CrossRef ] [ Google Scholar ]
• Li Q., Luo Y., Hao Z., Smith B., Guo Y., Tyrone C. (2021). Risk factors of cyberbullying perpetration among school-aged children across 41 countries: a perspective of routine activity theory . Int. J. Bull. Prevent . 3 , 168–180. 10.1007/s42380-020-00071-6 [ CrossRef ] [ Google Scholar ]
• Liberati A., Altman D. G., Tetzlaff J., Mulrow C., Gøtzsche P. C., Ioannidis J. P. A. (2009). The prisma statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: E|xplanation and elaboration . J. Clin. Epid. 62 , 1–34. 10.1016/j.jclinepi.2009.06.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Livingstone S., Gorzig A., Olafsson K. (2011). Disadvantaged children and online risk . London: EU Kids Online, London School of Economics and Political Science. Available online at: http://eprints.lse.ac.uk/39385/ (accessed November 10, 2011). [ Google Scholar ]
• Longobardi C., Settanni M., Fabris M. A., Marengo D. (2020). Follow or be followed: exploring the links between Instagram popularity, social media addiction, cyber victimization, and subjective happiness in Italian adolescents . Child. Youth Serv. Rev . 113 , 104955. 10.1016/j.childyouth.2020.104955. [ CrossRef ] [ Google Scholar ]
• López C. A., López R. M. (2017). 4.8 hate speech, cyberbullying and online anonymity in Online Hate Speech in the European Union A Discourse Analytic Perspective (Stavros Assimakopoulos Fabienne H. Baider Sharon Millar) , 80–83. [ Google Scholar ]
• Low S., Espelage D. (2013). Differentiating cyber bullying perpetration from non-physical bullying: Commonalities across race, individual, and family predictors . Psychol. Violence . 3 , 39–52. 10.1037/a0030308 [ CrossRef ] [ Google Scholar ]
• MacAvaney S., Yao H. R., Yang E., Russell K., Goharian N., Frieder O. (2019). Hate speech detection: challenges and solutions . PloS One . 14. 10.1371/journal.pone.0221152 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Machackova H., Blaya C., Bedrosova M., Smahel D., Staksrud E. (2020). Children's Experiences With Cyberhate . EU Kids Online. [ Google Scholar ]
• Machackova H., Cerna A., Sevcikova A., Dedkova L., Daneback K. (2013). Effectiveness of coping strategies for victims of cyberbullying . Cyberpsychol. J. Psychosoc. Res. Cyberspace . 7 :1–2. 10.5817/CP2013-3-5 [ CrossRef ] [ Google Scholar ]
• Mardianto H, Anurawan, F., Chusniyah T., Rahmawati H., Ifdil I., Pratama M. (2019). Cyber aggression of students: the role and intensity of the use of social media and cyber wellness . Int. J. Innov. Creat. Change . 5 , 567–582. 10.22216/jbe.v1i1.3349 [ CrossRef ] [ Google Scholar ]
• Mehari K. R., Farrell A. D. (2018). Where does cyberbullying fit? A comparison of competing models of adolescent aggression . Psychol. Violence , 8 , 31–42. 10.1037/vio0000081 [ CrossRef ] [ Google Scholar ]
• Mishna F., Cook C., Gadalla T., Daciuk J., Solomon S. (2010). Cyber bullying behaviors among middle and high school students . Am. J. Orthopsychiatry . 80 , 362-374. 10.1111/j.1939-0025.2010.01040.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mitchell K. J., Finkelhor D., Wolak J., Ybarra M. L., Turner H. (2011). Youth internet victimization in a broader victimization context . J. Adolesc. Health . 48 , 128–134. 10.1016/j.jadohealth.2010.06.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moher D., Liberati A., Tetzlaff J., Altman D. G., P. R. I. S. M. A Group (2009). Preferred reporting items for Systematic reviews and meta-analyses: the prisma statement . PLoS Med. 6 :e1000097. 10.1371/journal.pmed.1000097 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Murphy T. P., Laible D., Augustine M. (2017). The influences of parent and peer attachment on bullying . J. Child Family Stud . 26 , 1388–1397. 10.1007/s10826-017-0663-2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nagle J. (2018). Twitter, cyber-violence, and the need for a critical social media literacy in teacher education: a review of the literature . Teach. Teach. Educ . 76 , 86–94. 10.1016/j.tate.2018.08.014 [ CrossRef ] [ Google Scholar ]
• O'Keeffe G. S., Clarke-Pearson K., Council on Communications Media . (2011). The impact of social media on children, adolescents, and families . Pediatrics . 127 , 800-804. 10.1542/peds.2011-0054 10.1542/peds.2011-0054 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Oksanen A., Hawdon J., Holkeri E., Näsi M., Räsänen P. (2014). Exposure to online hate among young social media users . Sociol. Stud. Child. Youth . 18 , 253-273. 10.1108/S1537-466120140000018021 [ CrossRef ] [ Google Scholar ]
• Ortega-Ruiz R., Elipe P., Mora-Merchán J. A., Calmaestra J., Vega E. (2009). The emotional impact on victims of traditional bullying and cyberbullying a study of spanish adolescents . Zeitschrift für Psychologie 217 :197–204. 10.1027/0044-3409.217.4.197 [ CrossRef ] [ Google Scholar ]
• Page M. J., McKenzie J. E., Bossuyt P. M., et al.. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews . BMJ . 372 ,71. 10.1136/bmj.n71 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Papatraianou L. H., Levine D., West D. (2014). Resilience in the face of cyberbullying: An ecological perspective on young people's experiences of online adversity . Pastoral Care in Education. 32 , 264–283. 10.1080/02643944.2014.974661 [ CrossRef ] [ Google Scholar ]
• Patchin J. W., Hinduja S. (2010). Cyberbullying and Self-Esteem . J. School Health . 80 :614–621. 10.1111/j.1746-1561.2010.00548.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pauwels L., Schils N. (2016). Differential online exposure to extremist content and political violence: Testing the relative strength of social learning and competing perspectives . Terror. Pol. Violence . 28:1 , 1–29. 09546553.2013.876414 10.1080/09546553.2013.876414 [ CrossRef ] [ Google Scholar ]
• Peterson J. K., Densley J. (2017). Cyber violence: What do we know and where do we go from here? . Aggr. Violent Behav . 34 , 193–200. 10.1016/j.avb.2017.01.012 [ CrossRef ] [ Google Scholar ]
• Pyzalski J. (2012). From cyberbullying to electronic aggression: Typology of the phenomenon . Emot. Behav. Difficult . 17 , 305–317. 10.1080/13632752.2012.704319 [ CrossRef ] [ Google Scholar ]
• Räsänen P., Hawdon J., Holkeri E., Keipi T., Näsi M., Oksanen A. (2016). Targets of online hate: Examining determinants of victimization among young Finnish Facebook users . Violence Victims . 31:4 , 708–725. 10.1891/0886-6708.VV-D-14-00079 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Reichelmann A., Hawdon J., Costello M., Ryan J., Blaya C., Llorent V., et al.. (2020). Hate knows no boundaries: online hate in six nations . Deviant Behav . 42 , 1100–1111. 01639625.2020.1722337 10.1080/01639625.2020.1722337 [ CrossRef ] [ Google Scholar ]
• Robin L., Brener N. D., Donahue S. F., Hack T., Hale K., Goodenow C. (2002). Associations between health risk behaviors and opposite-, same-, and both-sex sexual partners in representative samples of Vermont and Massachusetts high school students . Arch. Pediatrics Adolesc. Med. 156 , 349–355. 10.1001/archpedi.156.4.349 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Russell S. T., Franz B. T., Driscoll A. K. (2001). Same-sex romantic attraction and experiences of violence in adolescence . Am. J. Public Health. 91 , 903–906. 10.2105/AJPH.91.6.903 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schneider C. A., Rasband W. S., Eliceiri K. W. (2012). NIH Image to ImageJ: 25 years of image analysis . Nat Methods . 9(7) :671–675. 10.1038/nmeth.2089 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schultze-Krumbholz A., Scheithauer H. (2009). Bullying and cyberbullying offending among US youth: the influence of six parenting dimensions . J. Child Family Stud . 27 , 1–20. 10.1027/0044-3409.217.4.224 [ CrossRef ] [ Google Scholar ]
• Seglow J. (2016). Hate speech, dignity and self-respect . Ethical Theory Moral Pract . 19 , 1103–1116. 10.1007/s10677-016-9744-3 [ CrossRef ] [ Google Scholar ]
• Ševčíková A., Šmahel D. (2009). Online harassment and cyberbullying in the Czech Republic: Comparison across age groups . J. Psychol. 217 , 227–229. 10.1027/0044-3409.217.4.227 [ CrossRef ] [ Google Scholar ]
• Shapiro L. A. S., Margolin G. (2014). Growing up wired: Social networking sites and adolescent psychosocial development . Clin. Child Family Psychol. Rev. 17 , 1–18. 10.1007/s10567-013-0135-1 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sinclair K. O., Bauman S., Poteat V. P., Koenig B., Russell S. T. (2012). Cyber and bias-based harassment: Associations with academic, substance use, and mental health problems . J. Adolesc. Health. 50 , 521–523. 10.1016/j.jadohealth.2011.09.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith A. (2009). Radicalization—A Guide for the Perplexed (National Security Criminal Investigations, Trans.) . Canadian: Royal Canadian Mounted Police. [ Google Scholar ]
• Sticca F., Machmutow K., Stauber A., Perren S., Palladino B. E., Nocentini A., et al.. (2015). The coping with cyberbullying questionnaire: development of a new measure . Societies . 5 , 515–536. 10.3390/soc5020515 [ CrossRef ] [ Google Scholar ]
• Tennakoon H. (2021). Can the ‘Dark Triad'traits be a predictor of cyber hate speech . Academia Lett . 2. 10.20935/AL1965 [ CrossRef ] [ Google Scholar ]
• Tynes B., Giang M. T., Williams D. R., Thompson G. N. (2008). Online racial discrimination and psychological adjustment among adolescents . J. Adolesc. Health. 43 , 565–569., 0.2008.08.021 10.1016/j.jadohealth.2008.08.021 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• UK Safer Internet Centre. (2016). Creating a Better Internet for All: Young People's Experiences of Online Empowerment + Online Hate . Available online at: https://childnetsic.s3.amazonaws.com/ufiles/SID2016/Creating%20a%20Better%20Internet%20for%20All.pdf (accessed February 9, 2021)
• Waasdorp E. T., Hopkins J., Bradshaw C. P. (2018). Examining variation in adolescent bystanders' responses to bullying . School Psychol. Rev . 47 , 18–33. 10.17105/SPR-2017-0081.V47-1 [ CrossRef ] [ Google Scholar ]
• Wachs S., Costello M., Wright M. F., Flora K., Daskalou V., Maziridou E., et al.. (2021). DNT LET'EM H8 U!: Applying the routine activity framework to understand cyberhate victimization among adolescents across eight countries . Comput. Educ . 160 , 104026. 10.1016/j.compedu.2020.104026 [ CrossRef ] [ Google Scholar ]
• Wachs S., Gámez-Guadix M., Wright M. F., Görzig A., Schubarth W. (2020). How do adolescents cope with cyberhate? Psychometric properties and socio-demographic differences of a coping with cyberhate scale . Comput. Hum. Behav. 104 , 106167. 10.1016/j.chb.2019.106167 [ CrossRef ] [ Google Scholar ]
• Wang J., Iannotti R. J., Nansel T. R. (2009). School bullying among adolescents in the United States: Physical, verbal, relational, and cyber . J. Adolesc. health . 45 , 368–375. 2009.03.021 10.1016/j.jadohealth.2009.03.021 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Whittaker E., Kowalski R. M. (2015). Cyberbullying via social media . J. School Violence . 14 , 11–29. 15388220.2014.949377 10.1080/15388220.2014.949377 [ CrossRef ] [ Google Scholar ]
• Williams T., Connolly J., Pepler D., Craig W. (2003). Questioning and sexual minority adolescents: High school experiences of bullying, sexual harassment and physical abuse . Can. J. Commun. Mental Health . 22 , 47–58. 10.7870/cjcmh-2003-0013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yang C., Sharkey J. D., Reed L. A., Dowdy E. (2020). Cyberbullying victimization and student engagement among adolescents: Does school climate matter? Sch. Psychol. 35 , 158. 10.1037/spq0000353 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ybarra M. L., Mitchell K. J. (2004). Online aggressor/targets, aggressors, and targets: a comparison of associated youth characteristics . J. Child Psychol. Psychiatry . 45 , 1308–1316. 10.1111/j.1469-7610.2004.00328.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ybarra M. L., Mitchell K. J., Korchmaros J. D. (2011). National trends in exposure to and experiences of violence on the Internet among children . Pediatrics . 128 , e1376–e1386. 10.1542/peds.2011-0118 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zaborskis A., Ilionsky G., Tesler R., Heinz A. (2018). The association between cyberbullying, school bullying, and suicidality among adolescents . J. Crisis Interven. Suicide Prevent . 40 , 100–114. 10.1027/0227-5910/a000536 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zych I., Farrington D. P., Ttofi M. (2019). Protective factors against bullying and cyberbullying: a systematic review of meta-analyses . Aggress. Violent Behav. 45 , 4–19. 10.1016/j.avb.2018.06.008 [ CrossRef ] [ Google Scholar ]

