online news literature review

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Media literature review guide: How to conduct a literature review of news sources

On this page, first steps: questions to ask, key things to keep in mind, sources by media type, online news, news websites, archival websites, library subscription news sources, archived broadcast/tv news, original audio-visual broadcasts, broadcast news transcripts, searching news sources methodically, how to do news content analysis, saving, exporting, and citing news from websites/databases, additional research help, related sfu resource guides.

Use this guide if you are conducting a literature review of news sources on a certain topic, and need help locating a sample of news sources for your analysis.  For example:

• BC newspaper articles covering RCMP sexual harassment claims over the past ten years
• Newspaper, online news website and TV news stories reporting on the marijuana 4/20 event in 2007 and later in 2017.

Ask yourself:

• Geography: Are there any geographic parameters to your news search (e.g., specific city or cities, provinces or countries of news sources)?
• Time frame: Are you searching within a specific time range, or at least before or after a particular day?
• News format: What news media types are you interested in (online news content, newspapers, etc.)?

A few things key things to keep in mind:

• Older newspaper content (1990s and earlier) may not be digitized yet.  A common exception is the digitization of much older newspaper content, such as the Globe and Mail Canada , which provides coverage from 1844. There is often a large digitization gap between the historical content and more recent news content. Alternative access may be available through SFU Library's microfilm collection.
• There are both free and library subscription news archives available. There is often overlapping coverage between the free and subscription sources.
• SFU library subscribes to several online news sources, also known as "subscription news sources", which may provide more reliable and comprehensive archival content.

Examples: CBC , Vancouver Sun , The Wall Street Journal

You can go directly to a commercial news website and search the site for its news archives.

A few things to note about general news websites:

• Archival content is limited and not comprehensive
• Extent of archival coverage is usually unknown/undocumented
• Links to older news stories may come and go, and older versions may have been edited
• Links may be unstable
• Bonus: Associated images are typically archived along with the article

Example: The Internet Archive

You may be able to obtain archival content through third party websites, which independently and intermittently scrape commercial web content for archiving.

Notes on archival websites:

• The Internet Archive scrapes a vast amount of web content for archiving (about 286 billion + web pages). Paste the newspaper's URL (e.g., http://vancouversun.com/) into the search bar to see which news web pages have been archived.
• Note that the Internet Archive only archives a sample of pages from news websites, and therefore does not provide complete historical coverage of a news source.
• View the  News & Public Affairs section of the Internet Archives for new collections by topic (e.g., "The Iraq War Collection"). Collections are primarily American.

Examples: Canadian Newsstream , CBCA , Factiva

SFU Library subscribes to a number of news databases which systematically archive news sources from both traditional print newspapers, and online and other media news sources. 

Notes on subscription news sources:

• Offers a much more comprehensive searching of backfiles; extent of historical coverage explicitly outlined
• Can search multiple news sources at once by various filters, for instance, all Canadian newspapers
• Smaller Canadian newspapers can be included in your search (e.g., Burnaby Now)
• Many of the articles found in these specialized databases will also show up in a general library catalogue search. However, going to the directly to the subscription database allows for much more targeted searching
• Some of these databases also archive scholarly journals, so be sure to set your search limiters so that newspapers are in your results
• Note: Original images as published in situ may be available in newspaper microform
• Note that you may find multiple versions of one article found across different news sources. This relates to how press releases are distributed, as well as how media conglomerations share and modify content.
• Some of these databases are more complicated to use , but offer a more powerful and robust search in exchange for your efforts

Examples: CTV National News, The National with Peter Mansbridge, PBS Newshour

• It is very challenging to find older, archived broadcast/TV news, as publicly available archival sources are limited.
• Some news archives focus on news originally broadcast through cable television , while others aim to capture news stories broadcast on the internet (" born digital ").
• Television companies may have their own private archives of news footage, not readily available to the public.
• SFU Library does not currently have a subscription to any broadcast news archives.

Some resources:

The  Internet Archive's TV News archive includes extensive archived video material, mostly from the US. Advanced search by news program and network is available. Keyword searching searches closed captions. Coverage begins around 2009.

CBC Archives incorporates news, images, and audio files from across Canada in its extensive archives. Select items are exhibited on a changing basis. Coverage may include news stories, such as 1993: World Trade Centre Bombed . Users can also explore the  CBC Archives Sales  website for items to purchase.

