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  • Published: 29 April 2024

Problematic social media use mediates the effect of cyberbullying victimisation on psychosomatic complaints in adolescents

  • Prince Peprah 1 , 2 ,
  • Michael Safo Oduro 3 ,
  • Godfred Atta-Osei 4 ,
  • Isaac Yeboah Addo 5 , 6 ,
  • Anthony Kwame Morgan 7 &
  • Razak M. Gyasi 8 , 9  

Scientific Reports volume  14 , Article number:  9773 ( 2024 ) Cite this article

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  • Public health
  • Risk factors

Adolescent psychosomatic complaints remain a public health issue globally. Studies suggest that cyberbullying victimisation, particularly on social media, could heighten the risk of psychosomatic complaints. However, the mechanisms underlying the associations between cyberbullying victimisation and psychosomatic complaints remain unclear. This cross-cultural study examines the mediating effect of problematic social media use (PSMU) on the association between cyberbullying victimisation and psychosomatic complaints among adolescents in high income countries. We analysed data on adolescents aged 11–16.5 years (weighted N = 142,298) in 35 countries participating in the 2018 Health Behaviour in School-aged Children (HBSC) study. Path analysis using bootstrapping technique tested the hypothesised mediating role of PSMU. Results from the sequential binary mixed effects logit models showed that adolescents who were victims of cyberbullying were 2.39 times significantly more likely to report psychosomatic complaints than those who never experienced cyberbullying (AOR = 2.39; 95%CI = 2.29, 2.49). PSMU partially mediated the association between cyberbullying victimisation and psychosomatic complaints accounting for 12% ( \(\beta\)  = 0.01162, 95%CI = 0.0110, 0.0120) of the total effect. Additional analysis revealed a moderation effect of PSMU on the association between cyberbullying victimisation and psychosomatic complaints. Our findings suggest that while cyberbullying victimisation substantially influences psychosomatic complaints, the association is partially explained by PSMU. Policy and public health interventions for cyberbullying-related psychosomatic complaints in adolescents should target safe social media use.

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A nationwide study on time spent on social media and self-harm among adolescents, introduction.

Adolescence is noted to be a critical developmental stage, with many problems, including loneliness 1 , poor friendships, an adverse class climate, school pressure 2 , suicidal ideation and attempts, and psychosomatic complaints 3 . Psychosomatic complaint is a combination of physical ailments (i.e., headaches, stomach aches, fatigue, and muscle pain) caused or exacerbated by psychological factors such as stress, irritability, anxiety, or emotional distress 4 , 5 . Psychosomatic complaints are common among adolescents, and recent estimates indicate that the global prevalence of psychosomatic complaints ranges between 10 and 50% 6 . Also, an increase in self-reported psychosomatic complaints and related mental health complaints have been reported in adolescents from high-income countries 7 , 8 . The high prevalence of psychosomatic complaints is of concern as psychosomatic complaints have severe implications for multiple detrimental health outcomes, healthcare expenditure, and quality of life of young people 9 . Thus, it is of utmost importance to identify the proximate risk factors for psychosomatic complaints among young people to aid in developing targeted interventions to reduce the incidence of psychosomatic complaints, mainly in high-income countries.

While extant research has identified risk factors for psychosomatic complaints, including malnutrition, low physical activity, and poor parental guidance 10 , 11 , 12 , one understudied but potentially important risk factor is cyberbullying victimisation. Cyberbullying victimisation is an internet-based aggressive and intentional act of continually threatening, harassing, or embarrassing individuals who cannot defend themselves using electronic contact forms such as emails, text messages, images, and videos 13 , 14 . Indeed, being typical of interpersonal interactions, cyberbullying victimisation has shown a rising trend, particularly during adolescence 15 . International literature has shown the prevalence of cyberbullying victimisation to be between 12 and 72% among young people 14 , 16 . It may be hypothesised that cyberbullying victimisation potentially increases the risk of psychosomatic complaints through factors such as problematic social media use (PSMU) 17 , 18 . However, studies are needed to identify whether and the extent to which such factors mediate the potential association of cyberbullying victimisation with psychosomatic complaints among young people.

Given this background, the present study aimed to investigate the association between cyberbullying victmisation and psychosomatic complaints in 142,298 young people aged 11–16.5 years from 35 high-income countries. A further aim was to quantify how PSMU mediates the association between cyberbullying victimisation and psychosomatic complaints.

Cyberbullying victimisation and adolescents’ psychosomatic complaints

Research has consistently shown that cyberbullying victimisation significantly impacts adolescents’ mental health 19 . For example, Kowalski and Limber 20 found that cyberbullying victimisation is associated with increased levels of depression, anxiety, and social anxiety, as well as psychosomatic complaints, such as fatigue and muscle tension. Further, studies have shown that cyberbullying victimisation and perpetration can lead to a variety of physical, social, and mental health issues, including substance abuse and suicidal thoughts and attempts 21 , 22 , 23 , 24 . Furthermore, cyberbullying victimisation is strongly associated with suicidal thoughts and attempts, regardless of demographic factors like gender or age 21 , 25 . These findings underscore the urgent need for interventions that address the mental health consequences of cyberbullying, particularly for adolescents, who are most vulnerable to its harmful effects. The findings also suggest that cyberbullying might be a potential underlying predictor of higher psychosomatic disorders among adolescents. This present study, therefore, hypothesises that H1: there is a statistically significant association between cyberbullying victimisation (X) and psychosomatic complaints (Y) (total effect).

The role of adolescents’ PSMU

Problematic Social Media Use (PSMU), a subtype of problematic internet use, refers to the uncontrolled, compulsive or excessive engagement with social media platforms such as Facebook and Twitter, characterised by addictive behaviours like mood alteration, withdrawal symptoms, and interpersonal conflicts. This pattern of social media usage can result in functional impairments and adverse outcomes 26 . Scholars and professionals have shown great concern about the length of time adolescents spend on social media. Studies have observed that (early) adolescence could be a crucial and sensitive developmental stage in which adolescent users might be unable to avoid the harmful impacts of social media use 27 . According to current research, PSMU may increase adolescents’ exposure to cyberbullying victimisation, which can have severe consequences for their mental health 28 , 29 , 30 . Similarly, an association between PSMU and physical/somatic problems, as well as somatic disorders, has been established in many studies 31 , 32 . Hanprathet et al. 33 demonstrated the negative impact of problematic Facebook use on general health, including somatic symptoms, anxiety, insomnia, depression, and social dysfunction. According to Cerutti et al. 34 , adolescents with problematic social media usage have more somatic symptoms, such as stomach pain, headaches, sore muscles, and poor energy, than their counterparts. Hence, inadequate sleep may be associated with PSMU, harming both perceived physical and mental health 35 , 36 . Again, supporting the above evidence, the relationship between PSMU, well-being, and psychological issues have been highlighted in meta-analytic research and systematic reviews 27 , 31 , 37 , 38 . Thus, this study proposes the following hypothesis: H2: there is a specific indirect effect of cyberbullying victimisation (X) on psychosomatic complaints (Y) through PSMU (M1) (indirect effect a 1 b 1 ).

Study, sample, and procedures

This study used data from the 2018 Health Behaviour in School-aged Children (HBSC) survey conducted in 35 countries and regions across Europe and Canada during the 2017–2018 academic year 39 . The HBSC research team/network is an international alliance of researchers collaborating on a cross-national survey of school students. The HBSC collects data every four years on 11-, 13- and 15- year-old adolescent boys’ and girls’ health and well-being, social environments, and health behaviours. The sampling procedure for the 2018 survey followed international guidelines 40 , 41 . A systematic sampling method was used to identify schools in each region from the complete list of both public and private schools. Participants were recruited through a cluster sampling approach, using the school class as the primary sampling unit 42 . Some countries oversampled subpopulations (e.g., by geography and ethnicity), and standardised weights were created to ensure representativeness of the population of 11, 13, and 15 years 43 . Questionnaires were translated based on a standard procedure to allow comparability between the participating countries. Our analysis used data from 35 countries and regions with complete data on cyberbullying victimisation, PSMU, and psychosomatic complaints. The study complies with ethical standards in each country and follows ethical guidelines for research and data protection from the World Health Organisation and the Organisation for Economic Co-operation and Development. Depending on the country, active or passive consent was sought from parents or legal guardians and students which was checked by teachers to participate in the study. The survey was conducted anonymously and participation in the study was voluntary for schools and students. Schools, children and adolescents could refuse to participate or withdraw their consent until the day of the survey. Moreover, all participating students were free to cease filling out the questionnaire at any moment, or to answer only selected questions. More detailed information on the methodology of the HBSC study including ethics and data protection can be found elsewhere 44 , 45 .

Outcome variable: psychosomatic complaints

Psychosomatic complaints was assessed by one collective item asking students how often they had experienced the following complaints over the past six months: headache, stomach aches, feeling low, irritability or bad mood, feeling nervous, dizziness, abdominal pain, sleep difficulty, and backache. Response options included: about every day, more than once a week, about every week, about every month, and rarely or never. This scale has sufficient test–retest reliability and validity 46 , good internal consistency (Cronbach’s a = 0.82) 47 , and has been applied in several multiple country analyses 48 , 49 . The scale is predictive of emotional problems and suicidal ideation in adolescents 50 , 51 . For our analysis, the scale was dichotomised with two or more complaints several times a week or daily coded as having psychosomatic complaints 47 , 49 .

Exposure variable: Cyberbullying victimisation

Cyberbullying victimisation is the exposure variable in this study. Thus, the exposure variable pertains to only being a victim of cyberbullying and does not include perpetration of cyberbullying. Students were first asked to read and understand a short definition of cyberbullying victimisation. They were then asked how often they were bullied over the past two months (e.g., someone sending mean instant messages, emails, or text messages about you; wall postings; creating a website making fun of you; posting unflattering or inappropriate pictures of you online without your permission or sharing them with others). Responses included: “ I have not   been  cyberbullied”, “once or twice”, “two or three times a month”, “about once a week”, and “several times a week”. These were dichotomised into “never" or “once or more". This measure of bullying victimisation has been validated across multiple cultural settings 43 , 52 , 53 , 54 .

Mediating variable

Problematic social media use (PSMU) was assessed with the Social Media Disorder Scale (Cronbach’s a = 0.89) 55 . The scale contains nine dichotomous (yes/no) items describing addiction-like symptoms, including preoccupation with social media, dissatisfaction about lack of time for social media, feeling bad when not using social media, trying but failing to spend less time using social media, neglecting other duties to use social media, frequent arguments over social media, lying to parents or friends about social media use, using social media to escape from negative feelings, and having a severe conflict with family over social media use. In this study, the endorsement of six or more items indicated PSMU as evidence suggests that a threshold of six or more is an indicative of PSMU 54 , 56 . This scale has been used across cultural contexts 43 , 52 , 54 .

Informed by previous studies 43 , 54 , 57 , the analysis controlled for theoretically relevant confounders, including sex (male/female) and age. Family affluence/socio-economic class was assessed using the Relative Family Affluence Scale, a validated six-item measure of material assets in the home, such as the number of vehicles, bedroom sharing, computer ownership, bathrooms at home, dishwashers at home, and family vacations) 56 , 58 . Finally, parental and peer support were measured using an eight item-measure 59 . Responses were recorded on a 7-point Likert scale (ranging from 0 indicating very strongly disagree to 6 indicating very strongly agree).

Statistical analysis

Region-specific descriptive statistics were calculated to describe the sample. Next, Pearson’s Chi-squared association test with Yates’ continuity correction was performed to examine plausible associations between psychosomatic complaints and other categorical study variables. Also, to account for the regional clustering or unobserved heterogeneity observed in the analytic sample, sequential mixed effect binary logit models with the inclusion of a random intercept were fitted to further examine the associations between psychosomatic complaints and cyberbullying victimisation as well as other considered covariates. Furthermore, a parallel mediator model was fitted to evaluate the specified hypothesis and understand the potential mechanism linking cyberbullying victimisation and psychosomatic complaints. More specifically, cyberbullying victimisation (X) was modelled to directly influence psychosomatic complaints (Y) and indirectly via PSMU (M). Since core variables were binary, paths could be estimated with a sequence of three logit equations: 60 , 61

where, \({i}_{1}\) , \({i}_{2}\) , and \({i}_{3}\) represent the intercept in the respective equations. The path coefficient, c, in Eq. ( 1 ) represents the total effect of predictor X on outcome Y . In Eq. ( 2 ), the path coefficient a denotes the effect of predictor X on the mediator M . Also, the c' parameter in Eq. ( 3 ) represents the direct effect of the predictor X on the response Y , adjusting for the mediator M . Lastly, the path coefficient b coefficient in Eq. ( 3 ) represents the indirect effect of the mediator M on the outcome Y , when adjusting for the predictor X . These logit models provide effect estimates on the log-odds scale, and thus can be transformed into odds ratios. Each model was adjusted for the potential confounding variables.

All statistical analyses were performed using R Software (v4.1.2; R Core Team 2021) with \(\alpha\)  =  0.05 as the significance level. More specifically, the package “mediation” in R 62 was used for the mediation analysis to estimate direct, indirect, and total effects. Inference is based on a non-parametric, 95% bias-corrected and accelerated (BCa) bootstrapped confidence interval 63 , 64 . Bootstrapping for indirect effects was set at 1000 samples, and once the 95% bootstrapped CI of the mediation effects did not include zero (0), it was deemed statistically significant. We also conducted further analysis by including an interaction between cyberbullying victimisation and PSMU to obtain insights analogous to the mediation model.

Ethics approval and consent to participate

The research was exclusively based on data sourced from the World Bank, which adheres to rigorous ethical standards in its data collection processes. Therefore, no separate ethical approval was sought or deemed necessary. Ethical approval was not required for this study since the data used for this study are secondary data. Necessary permissions and survey data were obtained from the World Bank. The World Bank data collection process upheld ethical standards and relevant guidelines in the research process including informed consent from all subjects and/or their legal guardian(s).