• Intermediate

Cyberbullying

• 1.0 Overview
• 3.0 Vocabulary
• 4.0 Conversation Questions

Discussing conversation questions about cyberbullying can be an excellent topic for English learners to help improve their vocabulary and overall level of English.

This topic requires a broad range of vocabulary, including words related to technology and social media, as well as emotional states such as fear, anger or sadness. Engaging in a conversation about cyberbullying can also help learners to develop their critical thinking skills as they explore the ethical and moral implications of online behaviour.

What is Cyberbullying?

Cyberbullying is a form of bullying that occurs online, using electronic devices such as phones, computers or tablets. It can involve sending hurtful messages, sharing embarrassing photos or videos, spreading rumours or lies, or even impersonating someone else.

Cyberbullying can happen on social media platforms, in chat rooms, through messaging apps or email. The impact of cyberbullying can be severe and long-lasting, causing emotional distress, anxiety, depression and can even lead to suicide. It is essential to be aware of cyberbullying and take steps to prevent it by being respectful and kind online and reporting any incidents of cyberbullying.

Key Vocabulary Related to Cyberbullying

Try and use the following vocabulary when answering the question. Click to look up the definition in the dictionary

• troll (noun)
• threaten (verb)
• speak out (phrasal verb)
• hurtful (adjective)
• offensive (adjective)
• hate speech (noun)
• abusive (adjective)
• bully (verb)

Conversation Questions About Cyberbullying

• What is cyberbullying, and how does it differ from traditional bullying?
• Have you or someone you know experienced cyberbullying? How did it impact you/them?
• How can cyberbullying affect a person's mental health and well-being?
• What are some examples of cyberbullying that you can think of?
• Should cyberbullying be considered a criminal offense?
• Does anonymity on the internet contribute to an increase in cyberbullying incidents?
• What are some ways to prevent cyberbullying from happening?
• Should cyberbullying be punishable by imprisonment or fines?
• How can we create a safer and more respectful online environment?
• Should social media companies be held accountable for cyberbullying that takes place on their platforms?
• Should parents be held responsible for their child's cyberbullying behaviour?
• Should schools be responsible for addressing cyberbullying incidents that happen outside of school?

Keep The Conversation Going!

Gregory is a qualified TEFL teacher who has been teaching English as a Foreign Language (ESL) for over a decade. He has taught in-person classes in Spain and to English learners around the world online.

• Open access
• Published: 22 December 2021

Cyberbullying detection: advanced preprocessing techniques & deep learning architecture for Roman Urdu data

• Amirita Dewani   ORCID: orcid.org/0000-0002-3816-3644 1 ,
• Mohsin Ali Memon   ORCID: orcid.org/0000-0003-2638-4252 1 &
• Sania Bhatti   ORCID: orcid.org/0000-0002-0887-8083 1  

Journal of Big Data volume  8 , Article number:  160 ( 2021 ) Cite this article

10k Accesses

25 Citations

1 Altmetric

Metrics details

Social media have become a very viable medium for communication, collaboration, exchange of information, knowledge, and ideas. However, due to anonymity preservation, the incidents of hate speech and cyberbullying have been diversified across the globe. This intimidating problem has recently sought the attention of researchers and scholars worldwide and studies have been undertaken to formulate solution strategies for automatic detection of cyberaggression and hate speech, varying from machine learning models with vast features to more complex deep neural network models and different SN platforms. However, the existing research is directed towards mature languages and highlights a huge gap in newly embraced resource poor languages. One such language that has been recently adopted worldwide and more specifically by south Asian countries for communication on social media is Roman Urdu i-e Urdu language written using Roman scripting. To address this research gap, we have performed extensive preprocessing on Roman Urdu microtext. This typically involves formation of Roman Urdu slang- phrase dictionary and mapping slangs after tokenization. We have also eliminated cyberbullying domain specific stop words for dimensionality reduction of corpus. The unstructured data were further processed to handle encoded text formats and metadata/non-linguistic features. Furthermore, we performed extensive experiments by implementing RNN-LSTM, RNN-BiLSTM and CNN models varying epochs executions, model layers and tuning hyperparameters to analyze and uncover cyberbullying textual patterns in Roman Urdu. The efficiency and performance of models were evaluated using different metrics to present the comparative analysis. Results highlight that RNN-LSTM and RNN-BiLSTM performed best and achieved validation accuracy of 85.5 and 85% whereas F1 score was 0.7 and 0.67 respectively over aggression class.

Introduction

Cyberbullying (aka hate speech, cyberaggression and toxic speech) is a critical social problem plaguing today’s Internet users typically youth and lead to severe consequences like low self-esteem, anxiety, depression, hopelessness and in some cases causes lack of motivation to be alive, ultimately resulting in death of a victim [ 1 ]. Cyberbullying incidents can occur via various modalities. For example, it can take the form of sharing/ posting offensive video content or uploading violent images or sharing the pictures without permission of the owner etc. However, cyberbullying via textual content is far more common [ 2 ]. In Pakistan, the usage of internet, smartphones and social media has increasingly become prevalent these days and the very frequent users are youngsters. According to a report, more than 65% of all the users lie between 18 and 29, and typically women are more susceptible and unprotected. People often use offensive language, use hate speech, and become aggressive to bully celebrities, leaders, women and an individual [ 3 ]. In Pakistan, victims have reported life disturbing and annoying experiences and most of the victims are educated youngsters (age group of 21–30 years) [ 4 ]. The traffic in cyberspace has escalated significantly during covid-19 pandemic. A report “COVID 19 and Cyber Harassment”, released by DRF in 2020 highlights a great rise in the number of cyberbullying and harassment cases during the pandemic. The complaints registered with DRF’s Cyber Harassment Helpline were surged by 189% [ 5 ].