Vanderbilt News Archive is a searchable, private database of broadcast news, but is unfortunately not free nor available through SFU library. Materials may be loaned, arriving through the mail in a hardcopy format.

YouTube It's possible a particular news broadcast was uploaded to YouTube.

Transcripts may be available from prior broadcast news stories. These are a possible alternative to finding the original broadcast in audio-visual format.

The following SFU databases contain some transcripts.

Canadian Business & Current Affairs Database Under "Document type", select "transcript". Run a search and then narrow by source and add keywords. Extensive transcripts are available for The National (CBC television), Canada AM (CTV television), and others.

Film & Television Literature Index with Full Text

Canadian Newsstream Under "Document type", select "transcript". Run a search and then narrow by source and add keywords. Extensive transcripts are available for The National (CBC television), Canada AM (CTV television), and others.

​Nexis Uni Includes transcripts from about 123 (mostly American) news broadcasts such as ABC, BBC, NPR, Fox News Network, and CNN.

Factiva (see image below for search instructions) An international collection of news broadcast transcripts in a variety of languages.

Finding transcripts in Factiva:

• 1. Expand the option of searching Sources , by clicking on the small arrow next to that word. This will open up a drop-down menu with the option to select source category By Type . Choose this option. 2. Transcripts will appear as an option. Expand this category to see the option of Transcripts: Broadcast.

Google News  will find articles related to your topic from a variety of sources.

• The scope of Google's news coverage, while appealingly very broad , is also very unknown . This significantly limits efforts toward systematic searching.
• Advanced search allows you to search by particular news source or web domain . For instance, you can run a search for Vancouver Sun or site:vancouversun.com for the web site
• News trends can be found under the "Top Stories" section. 

It's effective to plan your search before you tackle the databases and to track the databases you search, as well as the terms that you use. Follow these steps for effective research;

Write down a sentence describing the topic of your search

​ Compared to corporate media, alternative media offers vastly different frames on the impact climate change has on jobs within the petroleum industry.

Identify the key concepts in your topic

Compared to corporate media, alternative media offers vastly different frames on the impact climate change has on jobs within the petroleum industry .

Brainstorm synonyms or related terms for these key concepts

• You may need to do some background reading to identify pertinent terminology.
• Group the terms that relate to one of your key concepts. Your key concepts can be as specific as corporate names or as broad as the industry. Keep adding or deleting key concepts as you search.

Track research: search terms, search expressions, databases

Track the terms that you use to search, using an Excel spreadsheet or other record, grouping them by concept, noting definitions. As you find literature, you will add to this list of terms.

Select an appropriate database for your search

• Are you researching coverage in "mainstream" sources? Or are you looking for coverage from an "alternative" perspective?
• What is the scope of the specific news database? Does it provide geographical and chronological coverage suitable for your search? Do all the news source have to be Canadian? If YES, you might consider whether you should limit at the outset or when evaluating your results.
• Many databases enable you to include a publication date range, in order to focus your search on a specific time period.

Review your search results

• Analyze your results in order to assess and modify your search terms or search statement.
• You can use the database limiters to scope your results according to subject, publication, etc. For example, focus on the news before and after a pivotal event, by time period, by figuring out the correct terminology, and so on.

Consider whether you need to focus your search, by date, by publication, or other parameters

• TIP: If you are receiving too many off-topic results, try searching for your keywords in just the article title field.
• Similarly, consider whether your research would be improved by concentrating on particular types of news stories, such as editorials, opinion, columns, sports, etc.

Capture your results, either by emailing them to yourself or saving to a file

You will need to support your nomination with documentation of your research.

And, of course, watch out for signs of fake news.

• The content analysis guidebook
• Newswatcher's guide to content analysis
• Sage Research Methods Online. A vast research portal on research methodology. 

There are a number of free citation management software and tools available for students through SFU. Use one of the citation managers  to export and save articles. When you are looking at articles found through the SFU database, there will be options to "save" the article through citation managers such as Mendeley or Zotero.

Depending on which citation style you are using, the SFU citation & style guides explain how to cite news articles and other document types.