Preliminary analyses

The final analytic sample comprised complete information on 142,298 adolescents from 35 high-income countries (Table 1 ). The median age of the sample was 13.6 years. Most participants resided in Wales (6.26%) and the Czech Republic (6.16%). Notably, the prevalence of cyberbullying victimisation was 26.2%, and the majority (53%) were females. As observed in Table 2 , 84.6% of the participants self-reported high levels of psychosomatic complaints. Furthermore, among the participants who experienced PSMU, about 81.16% reported high levels of psychosomatic complaints. About 84.47% of the participants indicated receiving parental and peer support (see Table 2 ).

Main analyses

Results from the sequential binary mixed effects logit model are shown in Table 3 . In the first step, we included only cyberbullying victimisation in the model. We found that cyberbullying victims were 2.430 times more likely to report psychosomatic complaints than those who were not cyberbullied (OR = 2.430; 95%CI = 2.330, 2.530). The second step included sex, PSMU, parental and peer support, and family affluence as covariates. We found that cyber bullying victims were 2.390 times significantly more likely to report psychosomatic complaints than those who never experienced cyberbullying (AOR = 2.390; 95%CI = 2.29, 2.49). Additionally, the third model, which is an additional analysis involved the inclusion of an interaction between and cyberbullying victimisation and PSMU. The results showed that PSMU moderates the association between cyberbullying victimisation and psychosomatic complaints. Adolescents who were cyberbullied but did not report PSMU had reduced odds of psychosomatic complaints compared to those with PSMU (AOR = 1.220; 95%CI = 1.110–1.350). Furthermore, a caterpillar plot of empirical Bayes residuals of the models for the random intercept, region/country is obtained and shown in Fig.  1 . This represents individual effects for each country and offers additional insights into the extent of psychosomatic complaints heterogeneity across different countries. The plots visually demonstrates that regional variation for psychosomatic complaints does exist.

figure 1

A caterpillar plot of empirical Bayes residuals of the models for the random intercept, region/country. This represents individual effects for each region/country. Region or country abbreviations in the figure are as follows: [AL] Albania, [AZ] Azerbaijan, [AT] Austria, [BE-VLG] Vlaamse Gewest (Belgium), [BE-WAL] Wallone, Région (Belgium), [CA] Canada, [CZ] Czech Republic, [DE] Germany, [EE] Estonia, [CA] Canada, [ES] Spain, [FR] France, [GB-ENG] England, [GB-SCT] Scotland, [GB-WLS] Wales, [GE] Georgia, [GR] Greece, [HR] Croatia, [HU] Hungary, [IE] Ireland, [IL] Israel, [IS] Iceland, [IT] Italy, [KZ] Kazakhstan, [LT] Lithuania, [LU] Luxembourg, [MD] Moldova, [MT] Malta, [NL] Netherlands, [PT] Portugal, [RO] Romania, [RS] Serbia, [RU] Russia, [SE] Sweden, [SI] Slovenia, [TR] Turkey, [LU] Luxembourg and [UA] Ukraine.

Figure  2 shows the adjusted parallel mediation results. The effect of cyberbullying victimisation on psychosomatic complaints was significantly mediated by PSMU. The paths from cyberbullying victimisation to PSMU (a: \(\beta\) =0.648, p < 0.001), PSMU to psychosomatic complaints (b: \(\beta\) =0.889, p < 0.001), and that of cyberbullying victimisation to 0.8069 (c′: \(\beta\) =0.051, p < 0.001) were also statistically significant.

figure 2

A parallel mediation model of the influence of PSMU on the association between Cyberbullying Victimisation and Psychosomatic Complaints. a = path coefficient of the effect of exposure on the mediator. b = path coefficient of the effect of the mediator on the outcome. c’ = path coefficient of the direct effect of the exposure on outcome. CV, cyberbullying victimisation. PC, psychosomatic complaints.

Bootstrapping test of mediating effects

The total, direct, and indirect effects of the mediation model based on nonparametric bootstrap are presented in Table 4 . We observe that the estimated CI did not include zero (0) for any effects. This observation suggests a statistically significant indirect effect of cyberbullying victimisation on psychosomatic complaints via PSMU ( \(\beta\)  = 0.01162, 95%CI = 0.0110, 0.0120), yielding 12% of the total effect.

Key findings

This cross-cultural study examined the direct and indirect associations of cyberbullying victimisation with psychosomatic complaints via PSMU among adolescents. The results showed that cyberbullying victimisation independently influenced the experience of psychosomatic complaints. Specifically, adolescents who were victims of cyberbullying were more than two times more likely to report psychosomatic complaints. Crucially, our mediation analyses indicated that PSMU explain approximately 12% of the association between cyberbullying victimisation and psychosomatic complaints. In a further analysis, PSMU moderated the association between cyberbullying victimisation and psychosomatic complaints. This study is the first to examine the direct and indirect associations between cyberbullying victimisation and psychosomatic complaints through PSMU in adolescents across multiple high-income countries.

Interpretation of the findings

Our results confirmed the first hypothesis that there is a statistically significant direct association between cyberbullying victimisation and psychosomatic complaints. Thus, we found that cyberbullying independently directly affected the adolescents' experience of psychosomatic complaints. Previous studies have mainly focused on the direct effect of traditional face-to-face bullying on psychosomatic complaints 20 , 65 or compared the impact of traditional face-to-face bullying to cyberbullying concerning mental health 19 , 66 , 67 , 68 , 69 . A systematic review of traditional bullying and cyberbullying victimisation offers a comprehensive synthesis of the consequences of cyberbullying on adolescent health 19 . Another review suggested that cyberbullying threatened adolescents’ well-being and underscored many studies that have demonstrated effective relationships between adolescents’ involvement in cyberbullying and adverse health outcomes 70 . Other population-based cross-sectional studies have similarly shown that victims of cyberbullying experience significant psychological distress and feelings of isolation, which can further exacerbate their physical and mental health challenges 22 , 71 , 72 . The present study builds on the previously published literature by highlighting the effect of cyberbullying victimisation on adolescent psychosomatic complaints and the extent to which the association is mediated by PSMU.

Consistent with the second hypothesis, we found that PSMU mediated about 12% of the association between cyberbullying victimisation and psychosomatic complaints in this sample. While studies on the mediational role of PSMU in the relationship between cyberbullying victimisation and psychosomatic complaints are limited, evidence shows significant interplay among PSMU, cyberbullying victimisation, and psychosomatic complaints. For example, a study of over 58,000 young people in Italy found that PSMU was associated with increased levels of multiple somatic and psychological symptoms, such as anxiety and depression. 73 Another study of 1707 adolescents in Sweden found that cyberbullying victimisation was associated with increased depressive symptoms and the lowest level of subjective well-being 74 .

Other possible mediators of the cyberbullying victimisation-psychosomatic complaints association may include low self-esteem, negative body image, emotion regulation difficulties, social support, and personality traits such as neuroticism and impulsivity 20 , 67 , 72 , 75 , 76 . For example, Schneider et al. 75 have shown that emotional distress could increase psychosomatic symptoms such as headaches, stomach aches, and muscle tension. In addition, social isolation can lead to social withdrawal and a decreased sense of belonging 78 , 79 . Therefore, it is essential to explore these variables further and develop effective interventions and prevention strategies to address these interrelated factors and reduce their negative impact on adolescent health and well-being.

In a further analysis, the results show that PSMU does not only mediate but also moderate the association between cyberbullying victimisation and psychosomatic complaints among adolescents. Specifically, cyberbullied adolescents with no report of PSMU had reduced likelihoods of experiencing psychosomatic complaints compared to those with PSMU. This result is interesting and could be due to several factors. First, individuals with PSMU may already be experiencing heightened levels of psychological distress due to their excessive social media use, making them more vulnerable to the negative effects of cyberbullying 80 , 81 , 82 . For instance, excessive time spent on social media, particularly in activities such as comparing oneself to others or seeking validation through likes and comments, has been linked to increased psychological distress 83 , 84 . Conversely, the finding that cyberbullied adolescents without PSMU had reduced likelihoods of experiencing psychosomatic complaints compared to those with PSMU suggests a protective effect of lower social media use. Adolescents who are not excessively engaged with social media may have fewer opportunities for exposure to cyberbullying and may also have healthier coping strategies in place to deal with any instances of online victimisation 43 , 85 , 86 .

The results suggest that professionals in the fields of education, counselling, and healthcare should prioritise addressing the issue of cyberbullying victimisation when assessing the physical and psychological health of adolescents. Evidently, adolescents who experience cyberbullying require support. Thus, proactive measures are essential, and support could be provided by multiple professional communities that serve adolescents and young people in society, such as educational, behavioural health, and medical professionals. Sensitive inquiry regarding cyberbullying experiences is necessary when addressing adolescent health issues such as depression, substance use, suicidal ideation, and somatic concerns 19 . Our findings underscore the need for comprehensive, school-based programs focused on cyberbullying victimisation prevention and intervention.

Strengths and limitations

The study's main strength lies in the use of a large sample size representing multiple countries in high income countries. This large sample size improved the representativeness and veracity of our findings. The complex research approach helps advance our understanding of the interrelationships between cyberbullying victimisation, PSMU, and psychosomatic complaints among adolescents. However, the study has its limitations. First, the cross-sectional design does not allow directionality and causal inferences. Second, retrospective self-reporting for the critical study variables could lead to recall and social desirability biases. Third, the presence of residual and unobserved confounders, despite adjusting for some covariates, can be considered a limitation of this study. Further research is needed to confirm these findings and better understand how PSMU mediates the relationship between cyberbullying victimisation and psychosomatic complaints.

Conclusions

This study has provided essential insights into the interrelationships between cyberbullying victimisation, PSMU, and psychosomatic complaints among adolescents in high income countries. The findings suggest that cyberbullying is directly associated with psychosomatic complaints and that PSMU significantly and partially mediates this association. This study also highlights the importance of addressing cyberbullying victimisation and its negative impact on adolescent health and emphasises the need to address PSMU. Overall, the study underscores the importance of promoting healthy online behaviour and providing appropriate support for adolescents who experience cyberbullying victimisation. Further studies will benefit from longitudinal data to confirm our findings.

Data availability

The data that support the findings of this study are available from the World Bank, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Data are, however, available from the corresponding author ([email protected]) upon reasonable request and with permission of the World Bank.

Lyyra, N., Välimaa, R. & Tynjälä, J. Loneliness and subjective health complaints among school-aged children. Scand. J. Public Health 46 (20), 87–93. https://doi.org/10.1177/1403494817743901 (2018).

Article   PubMed   Google Scholar  

Ottova, V. et al. The role of individual-and macro-level social determinants on young adolescents’ psychosomatic complaints. J. Early. 32 (1), 126–158. https://doi.org/10.1177/0272431611419510 (2012).

Article   Google Scholar  

Heinz, A., Catunda, C., van Duin, C. & Willems, H. Suicide prevention: Using the number of health complaints as an indirect alternative for screening suicidal adolescents. J. Affect. Disord. 260 , 61–66. https://doi.org/10.1016/j.jad.2019.08.025 (2020).

Högberg, B., Strandh, M. & Hagquist, C. Gender and secular trends in adolescent mental health over 24 years–the role of school-related stress. Soc. Cci Med. 250 , 112890. https://doi.org/10.1016/j.socscimed.2020.112890 (2020).

Hagquist, C., Due, P., Torsheim, T. & Välimaa, R. Cross-country comparisons of trends in adolescent psychosomatic symptoms—a Rasch analysis of HBSC data from four Nordic countries. Health Qual. Life Outcomes 17 (1), 1–13. https://doi.org/10.1186/s12955-019-1097-x (2019).

Shorey, S., Ng, E. D. & Wong, C. H. Global prevalence of depression and elevated depressive symptoms among adolescents: A systematic review and meta-analysis. Br. J. Clin. Psychol. 61 (2), 287–305. https://doi.org/10.1111/bjc.12333 (2022).

Potrebny, T. et al. Health complaints among adolescents in Norway: A twenty-year perspective on trends. PloS one 14 (1), e0210509. https://doi.org/10.1371/journal.pone.0210509 (2019).

Article   CAS   PubMed   PubMed Central   Google Scholar  

van Geelen, S. M. & Hagquist, C. Are the time trends in adolescent psychosomatic problems related to functional impairment in daily life? A 23-year study among 20,000 15–16 year olds in Sweden. J. Psychol. Res. 87 , 50–56. https://doi.org/10.1016/j.jpsychores.2016.06.003 (2016).

Swedish Association of Local Authorities and Regions and Ministry of Health and Social Affairs. Insatser inom området psykisk hälsa och suicidprevention. Överenskommelse mellan staten och Sveriges Kommuner och Regioner (SKR). Swedish Association of Local Authorities and Regions and Ministry of Health and Social Affairs. Stokholm, Sweden: 2021–2022. https://skr.se/skr/halsasjukvard/utvecklingavverksamhet/psykiskhalsa/overenskommelsepsykiskhalsa.234.html (2022).

Brooks, S. J., Feldman, I., Schiöth, H. B. & Titova, O. E. Important gender differences in psychosomatic and school-related complaints in relation to adolescent weight status. Sci. Rep. 11 (1), 14147. https://doi.org/10.1038/s41598-021-93761-0 (2021).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Whitehead, R. et al. Trends in adolescent overweight perception and its association with psychosomatic health 2002–2014: Evidence from 33 countries. J. Adol. Health 60 (2), 204–211. https://doi.org/10.1016/j.jadohealth.2016.09.029 (2017).

Nilsen, W., Karevold, E., Røysamb, E., Gustavson, K. & Mathiesen, K. S. Social skills and depressive symptoms across adolescence: Social support as a mediator in girls versus boys. J. Adol. 36 (1), 11–20. https://doi.org/10.1016/j.adolescence.2012.08.005 (2013).

Englander, E., Donnerstein, E., Kowalski, R., Lin, C. A. & Parti, K. Defining cyberbullying. Pediatric 140 (S2), 148–151. https://doi.org/10.1542/peds.2016-1758U (2017).

Chan, H. C. O. & Wong, D. S. Traditional school bullying and cyberbullying in Chinese societies: Prevalence and a review of the whole-school intervention approach. Aggress. Viol. Behav. 23 , 98–108. https://doi.org/10.1016/j.avb.2015.05.010 (2015).