Recently, Roman Urdu language has been a contemporary trend and a viable language for communication on different social networking platforms. Urdu is national and official language of Pakistan and predominant among most communities across different regions. A survey statistic in [ 6 ] affirms that 300 million people are speaking Urdu language and approximately 11 million speakers are in Pakistan from which maximum users switched to Roman Urdu language for the textual communication, typically on social media. It is linguistically rich and morphologically complex language [ 7 ]. Roman Urdu language is highly variant with respect to word structures, writing styles, irregularities, and grammatical compositions. It is deficit of standard lexicon and available resources and hence become extremely challenging when performing NLP tasks.

An elaboration of script of Urdu instances and Roman Urdu is given in Table 1 . Instances highlighted are describing anti-social behavior.

This paper addresses toxicity/cyberbullying detection problem in Roman Urdu language using deep learning techniques and advanced preprocessing methods including usage of lexicons/resource that are typically developed to accomplish this work. Intricacies in analyzing the structure and patterns behind these typical aggressive behaviors, typically in a newly adopted language, and forming it as a comprehensive computational task is very complicated. The major contributions of this study are formation of a slang and contraction mapping procedure along with slang lexicon for Roman Urdu language and development of hybrid deep neural network models to capture complex aggression and bullying patterns.

The rest of the paper is organized as follows: Review of existing literature is presented in " Related Work " Section. " Problem statement " Section states research gap and gives formal definition of the addressed problem. " Methodology " Section describes the steps of research methodology and techniques and models used for the experimentations. Advanced preprocessing steps applied on Roman Urdu data are elaborated in " Data Preprocessing on Roman Urdu microtext " section. Implementation of proposed model architecture and hyperparameter settings are discussed in " Experimental Setup " section. " Results and Discussion " Section highlights and discusses study results and finally " Conlusion " Section concludes the research work and provides future research directions.

Related work

Due to the accretion of social media communication and adverse effects arising from its darker side on users, the field of automatic cyberbullying detection has become an emerging and evolving research trend [ 8 ]. Research work in [ 9 ] presents cyberbullying detection algorithm for textual data in English language. It is considered as one of the pioneers and highly cited research. They divided the task in text-classification sub problems related to sensitive topics and collected 4500 textual comments on controversial YouTube videos. This study implemented Naive Bayes, SVM and J48 binary and multiclass classifiers using general and specific feature sets. Study contributed in [ 10 ] applied deep learning architectures on Kaggle dataset and conducted experimental analysis to determine the effectiveness and performance of deep learning algorithms LSTM, BiLSTM, RNN and GRU in detecting antisocial behavior. Authors in [ 11 ] extracted data from four platforms i-e Twitter, YouTube, Wikipedia, and Reddit for developing an online hate classifier in English language using different classification techniques. Research carried out in [ 12 ] developed an automated approach to detect toxicity and unethical behavior in online communication using word embeddings and varying neural network layers. They suggested that LSTM layers and mimicked word embedding can uncover such behavior with good accuracy level.

Few of the studies in recent years has been contributed by researchers on other languages apart from English. Research work in [ 13 ] is unique and has gathered textual data from Instagram and twitter in Turkish language. They have implemented Naïve Bayes Multinomial, SVM, KNN and decision trees for cyberbullying detection along with Chi-square and information gain (IG) for feature selection. Work accomplished in [ 14 ] also addresses the problem of cyberaggression in Turkish language. The work extends comparison of different machine learning algorithms and found optimal results using Light Gradient Boosting Model. Van Hee, Cynthia, et al. in [ 15 ] proposed cyberbullying detection scheme for Dutch language. This is the first study on Dutch social media. Data was collected from ASKfm where users can ask and answer questions. The research uses default parameter settings for un-optimized linear kernel SVM based on n-grams and keyword system to identify bullying traces. F1 score for Dutch language was 61%. Problem of Arabic language cyberbullying detection was addressed and accomplished in [ 16 ]. This study used Dataiku DSS and WEKA for ML tasks. The data was scrapped from facebook and twitter. The study concluded that even though the detection approach was not comparable with the other studies in English language but overall Naive Bayes and SVM yield reasonable performance. Research work in [ 17 ] by Gomez-Adorno, Helena, et al. proposed automatic aggression detection for Spanish tweets. Several types of n-grams and linguistically motivated patterns were used but the best run could only achieve F1 score of 42.85%. Studies presented in [ 18 , 19 , 20 ] are based on automatic detection of cyberbullying content in German language. Research conducted in [ 18 ] proposed an approach based on SVM, CNN and ensemble model using unigram, bigrams and character N-grams for categorizing offensive tweets in German language. Research presented in [ 21 ] attempted for the very first time to identify bullying traces in Indonesian language. Association Rule mining and FP growth text mining were used to identify trends for bullying patterns in Jakarta and Surabaya cities using social media text. This baseline study on Indonesian language was further extended by Nurrahmi, Hani et al. in [ 22 ]. Study in [ 23 ] made first attempt to develop a corpus of code-mixed data considering Hindi and English language. They proposed a scheme for hate speech detection using N-grams and lexical features. An ensemble approach by combining the predictions of Convolutional Neural Network (CNN) and SVM algorithms were used for identifying such patterns. The weighted F1 score for Hindi language ranged between 0.37 and 0.55 for different experiments [ 24 ]. In the year 2019, Association for computational linguistics initiated the project for automatic detection of cyberbullying in Polish language [ 25 ]. Research conducted in [ 26 ] attempted to uncover cyberbullying patterns in Bengali language implementing passive aggressive, SVM and logistic regression. The optimum accuracy achieved was 78.1%. Recently, work contributed in [ 27 ] presented first study in Roman Urdu using lexicon based approach. The dataset was highly skewed comprising of only 2.2% toxic data. As according to [ 28 ], biased sampling and measurement errors are highly prone to classification errors when working on such datasets. Moreover, pattern detection based on predefined bullying and non-bullying lexicons were shortcomings of this study.

For automated detection of complex cyberbullying patterns, studies contributed by different scholars employ supervised, unsupervised, hybrid and deep learning models, vast feature engineering techniques, corpora, and social media platforms. However, the existing literature is mainly oriented towards unstructured data in English language. Some recent studies and projects have been initiated in other languages as discussed previously. To the best of our knowledge and literature review, no detailed work has been contributed in Roman Urdu to systematically analyze cyberbullying detection phenomenon using advanced preprocessing techniques (involving the usage of Roman Urdu resources) and deep learning approaches under different configurations.

Problem statement

The escalated usage of social networking sites and freedom of speech has given optimal ground to individuals across all demographics for cyberbullying and cyberaggression. This leaves drastic and noticeable impacts on behavior of a victim, ranging from disturbance in emotional wellbeing and isolation from society to more severe and deadly consequences [ 29 ]. Automatic Cyberbullying detection has remained very challenging task since social media content is in natural language and is usually posted in unstructured free-text form leaving behind the language norms, rules, and standards. Evidently, there exists a substantial number of research studies which primarily focus on discovering cyberbullying textual patterns over diverse social media platforms as discussed previously in literature review section. However, most of the detection schemes and automated approaches formulated are for resource-rich and mature languages spoken worldwide. Roman Urdu is typically spoken in South Asia and is a highly resource deficient language. Hence this research puts novel efforts to propose data pre-processing techniques on Roman Urdu scripting and develop deep learning-based hybrid models for automated cyberbullying detection in Roman Urdu language. The outcomes of this study, if implemented, will assist cybercrime centers and investigation agencies for monitoring social media contents and in making cyberspace secure and safer place for all segments of society.

Methodology

The research methodology is depicted in Fig.  1 .

Proposed research methodology

The development of hate speech/cyberbullying corpus with minor skew and automated development of domain specific roman Urdu stop words is published in our previous work [ 30 ]. The work details formation of computational linguist resources. Further steps of methodology are discussed in subsequent sections. The Deep Neural Network (DNN) based techniques and models used for the experimentations are detailed below.

Model description

Recurrent neural networks (rnn).

RNN [ 31 ] has been applied in literature for successive time series applications with temporal dependencies. An unfolded RNN can handle processing of current data by utilizing past data. Meanwhile, RNN has the issue of training long-term dependencies. This has been addressed by one of the RNN variant.

Long short-term memory networks (LSTM)

LSTM has been employed as an advanced version of RNN network. It resolves the shortcoming of RNN by applying memory cells also known as hidden layer units. Memory cells are controlled through three gates named as: input gate, output gate and forget gate. They have the self-connections which store the temporal state of network [ 31 ]. Input and output gates address and control the flow of information from memory cell input and output to rest of the network. The forget gate, usually called as a remember vector, transfers the information with higher weights from previous neuron to the next neuron. The forget gate is added to the memory cell. The information resides in memory depending upon the high activation results; the information will be stored in memory cell iff the input unit has high activation. However, the information will be transferred to next neuron if the output unit has high activation. Otherwise, input information with high weights resides in memory cell [ 31 ].

Mathematically, LSTM network can be described as [ 32 ]:

where W h   ∈  R m × d and U h   ∈  R m × m indicates weight matrices, x t denotes the current word embedding, b h   ∈  R m refers to bias term, whereas f(x ) is a non-linear function.

LSTM has more complex architecture including hidden states and tends to remember information for either short or long term. The hidden state [ 33 ] of LSTM is computed as follows:

where f t denotes the forget gate, i t refers to the input gate, c t denotes the cell state, o t is the output gate, h t is the regular hidden state, σ indicates sigmoid function, and ◦ is the Hadamard product.

Bidirectional Long short-term memory networks (BiLSTM)

In the traditional recurrent neural network model and LSTM model, the propagation of information is only in forward direction. This results in computation of an output vector only based on the current input at time t and the output of the previous unit. The back propagation of information in network is achieved by merging two bidirectional recurrent neural network (BiRNN) and LSTM units, one for forward direction and one for backward direction. This helps in capturing contextual information and enhances learning ability [ 34 ].