Ask a Librarian

See News resources: Finding newspaper articles and newspapers  to help find newspaper articles and newspapers.

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Article Contents

Interactive features of online newspapers: identifying patterns and predicting use of engaged readers, literature review, discussion and conclusions, about the author.

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Deborah S Chung, Interactive Features of Online Newspapers: Identifying Patterns and Predicting Use of Engaged Readers, Journal of Computer-Mediated Communication , Volume 13, Issue 3, 1 April 2008, Pages 658–679, https://doi.org/10.1111/j.1083-6101.2008.00414.x

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This study seeks to identify 1) categories of interactivity, which are promoted through different types of interactive features, 2) patterns of online newspaper readers’ uses of interactive features, and 3) factors, if any, that predict the use of different types of interactive features. Based on an online survey of 542 respondents, four categories of interactive features were identified. Findings show that interactive features are generally used infrequently, especially the features that facilitate human-to-human communication and the features that allow audiences to express their views. Regression analyses show that different user characteristics and backgrounds predict the use of specific types of interactive features. This study illustrates that news organizations need not worry about applying all types of interactive features to engage their readers as the features serve distinct functions. Instead, news organizations should focus on building credibility and may seek to identify their online news audiences and then subsequently provide interactive features accordingly. (152)

The potential of “interactivity” has fueled extraordinary anticipation over the adoption of a two-way communication model in the news industry. Interactivity, a quality of new media and the Internet in particular, fundamentally challenges the traditional one-way directional flow of news by providing news audiences with increased choice options and even allowing them to participate in the production of information. Interactivity, thus, has the potential to transform the nature of traditional journalism practice through online news. Journalism is at a crossroads with its content and form evolving daily through multimedia platforms and numerous hyperlinks that easily allow readers to select stories. Most importantly, online newspapers are encouraged to share their control of news presentation with the audience by allowing increased communication among readers, promoting back-and-forth conversations between newsroom personnel and the audience, and providing opportunities for personalized journalism.

While online news publications and their application of interactivity have been scrutinized ( Li, 1998 ; Massey & Levy, 1999 ; Schultz, 1999 ; Chan-Olmsted & Park, 2000 ; Chung, 2004 ), little has been done to analyze and categorize the various interactive features that promote interactivity. Present studies also fail to illuminate the extent to which news audiences engage in interactivity, and the characteristics and backgrounds of online news audiences who engage themselves with online newspapers are absent in the literature. With information gathered through an online survey of readers from a local online newspaper, this study attempts to identify 1) categories of interactivity, which are promoted through different types of interactive features, 2) online newspaper readers’ uses of different types of interactive features, and 3) factors, if any, that predict the use of different categories of interactive features.

The (R)Evolution of News—From Print to Online

The newspaper is one of the oldest elements of contemporary media ( Boczkowski, 2004 ) and has long been considered the primary model of information delivery ( McQuail, 1994 ). Today there exist over 1,400 newspapers representing a $55 billion industry ( Newspaper Association of America, 2007 ). Yet there has long been concern with the top-down communication model of newspapers. Critics contend that the mass media have imposed a one-way discussion and argue that they largely produce messages independently from news audiences ( Habermas, 1962 ; Schultz, 1999 ). Critics also note the lack of intense political discussion and citizen dialogue opportunities available through traditional media channels ( Barber, 1984 ; Habermas, 1996 ). The civic journalism movement, which flourished in the 90s, is based on such discontent with the news industry. This movement attempted to reconnect with communities by actively communicating with the news audiences and favoring issues important to ordinary citizens ( Rosen, 1992 ; Charity, 1995 ; Merritt, 1998 ). Civic journalism today has further grown into community or participatory journalism that promotes interactive engagement between newsrooms and their communities ( Bowman & Willis, 2003 ).

The Internet is only the latest to challenge traditional news delivery methods. However, the implications of its influences are profound. While traditional news media have delivered information through a top-down, centralized model with journalists functioning as gatekeepers of information, online news media present greater opportunities for control and ownership as users assume more active roles in their news consumption experiences.