Griffiths, M. D., Kuss, D. J. & Demetrovics, Z. Social networking addiction: An overview of preliminary findings. Behav Addict. 2014 , 119–141. https://doi.org/10.1016/B978-0-12-407724-9.00006-9 (2014).

Athanasiou, K. et al. Cross-national aspects of cyberbullying victimization among 14–17-year-old adolescents across seven European countries. BMC Public Health 18 , 1–15. https://doi.org/10.1186/s12889-018-5682-4 (2018).

Nagata, J. M. et al. Cyberbullying and Sleep Disturbance among Early Adolescents in the US. Acad. Pediatr. 23 (6), 1220–1225. https://doi.org/10.1016/j.acap.2022.12.007 (2022).

Fahy, A. E. et al. Longitudinal associations between cyberbullying involvement and adolescent mental health. J. Ado.l Health 59 (5), 502–509. https://doi.org/10.1016/j.jadohealth.2016.06.006 (2016).

Zych, I., Ortega-Ruiz, R. & Del Rey, R. Systematic review of theoretical studies on bullying and cyberbullying: Facts, knowledge, prevention, and intervention. Aggress. Viol. Behav. 23 , 1–21. https://doi.org/10.1016/j.avb.2015.10.001 (2015).

Kowalski, R. M. & Limber, S. P. Psychological, physical, and academic correlates of cyberbullying and traditional bullying. J. Adol. Health 53 (1), S13–S20. https://doi.org/10.1016/j.jadohealth.2012.09.018 (2013).

Van Geel, M., Vedder, P. & Tanilon, J. Relationship between peer victimization, cyberbullying, and suicide in children and adolescents: A meta-analysis. JAMA Pediatr. 168 (5), 435–442. https://doi.org/10.1001/jamapediatrics.2013.4143 (2014).

Article   CAS   PubMed   Google Scholar  

Albdour, M., Hong, J. S., Lewin, L. & Yarandi, H. The impact of cyberbullying on physical and psychological health of Arab American adolescents. J. Immig. Minor. Health 21 , 706–715. https://doi.org/10.1007/s10903-018-00850-w (2019).

Yoon, Y. et al. Association of cyberbullying involvement with subsequent substance use among adolescents. J. Adol. Health 65 (5), 613–620. https://doi.org/10.1016/j.jadohealth.2019.05.006 (2019).

Yuchang, J., Junyi, L., Junxiu, A., Jing, W. & Mingcheng, H. The differential victimization associated with depression and anxiety in cross-cultural perspective: A meta-analysis. Trauma Viol. Abuse 20 (4), 560–573. https://doi.org/10.1177/1524838017726426 (2019).

Gini, G. & Espelage, D. L. Peer victimization, cyberbullying, and suicide risk in children and adolescents. Jama 312 (5), 545–546. https://doi.org/10.1001/jama.2014.3212 (2014).

Tullett-Prado, D., Doley, J. R., Zarate, D., Gomez, R. & Stavropoulos, V. Conceptualising social media addiction: A longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults. BMC Psychol. 23 (1), 1–27. https://doi.org/10.1186/s12888-023-04985-5 (2023).

Keles, B., McCrae, N. & Grealish, A. A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adol Youth 25 (1), 79–93. https://doi.org/10.1080/02673843.2019.1590851 (2020).

O’reilly, M. et al. Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin. Child Psychol. Psych. 23 (4), 601–613. https://doi.org/10.1177/1359104518775154 (2018).

Marino, C., Gini, G., Angelini, F., Vieno, A. & Spada, M. M. Social norms and e-motions in problematic social media use among adolescents. Addict. Behav. Rep. 11 , 100250. https://doi.org/10.1016/j.abrep.2020.100250 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Sedgwick, R., Epstein, S., Dutta, R. & Ougrin, D. Social media, internet use and suicide attempts in adolescents. Curr. Opin. Psychol. 32 (6), 534. https://doi.org/10.1097/YCO.0000000000000547 (2019).

Marino, C., Hirst, C. M., Murray, C., Vieno, A. & Spada, M. M. Positive mental health as a predictor of problematic internet and Facebook use in adolescents and young adults. J. Happ. Stud. 19 , 2009–2022. https://doi.org/10.1007/s10902-017-9908-4 (2018).

Sarmiento, I. G. et al. How does social media use relate to adolescents’ internalizing symptoms? Conclusions from a systematic narrative review. Adol. Res. Rev. 5 , 381–404. https://doi.org/10.1007/s40894-018-0095-2 (2020).

Hanprathet, N., Manwong, M., Khumsri, J. M. S., Yingyeun, R. & Phanasathit, M. Facebook addiction and its relationship with mental health among Thai high school students. J. Med. Assoc. Thailand 98 , 81–90 (2015).

Google Scholar  

Cerutti, R. et al. Sleep disturbances partially mediate the association between problematic internet use and somatic symptomatology in adolescence. Curr. Psychol. 40 , 4581–4589. https://doi.org/10.1007/s12144-019-00414-7 (2021).

Van Den Eijnden, R., Koning, I., Doornwaard, S., Van Gurp, F. & Ter Bogt, T. The impact of heavy and disordered use of games and social media on adolescents’ psychological, social, and school functioning. J. Behav. Addit. 7 (3), 697–706. https://doi.org/10.1556/2006.7.2018.65 (2018).

Andreassen, C. S. & Pallesen, S. Social network site addiction-an overview. Curr. Pharma Des. 20 (25), 4053–4061. https://doi.org/10.2174/13816128113199990616 (2014).

Article   CAS   Google Scholar  

Andreassen, C. S. Online social network site addiction: A comprehensive review. Curr. Addit Rep. 2 (2), 175–184. https://doi.org/10.1007/s40429-015-0056-9 (2015).

Best, P., Manktelow, R. & Taylor, B. Online communication, social media and adolescent wellbeing: A systematic narrative review. Child. Youth Serv. Rev. 41 , 27–36. https://doi.org/10.1016/j.childyouth.2014.03.001 (2014).

Boer, M. et al. Adolescents’ intense and problematic social media use and their well-being in 29 countries. J. Adol. Health 66 (6), S89–S99. https://doi.org/10.1016/j.jadohealth.2020.02.014 (2020).

Inchley, J. et al . Adolescent alcohol-related behaviours: Trends and inequalities in the WHO European Region, 2002–2014: Observations from the Health Behaviour in School-aged Children (HBSC) WHO collaborative cross-national study. World Health Organization. Regional Office for Europe (2018). https://apps.who.int/iris/handle/10665/342239 .

Moor, I. et al. The 2017/18 Health Behaviour in School-aged Children (HBSC) study–mthodology of the World Health Organization’s child and adolescent health study. J. Health Monitor. 5 (3), 88. https://doi.org/10.25646/6904 (2020).

Nardone, P. et al. Dietary habits among Italian adolescents and their relation to socio-demographic characteristics. Ann. Istit. Super. Sanita 56 (4), 504–513. https://doi.org/10.4415/ANN_20_04_15 (2020).

Craig, W. et al. Social media use and cyber-bullying: A cross-national analysis of young people in 42 countries. J. Adol. Health 66 (6), S100–S108. https://doi.org/10.1016/j.jadohealth.2020.03.006 (2020).

Moor, I. et al. The 2017/18 Health Behaviour in School-aged Children (HBSC) study–methodology of the World Health Organization’s child and adolescent health study. J. Health Monitor. 5 (3), 88 (2020).

Inchley, J., Currie, D., Cosma, A. & Samdal, O. Health Behaviour in School-Aged Children (HBSC) Study Protocol: Background, Methodology and Mandatory Items for the 2017/18 Survey ; CAHRU: St Andrews, UK (2018).

Haugland, S. & Wold, B. Subjective health complaints in adolescence—reliability and validity of survey methods. J. Adol. 24 (5), 611–624. https://doi.org/10.1006/jado.2000.0393 (2001).

Khan, A., Khan, S. R. & Lee, E. Y. Association between lifestyle behaviours and mental health of adolescents: Evidence from the Canadian HBSC Surveys, 2002–2014. Int. J. Environ. Res. Public Health 19 (11), 6899. https://doi.org/10.3390/ijerph19116899 (2022).

Högberg, B., Strandh, M., Johansson, K. & Petersen, S. Trends in adolescent psychosomatic complaints: A quantile regression analysis of Swedish HBSC data 1985–2017. Scand. J. Public Health 2022 , 21094497. https://doi.org/10.1177/14034948221094497 (2022).

Bjereld, Y., Augustine, L., Turner, R., Löfstedt, P. & Ng, K. The association between self-reported psychosomatic complaints and bullying victimisation and disability among adolescents in Finland and Sweden. Scand. J. Public Health 2022 , 1089769. https://doi.org/10.1177/14034948221089769 (2022).

Heinz, A., van Duin, C., Kern, M. R., Catunda, C. & Willems, H. Trends from 2006–2018 in Health, Health Behaviour, Health Outcomes and Social Context of Adolescents in Luxembourg . University of Luxembourg (2020).  http://hdl.habndle.net/10993/42571 .

Gariepy, G., McKinnon, B., Sentenac, M. & Elgar, F. J. Validity and reliability of a brief symptom checklist to measure psychological health in school-aged children. Child Indic. Res. 9 , 471–484. https://doi.org/10.1007/s12187-015-9326-2 (2016).

Biswas, T. et al. Variation in the prevalence of different forms of bullying victimisation among adolescents and their associations with family, peer and school connectedness: A population-based study in 40 lower and middle income to high-income countries (LMIC-HICs). J. Child. Adol. Trauma 2022 , 1–11. https://doi.org/10.1007/s40653-022-00451-8 (2022).

Sasson, H., Tur-Sinai, A., Dvir, K. & Harel-Fisch, Y. The role of parents and peers in cyberbullying perpetration: Comparison among Arab and Jewish and youth in Israel. Child Indic. Res. 2022 , 1–21. https://doi.org/10.1007/s12187-022-09986-6 (2022).

Marengo, N. et al. Cyberbullying and problematic social media use: An insight into the positive role of social support in adolescents—data from the Health Behaviour in School-aged Children study in Italy. Public Health 199 , 46–50. https://doi.org/10.1016/j.puhe.2021.08.010 (2021).

Van den Eijnden, R. J. J. M., Lemmens, J. & Valkenburg, P. The social media disorder scale: Validity and psychometric properties. Comp. Hum. Behav. 61 (August), 478487. https://doi.org/10.1016/j.chb.2016.03.038 (2016).

Borraccino, A. et al. Problematic social media use and cyber aggression in Italian adolescents: The remarkable role of social support. Int. J. Environ. Res. Public Health 19 (15), 9763. https://doi.org/10.3390/ijerph19159763 (2022).

Hamre, R., Smith, O. R. F., Samdal, O. & Haug, E. Gaming behaviors and the association with sleep duration, social jetlag, and difficulties falling asleep among Norwegian adolescents. Int. J. Environ. Res. Public Health 19 (3), 1765. https://doi.org/10.3390/ijerph19031765 (2022).

Currie, C. et al. Researching health inequalities in adolescents: The development of the Health Behaviour in School-Aged Children (HBSC) family affluence scale. Soc. Sci Med. 66 (6), 1429–1436. https://doi.org/10.1016/j.socscimed.2007.11.024 (2008).

Zimet, G. D., Powell, S. S., Farley, G. K., Werkman, S. & Berkoff, K. A. Psychometric characteristics of the multidimensional scale of perceived social support. J. Person. Assess. 55 (3–4), 610–617. https://doi.org/10.1080/00223891.1990.9674095 (1990).

MacKinnon, D. P., Lockwood, C. M., Brown, C. H., Wang, W. & Hoffman, J. M. The intermediate endpoint effect in logistic and probit regression. Clin. Trial 4 (5), 499–513. https://doi.org/10.1177/1740774507083434 (2007).

Rijnhart, J. J., Valente, M. J., Smyth, H. L. & MacKinnon, D. P. Statistical mediation analysis for models with a binary mediator and a binary outcome: The differences between causal and traditional mediation analysis. Prevent. Sci. 2021 , 1–11. https://doi.org/10.1007/s11121-021-01308-6 (2021).

Tingley D, Yamamoto T, Hirose K, Keele L, Imai K, Yamamoto MT. Package ‘mediation’. Computer software manual. 2019 Sep 13:175-84.

DiCiccio, T. J. & Efron, B. Bootstrap confidence intervals. Stat. Sci. 11 (3), 189–228. https://doi.org/10.1214/ss/1032280214 (1996).

Article   MathSciNet   Google Scholar  

Preacher, K. J. & Hayes, A. F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav. Res. Method 40 (3), 879–891. https://doi.org/10.3758/BRM.40.3.879 (2008).

Tomşa, R., Jenaro, C., Campbell, M. & Neacşu, D. Student’s experiences with traditional bullying and cyberbullying: Findings from a Romanian sample. Procedia-Soc. Behav. Sci. 78 , 586–590. https://doi.org/10.1016/j.sbspro.2013.04.356 (2013).

Baier, D., Hong, J. S., Kliem, S. & Bergmann, M. C. Consequences of bullying on adolescents’ mental health in Germany: Comparing face-to-face bullying and cyberbullying. J. Child Fam. Stud. 28 , 2347–2357. https://doi.org/10.1007/s10826-018-1181-6 (2019).

Beckman, L., Hagquist, C. & Hellström, L. Does the association with psychosomatic health problems differ between cyberbullying and traditional bullying?. Emot. Behav. Differ. 17 (3–4), 421–434. https://doi.org/10.1080/13632752.2012.704228 (2012).

Lazuras, L., Barkoukis, V. & Tsorbatzoudis, H. Face-to-face bullying and cyberbullying in adolescents: Trans-contextual effects and role overlap. Tech. Soc. 48 , 97–101. https://doi.org/10.1016/j.techsoc.2016.12.001 (2017).

Li, J., Sidibe, A. M., Shen, X. & Hesketh, T. Incidence, risk factors and psychosomatic symptoms for traditional bullying and cyberbullying in Chinese adolescents. Child. Youth Serv. Rev. 107 , 104511. https://doi.org/10.1016/j.childyouth.2019.104511 (2019).