In bidirectional LSTM, outputs of two LSTM networks are stacked together. The first LSTM is a regular sequence starting from the starting of the paragraph, while the second LSTM is a standard sequence, and the series of inputs are fed in the opposite order. The first hidden state is denoted by ht forward whereas second LSTM unit’s hidden state is denoted by ht backward . After processing data, the final state ht Bilstm is computed by concatenating the two hidden states as given in Eq.  3 .

where  ⊕  denotes a concatenation operator.

Convolutional neural networks (CNN)

Convolution neural networks (aka CNN), originally incorporated for image processing tasks, have become very efficacious in different NLP and text classification applications. The network identifies correlations and patterns of data via their feature maps. Information about local structure of data is extracted by applying multiple filters with different dimensions [ 35 ].

Data preprocessing on Roman Urdu microtext

Big social media data in Roman Urdu language is inconsistent, incomplete, or precise, missing in certain behaviors or trends, and is likely to incorporate many errors. Roman Urdu users highly deviate language norms while communicating on social media. Hence data preprocessing is immensely significant. Some major data preprocessing steps applied on Roman Urdu microtext are detailed below.

Handling Unicode and encoded text formats

Unicode scheme provides every character in natural language text a unique code from 0 to 0 × 10FFFF. The uncleaned Roman Urdu data comprised of special symbols, emojis, and other typical stray characters represented using Unicode. We used Unicode transformation type 8 encoding to convert the data. This data was converted and handled using re and string modules in python.

Text cleaning

Text cleaning is essential step to eliminate or at least reduce noise from Microtext. This step comprised of case transformation, removal of punctuations and URLs, elimination of additional white spaces, exclusion of hashtags, digits & special character removal and removal of metadata/non-linguistic features.

Tokenization

Tokenization is immensely essential phase of text processing. It is the process of generating tokens by splitting textual content into words, phrases, or other meaningful parts. It is generally a form of text segmentation [ 36 ]. Tokenization was performed using Keras tokenizer to prepare the text for implementing deep learning networks.

Filtering stop words

Stop words are non-semantic division of text in natural language. The necessity that stop words should be eliminated from text is that they make the text higher dimensional with redundant features which are less significant for analysts. Removing stop words reduces the dimensionality of term space [ 37 ]. Development of domain specific stop words in Roman Urdu language automatically using statistical techniques and bilingual experts’ input, comprising of 173 words is detailed in our previous work [ 30 ]. Insignificant Roman Urdu words were typically articles such as ek (ایک), conjunctions and pronouns such as tum (تم), tumhara (تمھارا), us (اُس), wo/who (وہ), usko (اُسکو), preposition such as main (میں), pe (پے), par (پر), demonstratives such as ye (یے), inko (انکو), yahan(یہاں), and interrogatives such as kahan (کہاں), kab (کب), kisko (کس کو), kiski (کس کی) etc. Stop words were removed from Roman Urdu corpus, leaving behind the index terms which are important.

Mapping slangs and contractions

Existing libraries, APIs and toolkits in python language primarily support preprocessing functions for English and other mature languages. They can be partially used for Roman Urdu language. Moreover, most of the communication in Roman Urdu comprises of bully terms being used as slangs. High dimensional textual data also suppress significant features. Hence contraction mapping is mandatory for dimensionality reduction and to capture complex bullying patterns. Currently, Pycontractions Library [ 38 ] only supports English contraction mapping process. To address this problem, the study developed data slang mapping process. To map slangs to original terms and phrases in Roman Urdu language, we created Slang lexicon in Roman Urdu (SLRU) which also included Roman Urdu abuses and offensive terms used as a norm by Roman Urdu users. SLRU is in the form of a dictionary. It comprises of the key: value pairs, where key is the slang and value is its equivalent Roman Urdu phrase/term such as “AFIK”: “Jahan tak mujhay pata hai”, ASAP: “Jitna jaldi ho sakay”, “tbh”: “Sach main” and so on. The process of slang mapping is detailed in Fig.  2 .

Mapping process for slangs in Roman Urdu

The results of mapping process are highlighted in Fig.  3 .

Mapping on Roman Urdu Data

Experimental setup

This section discusses implementation of proposed neural network architecture and all hyper-parameter settings. All the experiments were performed on 11 Gen, core i7, 4 cores, 8 logical microprocessors, with 2.8 GHz processor speed, 256 GB Solid State Drive and python version 3.8, 64 bits.

Proposed model implementation and hyper-parameter settings

All models were implemented and trained in Keras; a high-level neural network API that works with open-source machine learning framework called TensorFlow [ 39 ]. All the implementation was accomplished using PyCharm. The optimal parameters and results were achieved through repeated experimentations.

Data was split into training and testing datasets. The data split was 0.8 for training and 0.2 for testing i-e 80% of data instances were used for training and 20% were holdout for testing and validation purpose. The sets were made using shuffled array. This allows model to learn over different data instances. Moreover, it helps to uncover reliability of model and consistency of results over repeated executions. Random state is also generated using numpy.random [ 40 ] for random sampling during splitting of data to ensure reproducible splits.

Textual input data must be integer encoded. In RNN-LSTM architecture, a sequential model was created. Initially an Embedding layer was added to the network and textual Roman Urdu data was provided as an input. Embedding layer embedded high dimensional text data in low dimensional vector space for generating dense vector representation of data. Embedding was formed using 2000 features and 128 embed dimensions. The experiment was initially executed on 20 epochs and 50 batch size. The batch size was based on the fact that model was having single lstm layer, and comparatively took lower training and validation time per step. The execution time for each epoch was approximately 10 ms. SpatialDropout1D was used with rate of 0.3. It helped to regularize the activations and maintain effective learning rate of the model. For updating network weights iteratively, this work uses binary cross entropy loss function and Adam optimizer. Sigmoid activation function was also implemented. It is denoted by f(x) and is defined as:

The Spatial Dropout layer was implemented instead of a simple Dropout. The major reason being was to retain the context of textual data established by neighboring words. Dropping random words (except for stop words, which were already removed during preprocessing step) can highly affect the context of uttered sentences and ultimately the performance of model. We incorporated two hidden dense layers denoted by D 1 and D 2 . The output of each hidden layer was computed to get the final output for cyberbullying text detection.

Keras tokenizer was used to accomplish pre-tokenization of all the data required for the implementation of RNN-biLSTM model. We created a sequential model with Embedding layer having 2000 maximum features. Subsequently a biLSTM layer comprising of two LSTM layers, one to read sequence in forward direction and other in backward direction, each with 64 units was added. Hidden layer (H 1 ) was formed using sigmoid activation function. For down sampling the feature maps, Dropout layer was added with 0.2 dropout rate. Moreover, we used 128 batch size to utilize low to moderate computational resources while still not slowing down the training process. Batch size highlights number of samples processed by model before updating of internal parameters. To combat overfitting, we added second dropout layer with rate of 0.25. Adam optimization was used with learning rate of 0.01 since batch size was not too small. For this model, we used binary cross entropy loss function. As the Epochs increase, the generalization ability of the model improves. However, too many epochs also lead to the problem of overfitting. The model was executed over different number of Epochs and average execution time for each epoch was 13 to 15 ms. The performance of model stabilized over 20 Epochs, above which the improvement was almost negligible.

In CNN model, initially the sentence was transformed into matrix where each row of matrix represented word vectors representation of data. We used 1000 features and 32 dimensions. Two convolutional filters were applied with 8 and 16 filters and 3 kernel size. Each filter was used to perform one dimensional convolution on word embeddings. Both Layers were 1D in nature. We set two dropout layers with dropout rate of 0.25 to improve generalization ability of developed model. Hidden layers with Relu and sigmoid activation functions were used. To extract most salient and prominent features, global maximum pooling layers were used with pool size = 2. Flatten layer was created after convolutional layers to flatten the output of the previous layer to a single long feature vector. The experiment was simulated over different Epochs. However, results got stable at 30 epochs.

Results and discussion

Empirical evaluation of cyberbullying detection scheme performance in Roman Urdu and experimental setups is accomplished via accuracy, precision, recall, and f1 measure metrics.

All the implemented models were executed several times over number of epochs to get consistency in evaluation parameters until it was a minor difference of ± 0.1. The results for LSTM are depicted in Fig.  4 . To ensure results validity and reliability, for a comparatively less skewed dataset, F1 measure (i-e a harmonic mean of precision and recall) is used as an evaluation metric. Furthermore, we have also reported macro and weighted average scores across all the classes. The evaluation results of RNN-LSTM are given in Fig.  4 .

RNN-LSTM evaluation Results

F1 score for RNN-LSTM over cyberbullying class was only 70%, however for non-cyberbullying class, score was 90%. We observed that nearly all the instances of majority class of non-cyber bullying are correctly classified by this model. The experimental simulation depicting model accuracy and validation accuracy during training and validation phases, before and after stabilization of evaluation parameters is represented in Figs.  5 and 6 respectively.

RNN-LSTM Model accuracy graph for 20 epochs

RNN-LSTM Model accuracy graph for 50 epochs

The accuracy improved over subsequent epochs. However, after 20 epochs it got stabilized. The average accuracy produced by this model was 93.5% during training and 85.5% during validation. Overall curve variation is indicating that no overfitting problem arise. The model loss during training and validation loss during testing over 20 and 50 epochs is shown in Figs.  7 and 8 respectively. The cross-entropy loss considered during configuration over different epochs converged well, thus indicating optimal model performance.

RNN-LSTM Model loss plot- Binary Cross entropy for 20 epochs

RNN-LSTM Model loss plot- Binary Cross entropy for 50 epochs

The evaluation results of RNN-BILSTM model over 20 epochs are given in Fig.  9 .