Nixon, C. L. Current perspectives: The impact of cyberbullying on adolescent health. Adol. Health Med. Therapy 2014 , 143–158. https://doi.org/10.2147/AHMT.S36456 (2014).

Olenik-Shemesh, D., Heiman, T. & Eden, S. Cyberbullying victimisation in adolescence: Relationships with loneliness and depressive mood. Emot. Behav. Differ. 17 (3–4), 361–374. https://doi.org/10.1080/13632752.2012.704227 (2012).

Sourander, A. et al. Psychosocial risk factors associated with cyberbullying among adolescents: A population-based study. Arch. Gener. Psychiatry 67 (7), 720–728. https://doi.org/10.1001/archgenpsychiatry.2010.79 (2010).

Claudia, M. et al. Problematic social media use: Associations with health complaints among adolescents. Ann. Istit. Super. Sanità 56 (4), 514–521. https://doi.org/10.4415/ANN_20_04_16 (2020).

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 17 (1), 45. https://doi.org/10.3390/ijerph17010045 (2020).

Gini, G. & Pozzoli, T. Bullied children and psychosomatic problems: A meta-analysis. Pediatrics 132 (4), 720–729. https://doi.org/10.1542/peds.2013-0614 (2013).

Landstedt, E. & Persson, S. Bullying, cyberbullying, and mental health in young people. Scand. J. Public Health 42 (4), 393–399. https://doi.org/10.1177/1403494814525 (2014).

Schneider, S. K., Odonnell, L., Stueve, A. & Coulter, R. W. Cyberbullying, school bullying, and psychological distress: A regional census of high school students. Am. J. Public Health 102 (1), 171–177. https://doi.org/10.2105/AJPH.2011.300308 (2012).

Brighi, A., Guarini, A., Melotti, G., Galli, S. & Genta, M. L. Predictors of victimisation across direct bullying, indirect bullying and cyberbullying. Emot. Behav. Differ. 17 (3–4), 375–388. https://doi.org/10.1080/13632752.2012.704684 (2012).

Cowie, H. Cyberbullying and its impact on young people’s emotional health and well-being. The Psychia 37 (5), 167–170. https://doi.org/10.1192/pb.bp.112.040840 (2013).

Berryman, C., Ferguson, C. J. & Negy, C. Social media use and mental health among young adults. Psych. Q. 89 , 307–314. https://doi.org/10.1007/s11126-017-9535-6 (2018).

Verduyn, P., Ybarra, O., Résibois, M., Jonides, J. & Kross, E. Do social network sites enhance or undermine subjective well-being? A critical review. Soc. Issue Policy Rev. 11 (1), 274–302. https://doi.org/10.1542/peds.2007-0693 (2017).

Vogel, E. A., Rose, J. P., Okdie, B. M., Eckles, K. & Franz, B. Who compares and despairs? The effect of social comparison orientation on social media use and its outcomes. Person. Individ. Differ. 86 , 249–256. https://doi.org/10.1016/j.paid.2015.06.026 (2015).

Keles, B., McCrae, N. & Grealish, A. A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adol. Youth 25 (1), 79–93. https://doi.org/10.1080/02673843.2019.1590851 (2020).

Boer, M. et al. Adolescents’ intense and problematic social media use and their well-being in 29 countries. J. Adol. Health 66 (6), 89–99. https://doi.org/10.1016/j.jadohealth.2020.02.014 (2020).

McHugh, B. C., Wisniewski, P., Rosson, M. B. & Carroll, J. M. When social media traumatizes teens: The roles of online risk exposure, coping, and post-traumatic stress. Int. Res. 28 (5), 1169–1188. https://doi.org/10.1108/IntR-02-2017-0077 (2018).

Trnka, R., Martínková, Z. & Tavel, P. An integrative review of coping related to problematic computer use in adolescence. Int. J. Public Health 61 , 317–327. https://doi.org/10.1007/s00038-015-0693-8 (2016).

Chen, L., Ho, S. S. & Lwin, M. O. A meta-analysis of factors predicting cyberbullying perpetration and victimization: From the social cognitive and media effects approach. New Media Soc. 19 (8), 1194–1213. https://doi.org/10.1177/1461444816634037 (2017).

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We thank the 2017/2018 HBSC survey team/network, the coordinator and the Data Bank Manager for granting us access to the datasets. We duly acknowledge all school children who participated in the surveys.

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impact of cyberbullying research paper

Qualitative Methods in School Bullying and Cyberbullying Research: An Introduction to the Special Issue

  • Published: 12 August 2022
  • Volume 4 , pages 175–179, ( 2022 )

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Introduction

School bullying research has a long history, stretching all the way back to a questionnaire study undertaken in the USA in the late 1800s (Burk, 1897 ). However, systematic school bullying research began in earnest in Scandinavia in the early 1970s with the work of Heinemann ( 1972 ) and Olweus ( 1978 ). Highlighting the extent to which research on bullying has grown exponentially since then, Smith et al. ( 2021 ) found that there were only 83 articles with the term “bully” in the title or abstract published in the Web of Science database prior to 1989. The numbers of articles found in the following decades were 458 (1990–1999), 1,996 (2000–2009), and 9,333 (2010–2019). Considering cyberbullying more specifically, Smith and Berkkun ( 2017 , cited in Smith et al., 2021 ) conducted a search of Web of Science with the terms “cyber* and bully*; cyber and victim*; electronic bullying; Internet bullying; and online harassment” until the year 2015 and found that while there were no articles published prior to 2000, 538 articles were published between 2000 and 2015, with the number of articles increasing every year (p. 49).

Numerous authors have pointed out that research into school bullying and cyberbullying has predominantly been conducted using quantitative methods, with much less use of qualitative or mixed methods (Hong & Espelage, 2012 ; Hutson, 2018 ; Maran & Begotti, 2021 ; Smith et al., 2021 ). In their recent analysis of articles published between 1976 and 2019 (in WoS, with the search terms “bully*; victim*; cyberbullying; electronic bullying; internet bullying; and online harassment”), Smith et al. ( 2021 , pp. 50–51) found that of the empirical articles selected, more than three-quarters (76.3%) were based on quantitative data, 15.4% were based on a combination of quantitative and qualitative data, and less than one-tenth (8.4%) were based on qualitative data alone. What is more, they found that the proportion of articles based on qualitative or mixed methods has been decreasing over the past 15 years (Smith et al., 2021 ). While the search criteria excluded certain types of qualitative studies (e.g., those published in books, doctoral theses, and non-English languages), this nonetheless highlights the extent to which qualitative research findings risk being overlooked in the vast sea of quantitative research.

School bullying and cyberbullying are complex phenomena, and a range of methodological approaches is thus needed to understand their complexity (Pellegrini & Bartini, 2000 ; Thornberg, 2011 ). Indeed, over-relying on quantitative methods limits understanding of the contexts and experiences of bullying (Hong & Espelage, 2012 ; Patton et al., 2017 ). Qualitative methods are particularly useful for better understanding the social contexts, processes, interactions, experiences, motivations, and perspectives of those involved (Hutson, 2018 ; Patton et al., 2017 ; Thornberg, 2011 ; Torrance, 2000 ).

Smith et al. ( 2021 ) suggest that the “continued emphasis on quantitative studies may be due to increasingly sophisticated methods such as structural equation modeling … network analysis … time trend analyses … latent profile analyses … and multi-polygenic score approaches” (p. 56). However, the authors make no mention of the range or sophistication of methods used in qualitative studies. Although there are still proportionately few qualitative studies of school bullying and cyberbullying in relation to quantitative studies, and this gap appears to be increasing, qualitative studies have utilized a range of qualitative data collection methods. These methods have included but are not limited to ethnographic fieldwork and participant observations (e.g., Eriksen & Lyng, 2018 ; Gumpel et al., 2014 ; Horton, 2019 ), digital ethnography (e.g., Rachoene & Oyedemi, 2015 ; Sylwander, 2019 ), meta-ethnography (e.g., Dennehy et al., 2020 ; Moretti & Herkovits, 2021 ), focus group interviews (e.g., Odenbring, 2022 ; Oliver & Candappa, 2007 ; Ybarra et al., 2019 ), semi-structured group and individual interviews (e.g., Forsberg & Thornberg, 2016 ; Lyng, 2018 ; Mishna et al., 2005 ; Varjas et al., 2013 ), vignettes (e.g., Jennifer & Cowie, 2012 ; Khanolainen & Semenova, 2020 ; Strindberg et al., 2020 ), memory work (e.g., Johnson et al., 2014 ; Malaby, 2009 ), literature studies (e.g., Lopez-Ropero, 2012 ; Wiseman et al., 2019 ), photo elicitation (e.g., Ganbaatar et al., 2021 ; Newman et al., 2006 ; Walton & Niblett, 2013 ), photostory method (e.g., Skrzypiec et al., 2015 ), and other visual works produced by children and young people (e.g., Bosacki et al., 2006 ; Gillies-Rezo & Bosacki, 2003 ).

This body of research has also included a variety of qualitative data analysis methods, such as grounded theory (e.g., Allen, 2015 ; Bjereld, 2018 ; Thornberg, 2018 ), thematic analysis (e.g., Cunningham et al., 2016 ; Forsberg & Horton, 2022 ), content analysis (e.g., Temko, 2019 ; Wiseman & Jones, 2018 ), conversation analysis (e.g., Evaldsson & Svahn, 2012 ; Tholander, 2019 ), narrative analysis (e.g., Haines-Saah et al., 2018 ), interpretative phenomenological analysis (e.g., Hutchinson, 2012 ; Tholander et al., 2020 ), various forms of discourse analysis (e.g., Ellwood & Davies, 2010 ; Hepburn, 1997 ; Ringrose & Renold, 2010 ), including discursive psychological analysis (e.g., Clarke et al., 2004 ), and critical discourse analysis (e.g., Barrett & Bound, 2015 ; Bethune & Gonick, 2017 ; Horton, 2021 ), as well as theoretically informed analyses from an array of research traditions (e.g., Davies, 2011 ; Jacobson, 2010 ; Søndergaard, 2012 ; Walton, 2005 ).

In light of the growing volume and variety of qualitative studies during the past two decades, we invited researchers to discuss and explore methodological issues related to their qualitative school bullying and cyberbullying research. The articles included in this special issue of the International Journal of Bullying Prevention discuss different qualitative methods, reflect on strengths and limitations — possibilities and challenges, and suggest implications for future qualitative and mixed-methods research.

Included Articles

Qualitative studies — focusing on social, relational, contextual, processual, structural, and/or societal factors and mechanisms — have formed the basis for several contributions during the last two decades that have sought to expand approaches to understanding and theorizing the causes of cyber/bullying. Some have also argued the need for expanding the commonly used definition of bullying, based on Olweus ( 1993 ) (e.g., Allen, 2015 ; Ellwood & Davies, 2010 Goldsmid & Howie, 2014 ; Ringrose & Rawlings,  2015 ; Søndergaard, 2012 ; Walton, 2011 ). In the first article of the special issue, Using qualitative methods to measure and understand key features of adolescent bullying: A call to action , Natalie Spadafora, Anthony Volk, and Andrew Dane instead discuss the usefulness of qualitative methods for improving measures and bettering our understanding of three specific key definitional features of bullying. Focusing on the definition put forward by Volk et al. ( 2014 ), they discuss the definitional features of power imbalance , goal directedness (replacing “intent to harm” in order not to assume conscious awareness, and to include a wide spectrum of goals that are intentionally and strategically pursued by bullies), and harmful impact (replacing “negative actions” in order to focus on the consequences for the victim, as well as circumventing difficult issues related to “repetition” in the traditional definition).

Acknowledging that these three features are challenging to capture using quantitative methods, Spadafora, Volk, and Dane point to existing qualitative studies that shed light on the features of power imbalance, goal directedness and harmful impact in bullying interactions — and put forward suggestions for future qualitative studies. More specifically, the authors argue that qualitative methods, such as focus groups, can be used to investigate the complexity of power relations at not only individual, but also social levels. They also highlight how qualitative methods, such as diaries and autoethnography, may help researchers gain a better understanding of the motives behind bullying behavior; from the perspectives of those engaging in it. Finally, the authors demonstrate how qualitative methods, such as ethnographic fieldwork and semi-structured interviews, can provide important insights into the harmful impact of bullying and how, for example, perceived harmfulness may be connected to perceived intention.

In the second article, Understanding bullying and cyberbullying through an ecological systems framework: The value of qualitative interviewing in a mixed methods approach , Faye Mishna, Arija Birze, and Andrea Greenblatt discuss the ways in which utilizing qualitative interviewing in mixed method approaches can facilitate greater understanding of bullying and cyberbullying. Based on a longitudinal and multi-perspective mixed methods study of cyberbullying, the authors demonstrate not only how qualitative interviewing can augment quantitative findings by examining process, context and meaning for those involved, but also how qualitative interviewing can lead to new insights and new areas of research. They also show how qualitative interviewing can help to capture nuances and complexity by allowing young people to express their perspectives and elaborate on their answers to questions. In line with this, the authors also raise the importance of qualitative interviewing for providing young people with space for self-reflection and learning.

In the third article, Q methodology as an innovative addition to bullying researchers’ methodological repertoire , Adrian Lundberg and Lisa Hellström focus on Q methodology as an inherently mixed methods approach, producing quantitative data from subjective viewpoints, and thus supplementing more mainstream quantitative and qualitative approaches. The authors outline and exemplify Q methodology as a research technique, focusing on the central feature of Q sorting. The authors further discuss the contribution of Q methodology to bullying research, highlighting the potential of Q methodology to address challenges related to gaining the perspectives of hard-to-reach populations who may either be unwilling or unable to share their personal experiences of bullying. As the authors point out, the use of card sorting activities allows participants to put forward their subjective perspectives, in less-intrusive settings for data collection and without disclosing their own personal experiences. The authors also illustrate how the flexibility of Q sorting can facilitate the participation of participants with limited verbal literacy and/or cognitive function through the use of images, objects or symbols. In the final part of the paper, Lundberg and Hellström discuss implications for practice and suggest future directions for using Q methodology in bullying and cyberbullying research, particularly with hard-to-reach populations.