RNN-BiLSTM evaluation Results

RNN-LSTM also performed reasonably well for cyberbullying detection task on Roman Urdu data. F1 score for non-cyberbullying content prediction was 90% whereas for cyberbullying content, the score was 67% only. This indicates that model erroneously classified/misclassified some of the aggressive class instances and TN rate was at average. Figs.  10 and 11 are depicting model accuracy and validation accuracy for RNN-BiLSTM.

RNN-biLSTM Model accuracy plot for 20 epochs

RNN-biLSTM Model accuracy plot for 50 epochs

The accuracy improved highly during training process up to 20 epochs. Overall average accuracy was 97% in training and 85% on validation set. 20% of the data was used for as a validation set, as stated earlier. During experimentation, we identified that accuracy of our model is not improving after a specific point i-e after 20 Epochs. The trivial variations can be clearly visualized from the graph in Fig.  9 . Model loss and validation loss during training and testing process for RNN-BiLSTM over 20 and 50 epochs is given in Figs.  12 and 13 respectively.

RNN-BiLSTM Model loss plot- Binary Cross entropy for 20 epochs

RNN-BiLSTM Model loss plot- Binary Cross entropy for 50 epochs

The cross-entropy loss was minimal (approximately 1.2), indicating good prediction capability of developed model.

Figure  14 represents the evaluation results for CNN model.

CNN model evaluation Results

CNN performed well for prediction of non-cyberbullying content, providing F1 score of 87%. However, model did not yield good efficiency for categorizing cyberbullying class, producing f1-score of 52%. The repeated experiments performed for CNN showed continuous improvements up to 30 Epochs. Figure  15 depicts model accuracy and validation accuracy. The experimental simulation over 50 epochs only shown minor improvements as represented in Fig.  16 . The average execution time for Epoch was 9 ms each. The training accuracy of 98% was achieved over different executions whereas model produced 85% validation accuracy.

CNN Model accuracy plot for 30 epochs

CNN Model accuracy plot for 50 epochs

CNN model loss and validation loss results at 30 and 50 epochs are presented in Fig.  17 and 18 respectively. The loss was minimal during training and converged. During validation the loss increased and diverged indicating only moderate performance over unseen instances typically from aggressive class.

CNN Model loss plot- Binary Cross entropy for 30 epochs

CNN Model loss plot- Binary Cross entropy for 50 epochs

The compiled model results indicating evaluation measures at stabilized epochs are depicted in Table 2 .

Cyberbullying has become an alarming social threat for today’s youth and has recently gained huge attention from research community. This research has addressed the problem of cyberbullying detection in Roman Urdu Language. Since Roman Urdu is highly resource deficient language, having different writing patterns, word structures, and irregularities thus making this work a challenging task. In this work we have presented advanced preprocessing techniques mainly a slang mapping mechanism, domain specific stop word removal, handling encoded formats and formulation of deep learning architecture to detect cyberbullying patterns in Roman Urdu language. We created experiments with vast parameters to build optimal classifier for cyberbullying tweets. The results highlighted that RNN-LSTM and RNN-BiLSTM with concatenation of forward and backward units provided better performance in 20 Epochs as compared to CNN. The existing work can be extended in numerous ways. The future studies can focus on development of ensemble models to uncover harassing and hate speech patterns. Moreover, the incorporation of context-specific features and handling of morphological variations might produce better results.

Availability of data and materials

The used raw dataset in this research is not publicly available. The data that support the findings of this research work are available from the corresponding author, on valid request due to privacy and ethical restrictions.

Abbreviations

Recurrent neural network

Long-short term memory

Bidirectional long-short term memory

Convolutional neural network

Social networking

Right to left

Left to right

True negative

Hellfeldt K, López-Romero L, Andershed H. Cyberbullying and psychological well-being in young adolescence: the potential protective mediation effects of social support from family, friends, and teachers. Int J Environ Res Public Health. 2020;17(1):45.

Article   Google Scholar  

Dadvar M. Experts and machines united against cyberbullying [PhD thesis]. University of Twente. 2014.

Magsi H, Agha N, Magsi I. Understanding cyber bullying in Pakistani context: causes and effects on young female university students in Sindh province. New Horiz. 2017;11(1):103.

Google Scholar  

Qureshi SF, Abbasi M, Shahzad M. Cyber harassment and women of Pakistan: analysis of female victimization. J Bus Soc Rev Emerg Econ. 2020;6(2):503–10.

S. Irfan Ahmed, Cyber bullying doubles during pandemic. https://www.thenews.com.pk/tns/detail/671918-cyber-bullying-doubles-during-pandemic . Accessed 24 Aug 2020.

Shahroz M, Mushtaq MF, Mehmood A, Ullah S, Choi GS. RUTUT: roman Urdu to Urdu translator based on character substitution rules and unicode mapping. IEEE Access. 2020;8:189823–41.

Mehmood F, Ghani MU, Ibrahim MA, Shahzadi R, Mahmood W, Asim MN. A precisely xtreme-multi channel hybrid approach for roman urdu sentiment analysis. IEEE Access. 2020;8:192740–59.

Alotaibi M, Alotaibi B, Razaque A. A multichannel deep learning framework for cyberbullying detection on social media. Electronics. 2021;10(21):2664.

Dinakar K, Reichart R, Lieberman H. Modeling the detection of textual cyberbullying. In: 5th international AAAI conference on weblogs and social media. 2011.

Iwendi C, Srivastava G, Khan S, Maddikunta PKR. Cyberbullying detection solutions based on deep learning architectures. Multimed Syst. 2020. https://doi.org/10.1007/s00530-020-00701-5 .

Salminen J, Hopf M, Chowdhury SA, Jung S, Almerekhi H, Jansen BJ. Developing an online hate classifier for multiple social media platforms. Hum Cent Comput Inf Sci. 2020;10(1):1–34.

Dessì D, Recupero DR, Sack H. An assessment of deep learning models and word embeddings for toxicity detection within online textual comments. Electronics. 2021;10(7):779.

S. A. Özel, E. Saraç, S. Akdemir, and H. Aksu, Detection of cyberbullying on social media messages in Turkish, In: 2017 International Conference on Computer Science and Engineering (UBMK), 2017, pp. 366–370.

E. C. Ates, E. Bostanci, and M. S. Guzel, Comparative Performance of Machine Learning Algorithms in Cyberbullying Detection: Using Turkish Language Preprocessing Techniques, arXiv Prepr. arXiv2101.12718, 2021.

Van Hee C, et al. Automatic detection of cyberbullying in social media text. PLoS ONE. 2018;13(10):e0203794.

Haidar B, Chamoun M, Serhrouchni A. A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Adv Sci Technol Eng Syst J. 2017;2(6):275–84.

Gómez-Adorno H, Bel-Enguix G, Sierra G, Sánchez O, Quezada D. A machine learning approach for detecting aggressive tweets in Spanish, In: IberEval@ SEPLN. 2018. pp. 102–107.

X. Bai, F. Merenda, C. Zaghi, T. Caselli, and M. Nissim, RuG at GermEval: Detecting Offensive Speech in German Social Media, in 14th Conference on Natural Language Processing KONVENS 2018, 2018, p. 63.

B. Birkeneder, J. Mitrovic, J. Niemeier, L. Teubert, and S. Handschuh, upInf—Offensive Language Detection in German Tweets, In: Proceedings of the GermEval 2018 Workshop, 2018, pp. 71–78.

J. M. Schneider, R. Roller, P. Bourgonje, S. Hegele, and G. Rehm, Towards the Automatic Classification of Offensive Language and Related Phenomena in German Tweets, In: 14th Conference on Natural Language Processing KONVENS 2018, 2018, p. 95.

H. Margono, X. Yi, and G. K. Raikundalia, Mining Indonesian cyber bullying patterns in social networks, In: Proceedings of the Thirty-Seventh Australasian Computer Science Conference-Volume 147, 2014, pp. 115–124.

H. Nurrahmi and D. Nurjanah, Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility, In: 2018 International Conference on Information and Communications Technology (ICOIACT), 2018, pp. 543–548.

A. Bohra, D. Vijay, V. Singh, S. S. Akhtar, and M. Shrivastava, A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection, In: Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, 2018, pp. 36–41.

A. Roy, P. Kapil, K. Basak, and A. Ekbal, An ensemble approach for aggression identification in english and hindi text, In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), 2018, pp. 66–73.

Association for Computational Linguistics. https://www.aclweb.org/portal/content/deadline-extension-first-task-automatic-cyberbullying-detection-polish-language . Accessed 09 May 2019.

Ghosh R, Nowal S, Manju G. Social media cyberbullying detection using machine learning in bengali language. Int J Eng Res Technol. 2021. https://doi.org/10.1109/ICECE.2018.8636797 .

Talpur KR, Yuhaniz SS, Sjarif NNBA, Ali B. Cyberbullying detection in Roman Urdu language using lexicon based approach. J Crit Rev. 2020;7(16):834–48. https://doi.org/10.31838/jcr.07.16.109 .

J. Brownlee, Imbalanced Classification, December 23, 2019. https://machinelearningmastery.com . Accessed 10 May 2021.

Arif M. A systematic review of machine learning algorithms in cyberbullying detection: future directions and challenges. J Inf Secur Cybercrimes Res. 2021;4(1):1–26.

A. Dewani, M. Ali Memon, and S. Bhatti, Development of Computational Linguistic Resources for Automated Detection of Textual Cyberbullying Threats in Roman Urdu Language, 3C TIC. Cuad. Desarro. Apl. a las TIC, 101–121., p. 17, 2021.

Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos Solitons Fractals. 2020;140:110212.

M. Cliche, “BB_twtr at SemEval-2017 task 4: Twitter sentiment analysis with CNNs and LSTMs,” arXiv Prepr. arXiv1704.06125, 2017.

W. Zaremba, I. Sutskever, and O. Vinyals, Recurrent neural network regularization, arXiv Prepr. arXiv1409.2329, 2014.

Xu G, Meng Y, Qiu X, Yu Z, Wu X. Sentiment analysis of comment texts based on BiLSTM. Ieee Access. 2019;7:51522–32.

S. Minaee, E. Azimi, and A. Abdolrashidi, Deep-sentiment: Sentiment analysis using ensemble of cnn and bi-lstm models, arXiv Prepr. arXiv1904.04206, 2019.

Uysal AK, Gunal S. The impact of preprocessing on text classification. Inf Process Manag. 2014;50(1):104–12.

Vijayarani S, Ilamathi MJ, Nithya M. Preprocessing techniques for text mining-an overview. Int J Comput Sci Commun Networks. 2015;5(1):7–16.

Pycontractions 2.0.1. https://pypi.org/project/pycontractions/ . Accessed 17 Nov 2021.

API Documentation. https://www.tensorflow.org/api_docs . Accessed 21 Oct 2020.

NumPy. https://numpy.org/ . Accessed 18 Nov 2021.

Download references

Acknowledgements

We would like to thank Institute of Information and Communication Technology, Mehran University of Engineering & Technology, for providing resources and funding, necessary to accomplish this research work.

This research has been performed at Institute of Information and Communication Technology, Mehran University of Engineering and Technology, Pakistan and is fully funded under MUET funds for postgraduate students.

Author information

Authors and affiliations.

Institute of Information and Communication Technologies, Department of Software Engineering, Mehran University of Engineering & Technology, Jamshoro, Sindh, Pakistan

Amirita Dewani, Mohsin Ali Memon & Sania Bhatti

You can also search for this author in PubMed   Google Scholar

Contributions

Corresponding author is the main contributor of this work from conception, drafting, algorithms development and implementation to analysis of results. Other authors are research supervisors who provided valuable guidance for work and content improvement. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Amirita Dewani .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare that they have no competing or conflicts of interest to report regarding the present study.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Dewani, A., Memon, M.A. & Bhatti, S. Cyberbullying detection: advanced preprocessing techniques & deep learning architecture for Roman Urdu data. J Big Data 8 , 160 (2021). https://doi.org/10.1186/s40537-021-00550-7

Download citation

Received : 03 September 2021

Accepted : 10 December 2021

Published : 22 December 2021

DOI : https://doi.org/10.1186/s40537-021-00550-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Advanced preprocessing
• Deep learning
• Hate speech detection
• Cyberbullying

Speech On Bullying [1-2 Minutes]

Here is given an example of speech on bullying. This article can help you understand how to compose public speaking material on similar topics just like this one. Welcome To TheNextSkill.com . Let’s start.

Speech On Bullying For Students

Hello and good morning to all,

Before I deliver my speech I would like to wish you all the best wishes & I also want to thank you a lot for giving me a chance to share my views on this vital topic i.e bullying . Let me start with a story.

Our moral science book teaches us to treat others the way we want ourselves to be treated by others. It feels good when someone treats us with respect and love. In contrast, when someone shows lousy behaviour towards us, It hurts. One such behaviour is called bullying.

Bullying is aggressive behaviour towards one or more vulnerable persons. Those who do bullying are called bullies and they want to dominate the other person(s). Bullying can leave physical or emotional scars on the personality of the victim.

There are four types of bullying i.e. physical, psychological, verbal and cyberbullying. It can happen at any stage of life and any place in the world. Most notably, family members unknowingly bully an individual in various ways.

You might be surprised to know that a UNESCO report states that 32% of students are bullied at school. It is also noted that most boys suffer physical bullying while most girls suffer psychological bullying. No matter what gender the victim has, bullying is needed to be eliminated from society.

Like other countries in the world, the cases of bullying are increasing gradually in our country. Although the government has introduced many initiatives to fight this critical issue, the common man must also put some effort in this direction.

Maybe the victims are unable to take a stand for themselves. Others can help them by taking a stand on their behalf of them. In fact, the victims are one of us. Most important, parents must teach their children not to bully others as a lesson of morality.

To sum it up, it is our duty to prevent bullying in schools, colleges and other parts of the country. Not only bullying harms the victim but it also impacts the personality of bullies. Hence, it is also needed to improve the self-esteem of individuals so that they can develop a strong personality, not a loose one.

This is what I wanted to share with all of you. I hope it was helpful. Thank you for listening.

Other Speeches

Importance of time management speech [1,2,3 minutes], speech on ethics and etiquette [1,2,3 minutes], speech about mahatma gandhi jayanti 2023.

• 1 Minute Speech On Health Is Wealth
• 2 Minute Speech On Child Labour
• 1 Minute Speech On Child Labour
• Speech On Nature [ 1-2 minutes ]
• 2 Minute Speech on Importance Of Education
• 1 Minute Speech on Pollution
• 2 Minute Speech on Population Explosion

Related Posts

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• International edition
• Australia edition
• Europe edition

Trans man bullied further after X shares takedown notice over alleged hate speech

Watchdog said post was found to mock his gender identity and equated transgender identity with a psychiatric condition

• Get our morning and afternoon news emails , free app or daily news podcast

When the social media site X was told to take down cyber abuse by Australia’s internet regulator, it passed on the notice to the user who posted the alleged abuse – which led to its target facing more online bullying.

On 22 March, eSafety sent a notice to X, formerly Twitter, requesting that a tweet from 29 February posted by a prominent anti-trans user about an Australian trans man who is a leading LGBTQ+ health expert be taken down.

The regulator said it should be removed on the grounds it was in breach of Australia’s online safety act. It was determined to be cyber abuse directed at an Australian adult, given it was found to misgender him, mock his gender identity and equated transgender identity with a psychiatric condition.

Sign up for Guardian Australia’s free morning and afternoon email newsletters for your daily news roundup

The user who posted the tweet in question shared the letter on his account to his over 400,000 followers and said in the replies that he had received a copy of the letter from X.

The first tweet in the thread has had over 2m impressions, according to X’s metrics, with some of the nearly 1,000 replies targeting the person who complained to eSafety about the tweet.

On 30 March, X’s global government affairs account confirmed it was blocking access to the tweet in Australia and would fight the notice.

“X is withholding the post in Australia in compliance with the order but intends to file a legal challenge to the order to protect its user’s right to free speech.”

The same user later posted a screenshot of the original tweet from the takedown letter, and that screenshot has not been removed from access in Australia.

A spokesperson for the eSafety commissioner said the type of content falling under the online abuse scheme must be both intended to cause serious harm and menacing, harassing or offensive in all circumstances.

“If the material only meets one of these two criteria, for example, if the post is offensive but is found to not be intended to cause serious harm, it will not be considered adult cyber abuse under the act,” the spokesperson said.

“Importantly, the adult cyber abuse scheme does not regulate hurt feelings, purely reputational damage, bad online reviews, strong opinions or banter.”

No fine has yet been issued and eSafety had not received any legal notices from X related to the notice since announcing it was planning legal action, Guardian Australia understands.

Under the adult cyberbullying scheme that was passed into law under the former Coalition government in 2021 , platforms such as X can be fined close to $800,000 for failing to remove posts within 24 hours of the notice being received.

after newsletter promotion

Such formal notices are rarely issued, and not limited to X. In the past two financial years, eSafety reported it had issued five such formal notices to platforms.

In the 2022-2023 financial year alone, the regulator said it had received 2,644 complaints from adults regarding cyber abuse and made 601 informal requests to platforms seeking the removal of such material, with 466 of these cases resulting in material being removed.

David Sharaz, the partner of former Liberal staffer Brittany Higgins used the scheme in 2022 to get a tweet taken down targeting both he and Higgins, Guardian Australia reported last year . This occurred prior to Elon Musk purchasing the platform in November 2022.

While X has vowed to fight the notice to protect the user’s free speech and fight the removal, users last month noticed that using “cisgender” in some tweets results in the platform reducing the visibility of that tweet, claiming it is in violation of the platform’s hateful conduct policy.

Musk tweeted last year that cisgender, a 30-year-old term to describe someone whose gender corresponds to their sex assigned at birth, as “a heterosexual slur”, despite the term also being applicable to non-heterosexual people. “Cis” is derived from Latin, meaning “on this side of”, as in the opposite of trans.

Guardian Australia sought comment from X. The platform last year lodged a case in the federal court appealing a $610,000 eSafety fine notice issued to the company over how it tackles online child safety abuse. Esafety has also lodged a case in the court over X’s failure to pay the fine. The case has yet to be heard.

• Australia news
• Social media
• Freedom of speech
• Transgender

More on this story

Alexandria Ocasio-Cortez recounts horror of seeing herself in ‘deepfake porn’

Most viewed.

Mobile Menu Overlay

The White House 1600 Pennsylvania Ave NW Washington, DC 20500

Remarks by President   Biden and Prime Minister Kishida Fumio of Japan in Joint Press   Conference

Rose Garden

1:23 P.M. EDT

PRESIDENT BIDEN:  Please, have a seat.

It’s an honor to stand here today with the Prime Minister of Japan, President Kishi- — Prime Minister Kishida. 

When I became president, I said that the United States would rebuild the muscle of our demo- — democratic alliances and we’d stand shoulder to shoulder with our allies again, because our alliances are America’s greatest asset.  The relationship with Japan is powerful proof of that — that in investing in our alliance and raising our collective ambitions, we yield remarkable results.

Over the last three years, the partnership between Japan and the United States has been transformed into a truly global partnership.  And that’s thanks in no small part to the courageous leadership of Prime Minister Kishida.  And I mean that sincerely.