In the fourth article, The importance of being attentive to social processes in school bullying research: Adopting a constructivist grounded theory approach , Camilla Forsberg discusses the use of constructivist grounded theory (CGT) in her research, focusing on social structures, norms, and processes. Forsberg first outlines CGT as a theory-methods package that is well suited to meet the call for more qualitative research on participants’ experiences and the social processes involved in school bullying. Forsberg emphasizes three key focal aspects of CGT, namely focus on participants’ main concerns; focus on meaning, actions, and processes; and focus on symbolic interactionism. She then provides examples and reflections from her own ethnographic and interview-based research, from different stages of the research process. In the last part of the article, Forsberg argues that prioritizing the perspectives of participants is an ethical stance, but one which comes with a number of ethical challenges, and points to ways in which CGT is helpful in dealing with these challenges.

In the fifth article, A qualitative meta-study of youth voice and co-participatory research practices: Informing cyber/bullying research methodologies , Deborah Green, Carmel Taddeo, Deborah Price, Foteini Pasenidou, and Barbara Spears discuss how qualitative meta-studies can be used to inform research methodologies for studying school bullying and cyberbullying. Drawing on the findings of five previous qualitative studies, and with a transdisciplinary and transformative approach, the authors illustrate and exemplify how previous qualitative research can be analyzed to gain a better understanding of the studies’ collective strengths and thus consider the findings and methods beyond the original settings where the research was conducted. In doing so, the authors highlight the progression of youth voice and co-participatory research practices, the centrality of children and young people to the research process and the enabling effect of technology — and discuss challenges related to ethical issues, resource and time demands, the role of gatekeepers, and common limitations of qualitative studies on youth voice and co-participatory research practices.

Taken together, the five articles illustrate the diversity of qualitative methods used to study school bullying and cyberbullying and highlight the need for further qualitative research. We hope that readers will find the collection of articles engaging and that the special issue not only gives impetus to increased qualitative focus on the complex phenomena of school bullying and cyberbullying but also to further discussions on both methodological and analytical approaches.

Allen, K. A. (2015). “We don’t have bullying, but we have drama”: Understandings of bullying and related constructs within the school milieu of a U.S. high school. Journal of Human Behavior in the Social Environment , 25 (3), 159–181.

Barrett, B., & Bound, A. M. (2015). A critical discourse analysis of No Promo Homo policies in US schools. Educational Studies, 51 (4), 267–283.

Article   Google Scholar  

Bethune, J., & Gonick, M. (2017). Schooling the mean girl: A critical discourse analysis of teacher resource materials. Gender and Education, 29 (3), 389–404.

Bjereld, Y. (2018). The challenging process of disclosing bullying victimization: A grounded theory study from the victim’s point of view. Journal of Health Psychology, 23 (8), 1110–1118.

Article   PubMed   Google Scholar  

Bosacki, S. L., Marini, Z. A., & Dane, A. V. (2006). Voices from the classroom: Pictorial and narrative representations of children’s bullying experiences. Journal of Moral Education, 35 (2), 231–245.

Burk, F. L. (1897). Teasing and Bullying. Pedagogical Seminary, 4 (3), 336–371.

Clarke, V., Kitzinger, C., & Potter, J. (2004). ‘Kids are just cruel anyway’: Lesbian and gay parents’ talk about homophobic bullying. British Journal of Social Psychology, 43 (4), 531–550.

Cunningham, C. E., Mapp, C., Rimas, H., Cunningham, S. M., Vaillancourt, T., & Marcus, M. (2016). What limits the effectiveness of antibullying programs? A thematic analysis of the perspective of students. Psychology of Violence, 6 (4), 596–606.

Davies, B. (2011). Bullies as guardians of the moral order or an ethic of truths? Children & Society, 25 , 278–286.

Dennehy, R., Meaney, S., Walsh, K. A., Sinnott, C., Cronin, M., & Arensman, E. (2020). Young people’s conceptualizations of the nature of cyberbullying: A systematic review and synthesis of qualitative research. Aggression and Violent Behavior, 51 , 101379.

Ellwood, C., & Davies, B. (2010). Violence and the moral order in contemporary schooling: A discursive analysis. Qualitative Research in Psychology, 7 (2), 85–98.

Eriksen, I. M., & Lyng, S. T. (2018). Relational aggression among boys: Blind spots and hidden dramas. Gender and Education, 30 (3), 396–409.

Evaldsson, A. -C., Svahn, J. (2012). School bullying and the micro-politics of girls’ gossip disputes. In S. Danby & M. Theobald (Eds.). Disputes in everyday life: Social and moral orders of children and young people (Sociological Studies of Children and Youth, Vol. 15) (pp. 297–323). Bingley: Emerald Group Publishing.

Forsberg, C., & Horton, P. (2022). ‘Because I am me’: School bullying and the presentation of self in everyday school life. Journal of Youth Studies, 25 (2), 136–150.

Forsberg, C., & Thornberg, R. (2016). The social ordering of belonging: Children’s perspectives on bullying. International Journal of Educational Research, 78 , 13–23.

Ganbaatar, D., Vaughan, C., Akter, S., & Bohren, M. A. (2021). Exploring the identities and experiences of young queer people in Mongolia using visual research methods. Culture, Health & Sexuality . Advance Online Publication: https://doi.org/10.1080/13691058.2021.1998631

Gillies-Rezo, S., & Bosacki, S. (2003). Invisible bruises: Kindergartners’ perceptions of bullying. International Journal of Children’s Spirituality, 8 (2), 163–177.

Goldsmid, S., & Howie, P. (2014). Bullying by definition: An examination of definitional components of bullying. Emotional and Behavioural Difficulties, 19 (2), 210–225.

Gumpel, T. P., Zioni-Koren, V., & Bekerman, Z. (2014). An ethnographic study of participant roles in school bullying. Aggressive Behavior, 40 (3), 214–228.

Haines-Saah, R. J., Hilario, C. T., Jenkins, E. K., Ng, C. K. Y., & Johnson, J. L. (2018). Understanding adolescent narratives about “bullying” through an intersectional lens: Implications for youth mental health interventions. Youth & Society, 50 (5), 636–658.

Heinemann, P. -P. (1972). Mobbning – gruppvåld bland barn och vuxna [Bullying – group violence amongst children and adults]. Stockholm: Natur och Kultur.

Hepburn, A. (1997). Discursive strategies in bullying talk. Education and Society, 15 (1), 13–31.

Hong, J. S., & Espelage, D. L. (2012). A review of mixed methods research on bullying and peer victimization in school. Educational Review, 64 (1), 115–126.

Horton, P. (2019). The bullied boy: Masculinity, embodiment, and the gendered social-ecology of Vietnamese school bullying. Gender and Education, 31 (3), 394–407.

Horton, P. (2021). Building walls: Trump election rhetoric, bullying and harassment in US schools. Confero: Essays on Education, Philosophy and Politics , 8 (1), 7–32.

Hutchinson, M. (2012). Exploring the impact of bullying on young bystanders. Educational Psychology in Practice, 28 (4), 425–442.

Hutson, E. (2018). Integrative review of qualitative research on the emotional experience of bullying victimization in youth. The Journal of School Nursing, 34 (1), 51–59.

Jacobson, R. B. (2010). A place to stand: Intersubjectivity and the desire to dominate. Studies in Philosophy and Education, 29 , 35–51.

Jennifer, D., & Cowie, H. (2012). Listening to children’s voices: Moral emotional attributions in relation to primary school bullying. Emotional and Behavioural Difficulties, 17 (3–4), 229–241.

Johnson, C. W., Singh, A. A., & Gonzalez, M. (2014). “It’s complicated”: Collective memories of transgender, queer, and questioning youth in high school. Journal of Homosexuality, 61 (3), 419–434.

Khanolainen, D., & Semenova, E. (2020). School bullying through graphic vignettes: Developing a new arts-based method to study a sensitive topic. International Journal of Qualitative Methods, 19 , 1–15.

Lopez-Ropero, L. (2012). ‘You are a flaw in the pattern’: Difference, autonomy and bullying in YA fiction. Children’s Literature in Education, 43 , 145–157.

Lyng, S. T. (2018). The social production of bullying: Expanding the repertoire of approaches to group dynamics. Children & Society, 32 (6), 492–502.

Malaby, M. (2009). Public and secret agents: Personal power and reflective agency in male memories of childhood violence and bullying. Gender and Education, 21 (4), 371–386.

Maran, D. A., & Begotti, T. (2021). Measurement issues relevant to qualitative studies. In P. K. Smith & J. O’Higgins Norman (Eds.). The Wiley handbook of bullying (pp. 233–249). John Wiley & Sons.

Mishna, F., Scarcello, I., Pepler, D., & Wiener, J. (2005). Teachers’ understandings of bullying. Canadian Journal of Education, 28 (4), 718–738.

Moretti, C., & Herkovits, D. (2021). Victims, perpetrators, and bystanders: A meta-ethnography of roles in cyberbullying. Cad. Saúde Pública, 37 (4), e00097120.

Newman, M., Woodcock, A., & Dunham, P. (2006). ‘Playtime in the borderlands’: Children’s representations of school, gender and bullying through photographs and interviews. Children’s Geographies, 4 (3), 289–302.

Odenbring, Y. (2022). Standing alone: Sexual minority status and victimisation in a rural lower secondary school. International Journal of Inclusive Education, 26 (5), 480–494.

Oliver, C., & Candappa, M. (2007). Bullying and the politics of ‘telling.’ Oxford Review of Education, 33 (1), 71–86.

Olweus, D. (1978). Aggression in the schools – Bullies and the whipping boys . Wiley.

Google Scholar  

Olweus, D. (1993). Bullying in school: What we know and what we can do . Blackwell.

Patton, D. U., Hong, J. S., Patel, S., & Kral, M. J. (2017). A systematic review of research strategies used in qualitative studies on school bullying and victimization. Trauma, Violence, & Abuse, 18 (1), 3–16.

Pellegrini, A. D., & Bartini, M. (2000). A longitudinal study of bullying, victimization, and peer affiliation during the transition from primary school to middle school. American Educational Research Journal, 37 (3), 699–725.

Rachoene, M., & Oyedemi, T. (2015). From self-expression to social aggression: Cyberbullying culture among South African youth on Facebook. Communicatio: South African Journal for Communication Theory and Research , 41 (3), 302–319.

Ringrose, J., & Rawlings, V. (2015). Posthuman performativity, gender and ‘school bullying’: Exploring the material-discursive intra-actions of skirts, hair, sluts, and poofs.  Confero: Essays on Education, Philosophy and Politics , 3 (2), 80–119.

Ringrose, J., & Renold, E. (2010). Normative cruelties and gender deviants: The performative effects of bully discourses for girls and boys in school. British Educational Research Journal, 36 (4), 573–596.

Skrzypiec, G., Slee, P., & Sandhu, D. (2015). Using the PhotoStory method to understand the cultural context of youth victimization in the Punjab. The International Journal of Emotional Education, 7 (1), 52–68.

Smith, P., Robinson, S., & Slonje, R. (2021). The school bullying research program: Why and how it has developed. In P. K. Smith & J. O’Higgins Norman (Eds.). The Wiley handbook of bullying (pp. 42–59). John Wiley & Sons.

Smith, P. K., & Berkkun, F. (2017). How research on school bullying has developed. In C. McGuckin & L. Corcoran (Eds.), Bullying and cyberbullying: Prevalence, psychological impacts and intervention strategies (pp. 11–27). Hauppage, NY: Nova Science.

Strindberg, J., Horton, P., & Thornberg, R. (2020). The fear of being singled out: Pupils’ perspectives on victimization and bystanding in bullying situations. British Journal of Sociology of Education, 41 (7), 942–957.

Sylwander, K. R. (2019). Affective atmospheres of sexualized hate among youth online: A contribution to bullying and cyberbullying research on social atmosphere. International Journal of Bullying Prevention, 1 , 269–284.

Søndergaard, D. M. (2012). Bullying and social exclusion anxiety in schools. British Journal of Sociology of Education, 33 (3), 355–372.

Temko, E. (2019). Missing structure: A critical content analysis of the Olweus Bullying Prevention Program. Children & Society, 33 (1), 1–12.

Tholander, M. (2019). The making and unmaking of a bullying victim. Interchange, 50 , 1–23.

Tholander, M., Lindberg, A., & Svensson, D. (2020). “A freak that no one can love”: Difficult knowledge in testimonials on school bullying. Research Papers in Education, 35 (3), 359–377.

Thornberg, R. (2011). ‘She’s weird!’ – The social construction of bullying in school: A review of qualitative research. Children & Society, 25 , 258–267.

Thornberg, R. (2018). School bullying and fitting into the peer landscape: A grounded theory field study. British Journal of Sociology of Education, 39 (1), 144–158.

Torrance, D. A. (2000). Qualitative studies into bullying within special schools. British Journal of Special Education, 27 (1), 16–21.

Varjas, K., Meyers, J., Kiperman, S., & Howard, A. (2013). Technology hurts? Lesbian, gay, and bisexual youth perspectives of technology and cyberbullying. Journal of School Violence, 12 (1), 27–44.

Volk, A. A., Dane, A. V., & Marini, Z. A. (2014). What is bullying? A Theoretical Redefinition, Developmental Review, 34 (4), 327–343.

Walton, G. (2005). Bullying widespread. Journal of School Violence, 4 (1), 91–118.

Walton, G. (2011). Spinning our wheels: Reconceptualizing bullying beyond behaviour-focused Approaches.  Discourse: Studies in the Cultural Politics of Education , 32 (1), 131–144.

Walton, G., & Niblett, B. (2013). Investigating the problem of bullying through photo elicitation. Journal of Youth Studies, 16 (5), 646–662.

Wiseman, A. M., & Jones, J. S. (2018). Examining depictions of bullying in children’s picturebooks: A content analysis from 1997 to 2017. Journal of Research in Childhood Education, 32 (2), 190–201.