Together, our countries are taking significant steps to strengthen defense and security cooperation, we’re modernizing command and control structures, and we’re increasing the interoperability and planning of our militaries so they can work together in a seamless and effective way.

This is the most significant upgrade in our alliance since the end — since it was first established. 

I’m also pleased to announce that for the first time, Japan and the United States and Australia will create a networked system of air, missile, and defense architecture.  We’re also looking forward to standing up a trilateral military exercise with Japan and the United Kingdom. 

And our AUKUS defense partnership with Australia and the United Kingdom is exploring how Japan can join our work in the second pillar, which focuses on advanced capabilities, including AI, autonomous systems.  All told, that represents a new benchmark for our military cooperation across a range of capabilities.

On the economic front, our ties have never been more robust.  Japan is the top foreign investor in the United States.  Say that again: Japan is the top foreign investor in the United States.  And we, the United States, are the top foreign investor in Japan. Nearly 1 million Americans work in Japanese companies here in the United States.

And to name just one example, a few months ago, Toyota announced an $8 billion investment in a massive battery production facility in North Carolina, which will inc- — employ thousands of people.  The Prime Minister is going to travel to North Carolina tomorrow to visit that project. 

Don’t stay.  Don’t stay.  We need you back in Japan.  (Laughter.)  They’ll probably try to keep you.

We also affirmed the science and education ties between Japan and the United States.  Those tries — ties stretch up to the moon, where two Japanese astronauts will join future American missions, and one will become the first non-American ever to land on the moon. 

And they reach into the high schools and universities, as well, where the Mineta Ambassadors — Minetas Program exists, named for our dear friend Norm Mineta.  We’re going to invest in new student exchanges, help train the next generation of Japanese and American leaders.  

We also discussed developments in the Middle East, including our shared support for a ceasefire and a hostage deal and urgent efforts to deal with the humanitarian crisis that exists in Gaza. 

We also want to address the Iranian threat to launch a sign- — they — they’re threatening to launch a significant attack on Israel.  As I told Prime Minister Netanyahu, our commitment to Israel’s security against these threats from Iran and its proxies is ironclad.  Let me say it again: ironclad.  We’re going to do all we can to protect Israel’s security.

And, finally, I want to commend the Prime Minister himself.  He is a statesman.  Command — you know, the fact is that you condemned Putin’s invasion of — brutal invasion of Ukraine when it happened.  You pledged more than $12 billion in aid; prioritizing nuclear nonproliferation at the United Nations Security Council; standing strong with the United States as we stand up for freedom of navigation, including in the South China Sea and as we maintain peace and stability across the Taiwan Straits; and taking the brave step of mending ties with the Republic of Korea so we can all stand shoulder to shoulder together.

Tomorrow, we will both be joined by another good friend, President Marcos of the Philippines, for a trilateral summit — the first of its kind. 

And through it all, our commitment to the defense of Japan under Article 5, including the Sena- — excuse me — Senkaku Islands, is unwavering.

Mr. Prime Minister, through our partnership, we have strengthened this alliance.  We have expanded our work together.  We have raised our shared ambitions.  And now, the U.S.-Japan alliance is a beacon to the entire world. 

There’s no limit to what our countries can and our people can do together. 

So, thank you for your partnership, your leadership, and your friendship. 

And now, over to you, Mr. Prime Minister.

PRIME MINISTER KISHIDA:  Thank you, Joe.

(As interpreted.)  President Biden and I have met and talked countless times and confirmed our shared notion that we are at crucial crossroads and that Japan-U.S. partnership is immensely important. 

The international community stands at a historical turning point.  In order for Japan, the U.S., the Indo-Pacific region, and, for that matter, the whole world to enjoy peace, stability, and prosperity lasting into the future, we must resolutely defend and further solidify a free and open international order based on the rule of law.

And again, today, I told the President that now is the time to demonstrate the true values that Japan and the United States can offer as global partners, that we must together fulfill our responsibilities to create a world where human dignity is upheld and that Japan will always stand firm with the United States.

I explained that, based on our national security strategy, Japan is determined to strengthen our defense force through position of counterstrike capabilities, increase our defense budget and other initiatives, and was reassured by President Biden of his strong support for such efforts.

In such context, we confirmed again the urgency to further bolster the deterrence and response capabilities of our alliance and concurred on reinforcing our security and defense cooperation to increase interoperability between the U.S. forces and our self-defense forces, including the improvement of our respective command-and-control frameworks.

We will be discussing the specifics as we plan for the next Japan-U.S. two-plus-two.

The President and I went on to discuss various specific challenges faced by the international community. 

First, we confirmed that unilateral attempts to change status quo by force or coercion is absolutely unacceptable, wherever it may be, and that we will continue to respond resolutely against such action through cooperation with allies and likeminded nations.

From such perspective, we agreed that our two countries will continue to respond to challenges concerning China through close coordination.  At the same time, we confirmed the importance of continuing our dialogue with China and cooperating with China on common challenges.

We also underscored the importance of peace and stability in the Taiwan Straits and confirmed our position to encourage peaceful resolution of the Cross-Straits issue.

The situation in North Korea, including nuclear and missiles development, was brought up as well.  We welcomed the progress seen in many areas of cooperation based on the outcome of the Japan-U.S.-ROK summit last August and concurred to coordinate even more closely as we face serious concerns under the current state of affairs.

President Biden once again demonstrated his strong support towards the immediate resolution of the abduction issue.

We reaffirmed the importance of realizing a free and open Indo-Pacific based on the rule of law and concurred to maintain close collaboration through various opportunities, including the Japan-U.S.-Philippines summit, which is planned for tomorrow.

Regarding Russia’s aggression of Ukraine, based on

a recognition that Ukraine today may be East Asia tomorrow — taking the issue as our own problem for Japan, I expressed our resolution to continue with stringent sanctions against Russia and strong support for Ukraine.  And we concurred to maintain close partnership with likeminded countries.

On the situation in the Middle East, I expressed my respect for the efforts of President Biden towards the release of the hostages, improvement of the humanitarian situation, and for calming down the situation.  I then explained how Japan is continuing diplomatic efforts to improve the humanitarian situation and to realize a sustainable ceasefire

and agreed to continue close cooperation towards the improvement of the situation, the realization of a two-state solution, and the stabilization of the region.

Regarding the economy, we firstly concurred that for both of us to lead the global economic growth together, the promotion of investment in both directions is important.  I explained how Japanese businesses are making a significant contribution to the U.S. economy by the investment and the creation of jobs, to which President Biden agreed.

In order to maintain and strengthen the competitive edge in the area of advanced technologies and to respond appropriately to issues such as economic coercion, non-market policies and practices, and excess capacities and to overcome the vulnerability of the supply chains and to lead a sustainable and inclusive economic growth, we affirmed that the collaboration of Japan and the United States is indispensable. 

In addition, we concurred to advance our cooperation in the areas such as decarbonization, AI, and start-ups. 

There was a huge achievement also in the area of space.  In the first half of the 1960s, when I was in the United States, it was the dawn of space development in the United States.  I am one of all those who were so excited in the U.S. by the spectacular challenge in space. 

The implementing arrangement has been signed on this occasion and the provision of the lunar rover by Japan and the allocation of two astronaut flight opportunities to the lunar surface to Japan were confirmed.  Under the Artemis program, I welcome the lunar landing by a Japanese astronaut as the first non-U.S. astronaut. 

We also discussed the efforts towards a world without nuclear weapons.  We affirmed the realistic and practical endeavors of nuclear disarmament, including the issuance of the G7 Leaders’ Hiroshima Vision last year.  And I welcomed the participation of the United States in the FMCT Friends, which was launched by my initiative. 

Lastly, in order to further strengthen the people-to-people bond, which is the cornerstone of our unwavering Japan-U.S. relationship, we affirmed to further promote people-to-people exchanges. 

As the outcome of our meeting today, we will issue the joint statement titled “The Global Partners for the Future.”  This is the expression of the determination of Japan and the United States to maintain and strengthen a free and open international order based on the rule of law that underpins the peace, stability, and prosperity of the international community and states the guiding principles. 

With our partnership, we will defend the future of Japan and the United States, the Indo-Pacific, and the world and make that future all the more prosperous. 

PRESIDENT BIDEN:  Thank you.  Now we’ll take a few questions. 

Jordan Fabian of Bloomberg. 

Q    Thank you, Mr. President.  Last month, you predicted the Federal Reserve would cut interest rates thanks to falling inflation.  But today, data showed that inflation rose more than expected for the third straight month.  So, how concerned are you about the fight against inflation stalling?  And do you stand by your prediction for a rate cut?

PRESIDENT BIDEN:  Well, I do stand by my prediction that before the year is out there will a rate cut.  This may delay it a month or so.  I’m not sure of that.  I don’t — we don’t know what the Fed is going to do for certain. 

But, look, we have dramatically reduced inflation from 9 percent down to close to 3 percent.  We’re in a situation where we’re better situated than we were when we took office where we — inflation was skyrocketing.  And we have a plan to deal with it, whereas the opposition — my opposition talks about two things.  They just want to cut taxes for the wealthy and raise taxes on other people. 

And so, I think they’re — they have no plan.  Our plan is one I think is still sustainable. 

Q    Mr. Prime Minister, you said that the Nippon Steel acquisition of U.S. Steel is a private matter.  But I’m wondering: Did you discuss the matter today with President Biden?  And do you believe that politics are influencing President Biden’s decision to oppose the deal?

And I wouldn’t mind, Mr. President, if you answer that one too. 

PRIME MINISTER KISHIDA:  (As interpreted.)  On the issue that you have raised, we understand that discussions are underway between the parties.  We hope these discussions will unfold in directions that would be positive for both sides. 

Japan believes that appropriate procedures based on law is being implemented by the U.S. government.  Japan is the largest investor to the United States.  Japanese businesses employ close to 1 million workers in the United States.  And investment from Japan to the U.S. can only increase upwards in the months and years to come. 

And we wish to cement this win-win relationship.  Thank you. 

PRESIDENT BIDEN:  I stand by my commitment to American workers.  I ca- — a man of my word, I’m going to keep it.  And with regard to that, I stand by our commitment to our alliance.  This is exactly what we’re doing — a strong alliance as well. 

Q    Nakakuki of Kyodo News.  My question is to both Prime Minister Kishida and President Biden.  At the summit, you confirmed your strong objections against unilateral attempts to change the status quo by force or coercion by China and agreed on reinforcing response capabilities. 

Under current circumstances, should Japan and the United States bolster defense capabilities?  China may become more preoccupied in military expansion and intensify its coercive behavior.  That is the risk of (inaudible).  In order to avoid divide and expa- — avoid the divide, how should Japan and U.S. respond?

PRIME MINISTER KISHIDA:  (As interpreted.)  Let me then take that question first.  At this summit, we confirmed that the United States and Japan will resolutely defend and bolster a free and open international order based on the rule of law and that Japan and the United States, as global partners, shall work together for that purpose.

On challenges concerning China, including the point you raised on objecting to unilateral attempts to change status quo by force or coercion, we concurred that Japan and the United States as global partners shall work in close coordination. 

And also, as I said previously, we will continue our dialogue with China and we will cooperate with China in tackling common challenges.  And the President and I confirmed the importance of such dialogue as well. 

Based on the solid trust with our ally, the United States, we will continue to call on China to fulfill its responsibilities as a major power. 

Japan’s policy, which I have consistently embraced, is to comprehensively promote the mutual strategic relationship we have with China and establish a constructive and stable Japan-China relationship through efforts by both sides.  That has been my consistent position that I have upheld.  We will continue to seek close communication with China at all levels. 

That’s it for me.

PRESIDENT BIDEN:  You know, first of all, we keep improving our lines of communications with one another — and that’s the United States and China.  We — I’ve met — I’ve recently spoken at length with President Xi.  And we’ve agreed that we would, number one, have personal contact with one another whenever we want to discuss to anything so there’d be no — nothing lipped — nothing slips between, as they say — between the cup and the lip, so we know exactly what the other team is thinking.  Number one. 

And so, we had a long discussion last — now almost — I guess almost two weeks ago now.  And — the best way to reduce the chances of miscalculation and misunderstanding.  That’s number one. 

Number two, in our alliance we have with Japan — is a purely defensive in nature.  It’s a defensive alliance.  And the things we discussed today improve our cooperation and are — and are purely about defense and readiness.  It’s not aimed at any one nation or a threat to the region.  And it — it doesn’t have anything to do with conflict.  And so, this is about restoring stability in the region.  And I think we have a chance of doing that. 

Okay.  Third que- — the next question.  Who — who do I call on next?  Hang on a second.  I got my list here.  Hang on.  I apologize. 

Aurelia of AFP.

Q    Thank you.  My first question would go to both of you, Mr. President and Mr. Prime Minister.  Is there a path for Japan to become a full member of AUKUS? 

And I would have a second question for you, Mr. President.  You’re now saying that Benjamin Netanyahu is making a mistake in Gaza.  What are you willing to do to make him change his strategy?  And would you consider conditioning military aid to Israel?  Thank you.

PRIME MINISTER KISHIDA:  (As interpreted.)  Thank you.  Your question about AUKUS, I will respond.  Our country — we want to contribute to the peace and stability of the region.  And therefore, we have consistently supported AUKUS. 

Having said that, the participants of AUKUS — U.S., UK, Australia — with such countries’ bi- — bilateral relationship or in multilateral occasions, we have established various relationships.  But for Japan, to have a direct cooperation with AUKUS, nothing has been decided at this moment.

Going forward with U.S., UK, or with Australia — with such countries, in bilateral or multilateral frameworks, we will continue our cooperation so that they will continue to be considered.

At the moment, about the relationship between Japan and AUKUS, that’s it.

PRESIDENT BIDEN:  With regard to my discussions with Bibi Netanyahu — Prime Minister Netanyahu, as well as our relationship with Israel, I have been very blunt and straightforward with the Prime Minister, as well as his War Cabinet, as well as the Cabinet. 

And the fact of the matter is that Bibi and I had a long discussion.  He agreed to do several things that related to, number one, getting more aid — both food and medicine — into Gaza and reducing significantly the attempts — the civilian casualties in any action taken in the region. 

And thus far — and we — and it’s tied to the hostages.  There are a number of hostages that are being held by a — by the — Hamas.  And just yesterday, we were meeting with the Vice President and our National Security Advisor before that, and they — and there are American hostages as well.  And they know how committed we are — the whole team — to getting their loved ones home, and we’re not going to stop until we do.

The new proposal on the table — Bill Burns led the effort to — for us, and we’re grateful for his work — there’s a now — up to Hamas.  They need to move on the proposal that’s been made.  And as I said, it would get these hostages home where they belong but also bring back a six-week cea- — ceasefire that we need now. 

And the fact is that we’re — they’re getting in somewhere, in the last three days, over a hundred trucks.  It’s not enough.  But it needs to be — be more, and there’s one more opening that has to take place in the north. 

So, we’ll see what he does in terms of meeting the commitments he made to me. 

Okay —

MS. JEAN-PIERRE:  This will be the last reporter. 

PRIME MINISTER KISHIDA:  (As interpreted.)  Mr. Shimizu, please.

Q    (As interpreted.)  Thank you.  Shimizu of NHK.  I ask the question to both of you. 

As Prime Minister Kishida mentioned, the abduction issue of North Korea, I believe, was discussed.  Prime Minister, you have expressed your wish to have a direct engagement with Kim Jong Un.  But they say that abduction is already result, which means that they are refusing.  During the meeting, what did you tell President Biden about the outlook of a summit?  And what engagement did you ask President Biden?

President Biden, my question: What did you hear from Prime Minister Kishida?  And what is your observation and feeling, your President, with the nuclear missile issues?  What is your position?  Do you support the summit between Japan and North Korea?  Thank you. 

PRIME MINISTER KISHIDA:  (As interpreted.)  First of all, if I may start, regarding my summit meeting with President Biden about North Korea, including the missile and nuclear issues we have discussed, and regarding the increasingly worrying situation, we have agreed to continue a close coordination. 

And on top of that, we concurred that the window of a discussion with North Korea is open.  And we discussed that Japan, U.S. — Japan, U.S., and ROK will continue to work closely together. 

I also asked for the continued understanding and cooperation for the immediate resolution of the abduction issue.  And President Biden once again gave myself a very strong assurance regarding the recent announcement by North Korea.

I will refrain from commenting on each and every announcement by North Korea.  But as I have been mentioning repeatedly, based on the perspective that the establishment of a meaningful relationship between Japan and North Korea is in the interest of both Japan and North Korea and that it could be hugely beneficial to the peace and stability of the region, my policy is to aim for a summit meeting with North Korea to resolve various issues and will advise high-level consultation directly under my instruction.  And that remains unchanged.  That is my response.

PRESIDENT BIDEN:  We did discuss this issue.  We both agreed the DPRK must — must also address the serious human rights and humanitarian concerns of the international community, including the immediate resolution of the abduction issue.

But, you know, the Prime Minister has just spoken to the potential of what his plans may mean.  But welcome — I welcome the opportunity — we welcome the opportunity of our allies to initiate dialogue with the Democratic Republic of Korea.

As I’ve said many times, we’re open to dialogue ourselves at any time wi- — but without preconditions from the DPRK.  So, I have faith in the — in the — Japan.  I have faith in the Prime Minister.  And I think his seeking a dialogue with them is a good thing.  It’s a positive thing. 

Q    Sir, on the issue of abortion — 

Q    What will you do if Israel invades Rafah?

Q    On the issue of abortion, sir, what do you say to the people of Arizona?

MS. JEAN-PIERRE:  This concludes the press conference.  Thanks, everybody.

Q    Mr. President, are the American hostages alive?

(Cross-talk.)

PRESIDENT BIDEN:  Why doesn’t everybody holler at once?

Q    I’ll ask you briefly.  On the issue of abortion, sir, respectfully, what do you say to the people of Arizona right now who are witnessing a law go in place that dates back to the Civil War era? 

PRESIDENT BIDEN:  Elect me.  I’m in the 20 — it’s the 20th century — 21st century, not back then.  They weren’t even a state.  I find —

Q    Sir, how does the —

PRESIDENT BIDEN:  I —

Q    Mr. President, how does the war in Ukraine come to an end?

PRESIDENT BIDEN:  Thank you.  Thank you all very much.  Thank you.

Q    Can you elaborate on what mistake Netanyahu is making, sir? MS. JEAN-PIERRE:  Thanks, everybody.

Q    How does the war in Ukraine come to an end, sir?

MS. JEAN-PIERRE:  This concludes the press conference.

(Cross-talk.) 

PRESIDENT BIDEN:  By the House — by the Hou- — the war in Ukraine comes to an end by the House Leader allowing a vote.  There’s overwhelming support for Ukraine among the majority of Democrats and Republicans.  There should be a vote now.

Q    Are the American hostages alive?

Q    Will you reconsider the LNG export ban, sir?

PRESIDENT BIDEN:  There is no ban to Japan. 

1:53 P.M. EDT

Stay Connected

We'll be in touch with the latest information on how President Biden and his administration are working for the American people, as well as ways you can get involved and help our country build back better.

Opt in to send and receive text messages from President Biden.