Wiseman, A. M., Vehabovic, N., & Jones, J. S. (2019). Intersections of race and bullying in children’s literature: Transitions, racism, and counternarratives. Early Childhood Education Journal, 47 , 465–474.

Ybarra, M. L., Espelage, D. L., Valido, A., Hong, J. S., & Prescott, T. L. (2019). Perceptions of middle school youth about school bullying. Journal of Adolescence, 75 , 175–187.

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Acknowledgements

We would like to thank the authors for sharing their work; Angela Mazzone, James O’Higgins Norman, and Sameer Hinduja for their editorial assistance; and Dorte Marie Søndergaard on the editorial board for suggesting a special issue on qualitative research in the journal.

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Horton, P., Lyng, S.T. Qualitative Methods in School Bullying and Cyberbullying Research: An Introduction to the Special Issue. Int Journal of Bullying Prevention 4 , 175–179 (2022). https://doi.org/10.1007/s42380-022-00139-5

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Prevalence and related risks of cyberbullying and its effects on adolescent

Gassem gohal.

1 Pediatric Department, Faculty of Medicine, Jazan University, Jazan, Saudi Arabia

Ahmad Alqassim

2 Family and Community Medicine Department, Faculty of Medicine, Jazan University, Jazan, Saudi Arabia

Ebtihal Eltyeb

Ahmed rayyani.

3 Medical Intern, Faculty of Medicine, Jazan University, Jazan, Saudi Arabia

Bassam Hakami

Abdullah al faqih, abdullah hakami, almuhannad qadri, mohamed mahfouz, associated data.

The authors ensure that the data supporting the results of this study are available within the article. The raw data for the study will be obtainable from the corresponding author upon reasonable demand.

Cyberbullying is becoming common in inflicting harm on others, especially among adolescents. This study aims to assess the prevalence of cyberbullying, determine the risk factors, and assess the association between cyberbullying and the psychological status of adolescents facing this problem in the Jazan region, Saudi Arabia.

A cross-sectional study was conducted on 355 students, aged between 12–18 years, through a validated online questionnaire to investigate the prevalence and risk factors of cyberbullying and assess psychological effects based on cyberbullying questionnaire and Mental Health Inventory-5 (MHI-5) questions.

The participants in this study numbered 355; 68% of participants were females compared to 32% were males. Approximately 20% of the participants spend more than 12 h daily on the Internet, and the estimated overall prevalence of cyberbullying was 42.8%, with the male prevalence slightly higher than females. In addition, 26.3% of the participants were significantly affected in their academic Performance due to cyberbullying. Approximately 20% of all participants considered leaving their schools, 19.7% considered ceasing their Internet use, and 21.1% considered harming themselves due to the consequences of cyberbullying. There are essential links between the frequency of harassment, the effect on academic Performance, and being a cyber victim.

Conclusions

Cyberbullying showed a high prevalence among adolescents in the Jazan region with significant associated psychological effects. There is an urgency for collaboration between the authorities and the community to protect adolescents from this harmful occurrence.

Introduction

Cyberbullying is an intentional, repeated act of harm toward others through electronic tools; however, there is no consensus to define it [ 1 – 3 ]. With the surge in information and data sharing in the emerging digital world, a new era of socialization through digital tools, and the popularization of social media, cyberbullying has become more frequent than ever and occurs when there is inadequate adult supervision [ 4 , 5 ]. A large study that looked at the incidence of cyberbullying among adolescents in England found a prevalence of 17.9%, while one study conducted in Saudi Arabia found a prevalence of 20.97% [ 6 , 7 ]. Cyberbullying can take many forms, including sending angry, rude, or offensive messages; intimidating, cruel, and possibly false information about a person to others; sharing sensitive or private information (outing); and exclusion, which involves purposefully leaving someone out of an online group [ 8 ]. Cyberbullying is influenced by age, sex, parent–child relationships, and time spent on the Internet [ 9 , 10 ]. Although some studies have found that cyberbullying continues to increase in late adolescence, others found that cyberbullying tends to peak at 14 and 15 years old before decreasing through the remaining years of adolescence [ 11 – 13 ].

The COVID-19 epidemic has impacted the prevalence of cyberbullying since social isolation regulations have reduced face-to-face interaction, leading to a significant rise in the use of social networking sites and online activity. As a result, there was a higher chance of experiencing cyberbullying [ 14 ].

Unlike traditional Bullying, which usually only occurs in school and is mitigated at home, victims of cyberbullying can be contacted anytime and anywhere. Parents and teachers are seen as saviors in cases of traditional Bullying. Simultaneously, in cyberbullying, children tend to be reluctant to tell adults for fear of losing access to their phones and computers, so they usually hide the cyberbullying incident [ 15 ]. Reports show that cyberbullying is a form of harm not easily avoided by the victim. In addition, in the cyber form of Bullying, identification of the victim and the perpetrator is generally challenging compared to traditional Bullying; this makes an accurate estimation of the problem widely contested [ 16 , 17 ].

There is growing evidence that is cyberbullying causes more significant levels of depression, anxiety, and loneliness than traditional forms of Bullying. A meta-analysis examining the association between peer victimization, cyberbullying, and suicide in children and adolescents indicates that cyberbullying is more intensely related to suicidal ideation than traditional Bullying [ 18 ]. Moreover, the significant problem is that cyberbullying impacts adolescent due to its persistence and recurrence. A recent report in Saudi Arabia indicated a growing rise in cyberbullying in secondary schools and higher education, from 18% to approximately 27% [ 19 ]. In primary schools and kindergartens in Saudi Arabia, we were not surprised to find evidence that children were unaware that cyberbullying is illegal. Although the study showed an adequate awareness of the problem in our country, Saudi Arabia, there were relatively significant misconceptions [ 20 ].

Adolescents' emotional responses to cyberbullying vary in severity and quality. However, anger, sadness, concern, anxiety, fear, and depression are most common among adolescent cyber victims [ 21 ]. Moreover, cyberbullying may limit students' academic Performance and cause higher absenteeism rates [ 22 ]. Consequently, this study aims to assess the prevalence of cyberbullying, determine the risk factors, and establish the association between cyberbullying and the psychological status of adolescents. We believe our study will be an extension of and significantly add to the literature regarding the nature and extent of cyberbullying in the Jazan region of Saudi Arabia.

A descriptive cross-sectional study was carried out in the Jazan region, a province of the Kingdom of Saudi Arabia. It is located on the tropical Red Sea coast of southwestern Saudi Arabia.

Design and participants

A descriptive cross-sectional study was carried out in the Jazan region, a province of the Kingdom of Saudi Arabia. It is located on the tropical Red Sea coast of southwestern Saudi Arabia. The study targeted adolescents (12–18 years old) who use the Internet to communicate in the Jazan region. The main inclusion criteria are adolescents between 12–18 years who use the Internet and agree to participate; however, it excludes adolescents not matching the inclusion criteria or those refusing to participate in the study. If participants were under 16, the parent and/or legal guardian should be notified. A sample of participants was estimated for this study, and the ideal sample size was calculated to be 385 using the Cochran formula, n  = (z) 2 p (1 – p) / d 2 . Where: p = prevalence of cyberbullying 50%, z = a 95% confidence interval, d = error of not more than 5%. A convenience sample was used to recruit the study participants. A self-administrated online questionnaire was used to collect the study information from May to December 2021.

The ethical approval for this study was obtained from The Institute Review Board (IRB) of Jazan University (Letter v.1 2019 dated 08/04/2021). Informed consent was acquired from all participants and was attached to the beginning of the form and mandatory to be read and checked before the participant proceeded to the first part of the questionnaire. For the participants under 16, informed consent was obtained from a parent and legal guardian.

Procedure of data collection and study measures

An Arabic self-administrated online questionnaire was used for this research. This anonymous online survey instrument was based on (Google Forms). The study team distributed the questionnaire to the participants through school teachers. The research team prepared the study questionnaire and chose the relevant cyberbullying scale questions from similar studies [ 5 , 6 ]. The questionnaire was translated by two bilingual professionals to ensure the accuracy and appropriateness of the instrument wording. A panel of experts then discussed and assessed the validity and suitability of the instrument for use on adolescents. The panel also added and edited a few questions to accommodate the local culture of Saudi students. It was validated with a pilot study that included 20 participants. The questionnaire was divided into three main sections. The first part of the questionnaire contains the basic participant information, including gender, age, nationality, school grade, residence, and information about family members and the mother's occupation and education. The mother's level of education was considered as it found that mothers' low levels of education specifically had a detrimental impact on the cyberbullying process [ 23 ]. The second section explores the participant's definition of cyberbullying, questions regarding exposure to cyberbullying as a victim or by bullying another person, and questions considering the possible risk factors behind cyberbullying. The last section explores how cyberbullying affects adolescents psychologically based on the standardized questionnaire Mental Health Inventory-5 (MHI-5). MHI-5 is a well-known, valid, reliable, and brief international instrument for assessing mental health in children and adolescents (such as satisfaction, interest in, and enjoyment of life) and negative aspects (such as anxiety and depression) [ 24 ]. It is composed of five questions, as shown in Table ​ Table1. 1 . There are six options available for each question, ranging from "all the time" (1 point) to "none of the time" (6 points); therefore, the adolescent's score varies between five and 30. These questions assess both negative and positive qualities of mental health, as well as questions about anxiety and depression. By adding all the item scores and converting this score to a scale ranging from 0 to 100, the final MHI-5 score is determined, with lower scores indicating more severe depressive symptoms. The value for which the sum of sensitivity and specificity was utilized to establish the ideal cut-off score for MHI-5 in many similar studies was reviewed to reach an optimal conclusion. Therefore, we considered all cut-off values with associated sensitivities and specificities of various MHI-5 cut-off points previously employed among adolescents in similar studies and compared them to conclude that MHI-5 = 70 as our cut points. So the presence of depressive symptoms is considered with an MHI-5 cut-off score of ≤ 70 [ 25 ].

Factor Analysis of the Arabic Version of the Mental Health Inventory – 5 (MHI-5) ( n  = 355)

Kaiser–Meyer–Olkin Measure of Sampling Adequacy = 0.700

Bartlett’s Test of Sphericity, Chi-Square = 739.84 p  < 0.001

The Questionnaires were initially prepared in English and then translated into Arabic. A native speaker with fluency in English (with experience in translation) converted the questionnaire from the initial English version into Arabic. Then, we performed a pilot study among 20 participants to ensure the readability and understandability of the questionnaire questions. We also assessed the internal consistency of the questionnaire based on Cronbach’s alpha, which produced an acceptable value of 0.672. The internal consistency for Mental Health Inventory-5 (MHI-5) was reported at 0.557. In order to assess the factor structure of the Arabic-translated version of the (MHI-5) questionnaire, a factor analysis was conducted. The factor loading of the instrument is shown in Table ​ Table1. 1 . Using principal component analysis and the varimax rotation method, we found a one-component solution explaining 56.766% of the total variance. All items loaded on the first factor ranged from (0.688 to 0.824), which confirms that a single factor has explained all the items of the scale. In addition, Bartlett’s test of sphericity was found significant ( p  < 0.001).

Data presentation & statistical analysis

Simple tabulation frequencies were used to give a general overview of the data. The prevalence of cyberbullying was presented using 95% C.I.s, and the Chi-squared test was performed to determine the associations between individual categorical variables and Mental Health. The univariate and multivariate logistic regression model was derived, and unadjusted and adjusted odds ratios (OR) and their 95% confidence intervals (C.I.s) were calculated. A P -value of 0.05 or less was used as the cut-off level for statistical significance. The statistical analysis was completed using SPSS ver. 25.0 (SPSS Inc. Chicago, IL, USA) software.

The distributed survey targeted approximately 385 students, but the precise number of respondents to the questionnaire was 355 (92% response rate), with 68% of female students responding, compared to 32% of male students. More than half of the respondents were secondary school students, with a nearly equal mix of respondents living in cities and rural areas. Table ​ Table2 2 demonstrates that 20% of the participants spend more than 12 h daily on the Internet and electronic gadgets, while only 13% spend less than two hours.

Socio-demographic characteristics of participants

Abbreviations: SD = standard deviation

As demonstrated in Table ​ Table3, 3 , the total prevalence of cyberbullying was estimated to be 42.8%, with male prevalence somewhat higher than female prevalence. Additional variables, such as the number of hours spent on the Internet, did not affect the prevalence. Table ​ Table4 4 shows the pattern and experience of being cyberbullied across mental health levels, as measured by the MHI-5.

Prevalence of cyberbullying among adolescents in the Jazan region

# p -value is based on Pearson's Chi-square test, Abbreviations: CI = confidence interval

Pattern and experience of being cyberbullied among adolescents according to a mental health level based on MHI-5

# p -value is based on Pearson's Chi-square test, Abbreviations: MHI-5 = the Mental Health Inventory-5

Academic Performance was significantly affected due to cyberbullying in 26.3% of the participants. Furthermore, approximately 20% of all participants considered leaving their schools for this reason. Moreover, 19.7% of the participants thought of stopping using the Internet and electronic devices, while 21.1% considered harming themselves due to the effects of cyberbullying. Regarding associations between various variables and psychological effects using the MHI-5, there are significant associations between whether the participant has been a cyber victim before (cOR 2.8), the frequency of harassment (cOR 1.9), academic Performance (cOR 6.5), and considering leaving school as a result of being a cyber victim (cOR 3.0). In addition, by using univariate logistic regression analysis, there are significant associations between the psychological effects and the participant's thoughts of getting rid of a bully (cOR 2.8), thinking to stop using electronic devices (cOR 3.0), and considering hurting themselves as the result of cyberbullying (cOR 6.4). In addition, the use of the multivariate logistic regression analysis showed that frequency of harassment was the only statistically significant predictor of mental health among adolescents (aOR 2.8). Other variables continue to have higher (aORs) but without statistical significance. All these results are demonstrated in Table ​ Table4 4 .

Cyberbullying prevalence rates among adolescents vary widely worldwide, ranging from 10% to more than 70% in many studies. This variation results from certain factors, specifically gender involvement, as a decisive influencing factor [ 26 , 27 ]. Our study found a prevalence of 42.8% (95% confidence interval (CI): 37.7–48), which is higher than the median reported prevalence of cyberbullying of 23.0% in a scoping review that included 36 studies conducted in the United States in adolescents aged 12 to 18 years old [ 28 ]. A systematic review found that cyberbullying ranged from 6.5% to 35.4% [ 3 ]. These two studies gathered data before the COVID-19 pandemic. When compared to recent studies, it was found that cyberbullying increased dramatically during the COVID-19 era [ 29 , 30 ]. Subsequently, with the massive mandate of world online communication in teaching and learning, young adolescents faced a large amount of cyberspace exposure with all risk-related inquiries. Psychological distress due to COVID-19 and spending far more time on the Internet are vital factors in this problem, which might be a reasonable explanation for our results.

There is insufficient data to compare our findings to the Arab world context, notably Saudi Arabia. Although, according to one study done among Saudi Arabian university students, the prevalence was 17.6%. [ 31 ]. we discovered a considerable discrepancy between this prevalence and our findings, and the decisive explanation is the difference in the target age group studied. Age is a crucial risk factor for cyberbullying, and according to one study, cyberbullying peaks at around 14 and 15 years of age and then declines in late adolescence. Thus, a U-inverted relation exists between prevalence and age [ 11 – 13 , 32 ].

In our study, males reported being more vulnerable to cyberbullying despite there being more female participants; this inconsistent finding with previous literature requires further investigation. A strong, but not recent, meta-analysis in 2014 reported that, in general, males are likely to cyberbully more than females. Females were more likely to report cyberbullying during early to mid-adolescence than males [ 11 ]. This finding presents a concern for males reporting lower than females’ results in our data and raises some questions about whether cultural or religious conservative values play a role.

Increased Internet hours are another risk factor in this study and were significantly associated with cyberbullying. Specifically, it was likely to be with heavy Internet users (> 12 h/day); a similar result was well documented in one equivalent study [ 3 ]. Notably, while some studies have reported that those living in city areas are more likely to be cyberbullying victims than their counterparts from suburban areas [ 3 ], our observations reported no significant influence of this factor on the prevalence of cyberbullying.

According to a population-based study on cyberbullying and teenage well-being in England, which included 110,000 pupils, traditional Bullying accounted for more significant variability in mental well-being than cyberbullying. It did, however, conclude that both types of Bullying carry a risk of affecting mental health [ 33 ]. We confirmed in this study that multiple occurrences of cyberbullying and the potential for being a victim are risk factors influencing mental health ( P  < 0.001). Moreover, the frequency of harassment also shows a significant, influential effect. The victim's desire to be free from the perpetrator carrying out the cyberbullying is probably an alarming sign and a precursor factor for suicidal ideation; we reported that nearly half of the participants wished they could get rid of the perpetrators. Furthermore, more than 20% of participants considered harming themselves due to cyberbullying; this result is consistent with many studies that linked cyberbullying and self-harm and suicidal thoughts [ 34 – 36 ].

Adolescence is a particularly vulnerable age for the effects of cyberbullying on mental health. In one Saudi Arabian study, parents felt that cyberbullying is more detrimental than Bullying in the schoolyard and more harmful to their children's mental health. According to them, video games were the most popular social platform for cyberbullying [ 37 ]. Both cross-sectional and longitudinal research shows a significant link between cyberbullying and emotional symptoms, including anxiety and depression [ 38 , 39 ]. Therefore, we employed the MHI-5 to measure the mental impact of cyberbullying on adolescents in this study. Overall, the MHI-5 questionnaire showed relatively high sensitivity in detecting anxiety and depression disorders for general health and quality of life assessments. The questions listed happy times, peacefulness, and sensations of calmness, in addition to episodes of anxiousness, downheartedness, and feelings of depression, as given in Table ​ Table1 1 .

Cyberbullying has been well-documented to affect the academic achievement of the victim adolescents. Therefore, bullied adolescents are likelier to miss school, have higher absence rates, dislike school, and report receiving lower grades. According to one meta-analysis, peer victimization has a significant negative link with academic achievement, as measured by grades, student performance, or instructor ratings of academic achievement [ 40 ]. In our investigation, we reported that up to 20% of participants considered leaving their schools due to the adverse effects of cyberbullying (cOR 3.0) and wished they could stop using the Internet; 26% of participants felt that their school performance was affected due to being cyber victims (cOR 6.5). The results of the univariate analysis showed a high odd ratio related to school performance and a willingness to leave school. This conclusion indicates the likelihood of these impacts specifically with a significant p-value, as shown in Table ​ Table5 5 .

Uni-variate and multivariate logistic regression analyses showing associations between various variables of adolescent cyberbullying and mental health level

* Reference category, CI Confidence Interval, cOR Crude Odds ratio, aOR Adjusted Odds ratio

In this study, approximately 88% of the participants were cyber victims compared to only 11% of cyberbullying perpetrators who committed this act on their peers. Mental health affection is well-reported in many studies on cyber victims with higher depression rates than cyberbullying perpetrators [ 41 , 42 ]. However, other studies indicate that cyberbullying victims are not the only ones affected; harm is also extended to involve perpetrators. Cyberbullying perpetrators have high-stress levels, poor school performance, and an increased risk of depression and alcohol misuse. Furthermore, research shows that adolescents who were victims or perpetrators of cyberbullying in their adolescence continue to engage in similar behavior into early adulthood [ 43 , 44 ].

Limitations of the study

Although the current study found a high prevalence and positive connections among variables, it should be emphasized that it was conducted on a determinate sample of respondents, 11 to 18 years old. Therefore, the results could not be generalized for other samples, age groups, and communities from other cultures and contexts. In addition, it was limited to adolescent survey responses, did not include parents' and caretakers' viewpoints, and failed to include other risk factors such as divorce and financial status. We believe future studies should consider parents' perspectives and more analysis of perpetrators' characteristics. Moreover, self-reported tools are susceptible to social desirability bias, which can influence test item responses. As a result, future research should employ a variety of monitoring and evaluation metrics and larger potential populations and age ranges. Another limitation of this analysis is that we cannot make conclusive inferences regarding gender and exact prevalence because male adolescents had a lower response rate than female adolescents, suggesting that males might be more sensitive to disclosing these issues.

Even though experts in the social sciences typically research cyberbullying, it is crucial to investigate it from a clinical perspective because it significantly affects mental health. Adolescents' lives have grown increasingly centered on online communication, which provides several possibilities for psychological outcomes and aggressive actions such as cyberbullying. Stress, anxiety, depressive symptoms, suicidal ideation, and deterioration in school performance are all linked to cyberbullying. Therefore, we emphasize the need for parents and educators to be conscious of these dangers and be the first line of protection for the adolescent by recognizing, addressing, and solving this problem. Furthermore, we urge the responsibility of pediatricians, physicians, and psychiatric consultants to create a comfortable atmosphere for adolescents to disclose and report this problem early and raise awareness of the problem in their communities. Furthermore, practical strategies for dealing with such occurrences involving health, education, and law authorities, should be supported to tackle this problem, which can affect the adolescent mentally and academically. Lastly, to decide how to intervene most effectively, more research must be done on the many methods to assess how schools, communities, and healthcare providers tackle cyberbullying.

Acknowledgements

We want to acknowledge the help and appreciate the efforts of the participating students and their guardians during data collection.

Authors’ contributions

GG, EE and AA did the study design, data collection, statistical analysis manuscript writing, editing, revision, approved final manuscript, and responsible for integrity of research.

AR, BH, AF, AH, AQ, and MM contributed in data collection, statistical analysis, manuscript writing, editing, revision, approved final manuscript.

Availability of data and materials

Declarations.

The ethical approval for this study was obtained from The Institute Review Board (IRB) of Jazan University (Letter v.1 2019 dated 08/04/2021). Informed consent was received from all participants, and for participants under age 16, informed consent was obtained from a parent and legal guardian. All methods were carried out under relevant guidelines and regulations.

Not applicable.

The authors state that they have no competing interests.

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Teens and Cyberbullying 2022

Nearly half of u.s. teens have been bullied or harassed online, with physical appearance being seen as a relatively common reason why. older teen girls are especially likely to report being targeted by online abuse overall and because of their appearance, table of contents.

  • Age and gender are related to teens’ cyberbullying experiences, with older teen girls being especially likely to face this abuse
  • Black teens are about twice as likely as Hispanic or White teens to say they think their race or ethnicity made them a target of online abuse
  • Black or Hispanic teens are more likely than White teens to say cyberbullying is a major problem for people their age
  • Roughly three-quarters of teens or more think elected officials and social media sites aren’t adequately addressing online abuse
  • Large majorities of teens believe permanent bans from social media and criminal charges can help reduce harassment on the platforms
  • Acknowledgments
  • Methodology

Pew Research Center conducted this study to better understand teens’ experiences with and views on bullying and harassment online. For this analysis, we surveyed 1,316 U.S. teens. The survey was conducted online by Ipsos from April 14 to May 4, 2022.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, which is an independent committee of experts that specializes in helping to protect the rights of research participants.

Ipsos recruited the teens via their parents who were a part of its  KnowledgePanel , a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey is weighted to be representative of U.S. teens ages 13 to 17 who live with parents by age, gender, race, ethnicity, household income and other categories.

Here are the  questions used for this report , along with responses, and  its methodology .

While bullying existed long before the internet, the rise of smartphones and social media has brought a new and more public arena into play for this aggressive behavior.

impact of cyberbullying research paper

Nearly half of U.S. teens ages 13 to 17 (46%) report ever experiencing at least one of six cyberbullying behaviors asked about in a Pew Research Center survey conducted April 14-May 4, 2022. 1

The most commonly reported behavior in this survey is name-calling, with 32% of teens saying they have been called an offensive name online or on their cellphone. Smaller shares say they have had false rumors spread about them online (22%) or have been sent explicit images they didn’t ask for (17%).

Some 15% of teens say they have experienced someone other than a parent constantly asking them where they are, what they’re doing or who they’re with, while 10% say they have been physically threatened and 7% of teens say they have had explicit images of them shared without their consent.

In total, 28% of teens have experienced multiple types of cyberbullying.

Defining cyberbullying in this report

This report measures cyberbullying of teens using six distinct behaviors:

  • Offensive name-calling
  • Spreading of false rumors about them
  • Receiving explicit images they didn’t ask for
  • Physical threats
  • Constantly being asked where they are, what they’re doing, or who they’re with by someone other than a parent
  • Having explicit images of them shared without their consent

Teens who indicate they have personally experienced any of these behaviors online or while using their cellphone are considered targets of cyberbullying in this report. The terms “cyberbullying” and “online harassment” are used interchangeably throughout this report.

Teens’ experiences with online harassment vary by age. Some 49% of 15- to 17-year-olds have experienced at least one of the six online behaviors, compared with 42% of those ages 13 to 14. While similar shares of older and younger teens report being the target of name-calling or rumor spreading, older teens are more likely than their younger counterparts (22% vs. 11%) to say someone has sent them explicit images they didn’t ask for, an act sometimes referred to as cyberflashing ; had someone share explicit images of them without their consent, in what is also known as revenge porn (8% vs. 4%); or been the target of persistent questioning about their whereabouts and activities (17% vs. 12%).

A bar chart showing that older teen girls more likely than younger girls or boys of any age to have faced false rumor spreading, constant monitoring online, as well as cyberbullying overall

While there is no gender difference in having ever experienced online abuse, teen girls are more likely than teen boys to say false rumors have been spread about them. But further differences are seen when looking at age and gender together: 15- to 17-year-old girls stand out for being particularly likely to have faced any cyberbullying, compared with younger teen girls and teen boys of any age. Some 54% of girls ages 15 to 17 have experienced at least one of the six cyberbullying behaviors, while 44% of 15- to 17-year-old boys and 41% of boys and girls ages 13 to 14 say the same. These older teen girls are also more likely than younger teen girls and teen boys of any age to report being the target of false rumors and constant monitoring by someone other than a parent.

White, Black and Hispanic teens do not statistically differ in having ever been harassed online, but specific types of online attacks are more prevalent among certain groups. 2 For example, White teens are more likely to report being targeted by false rumors than Black teens. Hispanic teens are more likely than White or Black teens to say they have been asked constantly where they are, what they’re doing or who they’re with by someone other than a parent.

There are also differences by household income when it comes to physical threats. Teens who are from households making less than $30,000 annually are twice as likely as teens living in households making $75,000 or more a year to say they have been physically threatened online (16% vs. 8%).

A bar chart showing that older teen girls stand out for experiencing multiple types of cyberbullying behaviors

Beyond those differences related to specific harassing behaviors, older teen girls are particularly likely to say they experience multiple types of online harassment. Some 32% of teen girls have experienced two or more types of online harassment asked about in this survey, while 24% of teen boys say the same. And 15- to 17-year-olds are more likely than 13- to 14-year-olds to have been the target of multiple types of cyberbullying (32% vs. 22%).

These differences are largely driven by older teen girls: 38% of teen girls ages 15 to 17 have experienced at least two of the harassing behaviors asked about in this survey, while roughly a quarter of younger teen girls and teen boys of any age say the same.

Beyond demographic differences, being the target of these behaviors and facing multiple types of these behaviors also vary by the amount of time youth spend online. Teens who say they are online almost constantly are not only more likely to have ever been harassed online than those who report being online less often (53% vs 40%), but are also more likely to have faced multiple forms of online abuse (37% vs. 21%).

These are some of the findings from a Pew Research Center online survey of 1,316 U.S. teens conducted from April 14 to May 4, 2022.

There are numerous reasons why a teen may be targeted with online abuse. This survey asked youth if they believed their physical appearance, gender, race or ethnicity, sexual orientation or political views were a factor in them being the target of abusive behavior online.

A bar chart showing that teens are more likely to think they've been harassed online because of the way they look than their politics

Teens are most likely to say their physical appearance made them the target of cyberbullying. Some 15% of all teens think they were cyberbullied because of their appearance.

About one-in-ten teens say they were targeted because of their gender (10%) or their race or ethnicity (9%). Teens less commonly report being harassed for their sexual orientation or their political views – just 5% each.

Looking at these numbers in a different way, 31% of teens who have personally experienced online harassment or bullying think they were targeted because of their physical appearance. About one-in-five cyberbullied teens say they were targeted due to their gender (22%) or their racial or ethnic background (20%). And roughly one-in-ten affected teens point to their sexual orientation (12%) or their political views (11%) as a reason why they were targeted with harassment or bullying online.

A bar chart showing that Black teens are more likely than those who are Hispanic or White to say they have been cyberbullied because of their race or ethnicity

The reasons teens cite for why they were targeted for cyberbullying are largely similar across major demographic groups, but there are a few key differences. For example, teen girls overall are more likely than teen boys to say they have been cyberbullied because of their physical appearance (17% vs. 11%) or their gender (14% vs. 6%). Older teens are also more likely to say they have been harassed online because of their appearance: 17% of 15- to 17-year-olds have experienced cyberbullying because of their physical appearance, compared with 11% of teens ages 13 to 14.

Older teen girls are particularly likely to think they have been harassed online because of their physical appearance: 21% of all 15- to 17-year-old girls think they have been targeted for this reason. This compares with about one-in-ten younger teen girls or teen boys, regardless of age, who think they have been cyberbullied because of their appearance.

A teen’s racial or ethnic background relates to whether they report having been targeted for cyberbullying because of race or ethnicity. Some 21% of Black teens report being made a target because of their race or ethnicity, compared with 11% of Hispanic teens and an even smaller share of White teens (4%).

There are no partisan differences in teens being targeted for their political views, with 5% of those who identify as either Democratic or Republican – including those who lean toward each party – saying they think their political views contributed to them being cyberbullied.

In addition to measuring teens’ own personal experiences with cyberbullying, the survey also sought to understand young people’s views about online harassment more generally.

impact of cyberbullying research paper

The vast majority of teens say online harassment and online bullying are a problem for people their age, with 53% saying they are a major problem. Just 6% of teens think they are not a problem.

Certain demographic groups stand out for how much of a problem they say cyberbullying is. Seven-in-ten Black teens and 62% of Hispanic teens say online harassment and bullying are a major problem for people their age, compared with 46% of White teens. Teens from households making under $75,000 a year are similarly inclined to call this type of harassment a major problem, with 62% making this claim, compared with 47% of teens from more affluent homes. Teen girls are also more likely than boys to view cyberbullying as a major problem.

Views also vary by community type. Some 65% of teens living in urban areas say online harassment and bullying are a major problem for people their age, compared with about half of suburban and rural teens.

Partisan differences appear as well: Six-in-ten Democratic teens say this is a major problem for people their age, compared with 44% of Republican teens saying this.

In recent years, there have been several initiatives and programs aimed at curtailing bad behavior online, but teens by and large view some of those behind these efforts – including social media companies and politicians – in a decidedly negative light.

A bar chart showing that large majorities of teens think social media sites and elected officials are doing an only fair to poor job addressing online harassment

According to teens, parents are doing the best of the five groups asked about in terms of addressing online harassment and online bullying, with 66% of teens saying parents are doing at least a good job, including one-in-five saying it is an excellent job. Roughly four-in-ten teens report thinking teachers (40%) or law enforcement (37%) are doing a good or excellent job addressing online abuse. A quarter of teens say social media sites are doing at least a good job addressing online harassment and cyberbullying, and just 18% say the same of elected officials. In fact, 44% of teens say elected officials have done a poor job addressing online harassment and online bullying.

Teens who have been cyberbullied are more critical of how various groups have addressed online bullying than those who haven’t

impact of cyberbullying research paper

Teens who have experienced harassment or bullying online have a very different perspective on how various groups have been handling cyberbullying compared with those who have not faced this type of abuse. Some 53% of teens who have been cyberbullied say elected officials have done a poor job when it comes to addressing online harassment and online bullying, while 38% who have not undergone these experiences say the same (a 15 percentage point gap). Double-digit differences also appear between teens who have and have not been cyberbullied in their views on how law enforcement, social media sites and teachers have addressed online abuse, with teens who have been harassed or bullied online being more critical of each of these three groups. These harassed teens are also twice as likely as their peers who report no abuse to say parents have done a poor job of combatting online harassment and bullying.

Aside from these differences based on personal experience with cyberbullying, only a few differences are seen across major demographic groups. For example, Black teens express greater cynicism than White teens about how law enforcement has fared in this space: 33% of Black teens say law enforcement is doing a poor job when it comes to addressing online harassment and online bullying; 21% of White teens say the same. Hispanic teens (25%) do not differ from either group on this question.

Teens have varying views about possible actions that could help to curb the amount of online harassment youth encounter on social media.

A bar chart showing that half of teens think banning users who bully or criminal charges against them would help a lot in reducing the cyberbullying teens may face on social media

While a majority of teens say each of five possible solutions asked about in the survey would at least help a little, certain measures are viewed as being more effective than others.

Teens see the most benefit in criminal charges for users who bully or harass on social media or permanently locking these users out of their account. Half of teens say each of these options would help a lot in reducing the amount of harassment and bullying teens may face on social media sites.

About four-in-ten teens think that if social media companies looked for and deleted posts they think are bullying or harassing (42%) or if users of these platforms were required to use their real names and pictures (37%) it would help a lot in addressing these issues. The idea of forcing people to use their real name while online has long existed and been heavily debated: Proponents see it as a way to hold bad actors accountable and keep online conversations more civil , while detractors believe it would do little to solve harassment and could even  worsen it .

Three-in-ten teens say school districts monitoring students’ social media activity for bullying or harassment would help a lot. Some school districts already use digital monitoring software to help them identify worrying student behavior on school-owned devices , social media and other online platforms . However, these programs have been met with criticism regarding privacy issues , mixed results and whether they do more harm than good .

A chart showing that Black or Hispanic teens more optimistic than White teens about the effectiveness of five potential solutions to curb online abuse

Having personally experienced online harassment is unrelated to a teen’s view on whether these potential measures would help a lot in reducing these types of adverse experiences on social media. Views do vary widely by a teen’s racial or ethnic background, however.

Black or Hispanic teens are consistently more optimistic than White teens about the effectiveness of each of these measures.

Majorities of both Black and Hispanic teens say permanently locking users out of their account if they bully or harass others or criminal charges for users who bully or harass on social media would help a lot, while about four-in-ten White teens express each view.

In the case of permanent bans, Black teens further stand out from their Hispanic peers: Seven-in-ten say this would help a lot, followed by 59% of Hispanic teens and 42% of White teens.

  • It is important to note that there are various ways researchers measure youths’ experiences with cyberbullying and online harassment. As a result, there may be a range of estimates for how many teens report having these experiences. In addition, since the Center last polled on this topic in 2018, there have been changes in how the surveys were conducted and how the questions were asked. For instance, the 2018 survey asked about bullying by listing a number of possible behaviors and asking respondents to “check all that apply.” This survey asked teens to answer “yes” or “no” to each item individually. Due to these changes, direct comparisons cannot be made across the two surveys. ↩
  • There were not enough Asian American teen respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout the report. ↩

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IMAGES

  1. Research Paper On Cyber Bullying

    impact of cyberbullying research paper

  2. (PDF) The Consequences Of Cyberbullying On The Mental Health Of High

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COMMENTS

  1. Cyberbullying Among Adolescents and Children: A Comprehensive Review of the Global Situation, Risk Factors, and Preventive Measures

    Although cyberbullying is still a relatively new field of research, cyberbullying among adolescents is considered to be a serious public health issue that is closely related to adolescents' behavior, mental health and development (16, 17). The increasing rate of Internet adoption worldwide and the popularity of social media platforms among the ...

  2. Cyberbullying and its influence on academic, social, and emotional

    A research, of 187 undergraduate students matriculated at a large U.S. Northeastern metropolitan Roman Catholic university (Webber and Ovedovitz, 2018), found that 4.3% indicated that they were victims of cyberbullying at the university level and a total of 7.5% students acknowledged having participated in bullying at that level while A survey ...

  3. Full article: Current perspectives: the impact of cyberbullying on

    Abstract. Cyberbullying has become an international public health concern among adolescents, and as such, it deserves further study. This paper reviews the current literature related to the effects of cyberbullying on adolescent health across multiple studies worldwide and provides directions for future research.

  4. Cyberbullying and its impact on young people's emotional health and

    The impact of cyberbullying on emotional health and well-being. Research consistently identifies the consequences of bullying for the emotional health of children and young people. Victims experience lack of acceptance in their peer groups, which results in loneliness and social isolation.

  5. PDF Youth and Cyberbullying: Another Look

    This paper presents an aggregation and summary of recent, primarily academic literature on youth (12-18-year-olds) and cyberbullying. It is important to note that this spotlight is not intended to stand alone. Rather, it seeks to serve as a brief update of a rich corpus of research literature on cyberbullying and is meant as an addendum to

  6. Problematic social media use mediates the effect of cyberbullying

    Results from the sequential binary mixed effects logit models showed that adolescents who were victims of cyberbullying were 2.39 times significantly more likely to report psychosomatic complaints ...

  7. Cyberbullying research

    The bibliometric papers (López-Meneses et al., 2020, ... In addition, this bibliometric analysis has highlighted the impact of cyberbullying research on addressing sustainable goals. This relationship between Cyberbullying and sustainable goals per se has never been investigated before, thereby opening up an array of research studies that can ...

  8. (PDF) Cyberbullying: A Review of the Literature

    cyberbullying, in which individuals or groups of individuals use the media to inflict emotional distress on. other individuals (Bocij 2004). According to a rece nt study of 743 teenager s and ...

  9. PDF CYBER BULLYING AND ACADEMIC PERFORMANCE

    Bullying is a form of peer aggression which can be as damaging as any form of conventional aggression (Mickie, 2011). The problem investigated in this research concerns cyber bullying that disturbs university students psychologically and emotionally. Bullying also prevents students from achieving good grades.

  10. Qualitative Methods in School Bullying and Cyberbullying Research: An

    School bullying research has a long history, stretching all the way back to a questionnaire study undertaken in the USA in the late 1800s (Burk, 1897).However, systematic school bullying research began in earnest in Scandinavia in the early 1970s with the work of Heinemann and Olweus ().Highlighting the extent to which research on bullying has grown exponentially since then, Smith et al. found ...

  11. Prevalence and related risks of cyberbullying and its effects on

    Approximately 20% of the participants spend more than 12 h daily on the Internet, and the estimated overall prevalence of cyberbullying was 42.8%, with the male prevalence slightly higher than females. In addition, 26.3% of the participants were significantly affected in their academic Performance due to cyberbullying.

  12. PDF REFEREED ARTICLE The Effects of Cyberbullying on Students and Schools

    The Effects of Cyberbullying on Students and Schools Cyberbullying is a serious problem that must be addressed in schools. Cyberbullying is a form of bullying that has become more prevalent as technology advances, and it is difficult to escape from. Cyberbullying is similar to bullying in that it is repeated harm, but it comes in the

  13. Cyberbullying and its influence on academic, social, and emotional

    Psychological impact of cyberbullying. ... The Revised Cyber Bullying Survey (RCBS), with a Cronbach's alpha ranging from .74 to .91 ... Contributed reagents, materials, analysis tools or data; Wrote the paper. Funding statement. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit ...

  14. CYBER BULLYING: CAUSES, PSYCHOLOGICAL IMPACT AND REMEDIES

    research. Cyberbullying can cause fear, low self-esteem, social isolation, bad academic performan ce. It can also cause difficulty in crea ting healthy relationships and most importantly, victims ...

  15. Cyberbullying on social networking sites: A literature review and

    1. Introduction. Cyberbullying is an emerging societal issue in the digital era [1, 2].The Cyberbullying Research Centre [3] conducted a nationwide survey of 5700 adolescents in the US and found that 33.8 % of the respondents had been cyberbullied and 11.5 % had cyberbullied others.While cyberbullying occurs in different online channels and platforms, social networking sites (SNSs) are fertile ...

  16. (PDF) Cyberbullying and its influence on academic, social, and

    This study investigated the influence of cyberbullying on the academic, social, and emotional development of undergraduate students. It's objective is to provides additional data and understanding ...

  17. Cyberbullying in High Schools: A Study of Students' Behaviors and

    Their preliminary analysis showed that 20% of the students reported they were cybervictims and 10% were cyberbullies. Text messaging over mobile phones was the most common medium used for cyberbullying. Research studies have indicated that cyberbullying is becoming a major issue in schools and has various negative effects.

  18. Frontiers

    Although cyberbullying is still a relatively new field of research, cyberbullying among adolescents is considered to be a serious public health issue that is closely related to adolescents' behavior, mental health and development (16, 17). The increasing rate of Internet adoption worldwide and the popularity of social media platforms among the ...

  19. Teens and Cyberbullying 2022

    Nearly half of U.S. teens ages 13 to 17 (46%) report ever experiencing at least one of six cyberbullying behaviors asked about in a Pew Research Center survey conducted April 14-May 4, 2022. 1. The most commonly reported behavior in this survey is name-calling, with 32% of teens saying they have been called an offensive name online or on their ...

  20. Effects of Cyber Bullying on Teenagers: a Short Review of Literature

    This paper has implications for educational policy and practice, including steps school leaders can take to curtail cyberbullying. Originality/value This paper builds on a small body of research ...

  21. Bullying in schools: the state of knowledge and effective interventions

    Abstract. During the school years, bullying is one of the most common expressions of violence in the peer context. Research on bullying started more than forty years ago, when the phenomenon was defined as 'aggressive, intentional acts carried out by a group or an individual repeatedly and over time against a victim who cannot easily defend him- or herself'.

  22. PDF The Impact of School Bullying On Students' Academic Achievement ...

    Bullying affects student's academic achievement in various ways. Ammermueller (2012) found that being bullied has a significantly negative impact on present and future students' performance in school Brank et al. (2012) indicated that bullying victims are weak, shy, and anxious. They added that victims' performance is poor in school and ...

  23. (PDF) Understanding the Influence of Cyberbullying Among ...

    This research sought to explore the effects of cyberbullying on high school students attending public schools in the Philippines. Given the widespread use of digital communication platforms and ...