The popularity of online newspapers ( Annual Report on American Journalism, 2006 ) can be attributed to the interactive quality of the Internet. Journalists expect to bring people “closer to the news” by adopting interactivity in their online presentation of the news ( Brown, 2000 ). The immediate back-and-forth communication is a new quality in the relationship between news publications and their audiences, and research suggests that online readers find this interaction valuable ( Pew Research Center for the People and the Press, 1999 ). The Digital Journalism Credibility Study (2003) claims that the interactive nature of e-mail links, chats and message boards are the essence of the medium and has the potential to recreate community. In general, interactivity is considered to be a positive characteristic of new technologies. Rafaeli (1988) writes that the consequences of interactivity are satisfaction, motivation, sense of fun, cognition and learning.

Interaction, Interactivity and Interactive Features on Online News Publications

Interactivity stems from the sociological concept of interaction where it is defined as the relationship between two or more people, who, in a given situation, mutually adjust their behaviors and actions to each other ( Jensen, 1998 ). Duncan (1989) refers to interaction as the state of reciprocal awareness. In the last 10–15 years the concept of interaction has found its way into the discussion of new communication technologies, such as the Internet. Communication scholars have, thus, discussed interaction online as “interactivity.” Many communication researchers use face-to-face communication as the standard of interactivity and evaluate the interactivity offered by websites based on how closely it resembles such communication ( Walther & Burgoon, 1992 ). While interactivity itself is not equivalent to interaction, the ideal of interactivity, however, has been discussed through the framework of “the conversational ideal ( Schudson, 1978 ).” Some scholars have challenged this movement ( Rafaeli, 1988 ), yet the numerous definitional models offered for interactivity suggest that “the conversational ideal” is still an ideal to embrace.

Thus far, interactivity has been discussed through various definitional models ( Bordewijk & van Kamm, 1986 ; Rogers, 1986 ; Rafaeli, 1988 , Rafaeli & Sudweeks, 1997 ; Heeter, 1989 ; Steuer, 1995 ; Kiousis, 2002 ). While there are numerous definitions of interactivity that cover considerable ground, a helpful way that scholars have conceptualized interactivity is through the distinction of medium interactivity from human interactivity ( Outing, 1998 ; Lee, 2000 ; Stromer-Galley, 2000 , 2004 ; Bucy, 2004 ; Chung, 2007 ). Medium interactivity, also known as user-to-system/document or content interactivity, is interactive communication between users and technology that is based on the nature of the technology itself and what the technology allows users to do. Human interactivity , also known as user-to-user or interpersonal interactivity, on the other hand, is communication between two or more users that takes place through a communication channel. Stromer-Galley (2000) considers human interactivity to be more interactive than medium interactivity because she finds this kind of interaction to be the foundation for public deliberation.

In this study interactivity is defined as a multi-dimensional construct that is on a continuum of medium to human interactivity. While interactivity is represented on a continuum, the categories of interactivity are manifested through various different forms of interactive features that fall on that continuum. For example, features representing medium interactivity solely rely on the technology to allow users to exert control and are considered as lower levels of interactivity. Interactive features that characterize medium interactivity include send-article-to-friend options, audio and video downloads and photo galleries.

Those features that utilize characteristics of medium interactivity and also allow partial human-to-human communication are considered middle-ground interactive features. This category exemplifies the medium-to-human interactivity continuum and resides between the two extremes of medium interactivity and human interactivity. These features, that allow customization options, provide the means for users to tailor information to their liking and/or share and express their views, but these features generally do not support the exchange of ideas. Thus, interactive features that represent this category include information customization features—such as weather and topic customization—and content submission features—such as news tips, news stories and photo submissions—and polls.

Finally, features that promote human interactivity that facilitate user-to-user mutual communication, or interpersonal communication, are considered as higher levels of interactivity. These features can be characterized through e-mail links, message boards and chat features. Using these features require more effort in that individuals must do more than clicking or selecting in order to actively use them. Human interactivity is considered to contribute to the key distinction between traditional forms of news delivery and online news in that the audience, if desired, can participate as active agents through interpersonal communication.

Deuze (2003) also translates interactivity dimensions for the design of news websites into navigational interactivity, adaptive interactivity and functional interactivity. Navigational interactivity, like medium interactivity, allows users to “navigate” a site with hyperlinks and menu bars. Adaptive interactivity, much like the blending of medium and human interactivity, allows the users’ experiences to have consequences on site content. Functional interactivity, much like human interactivity, allows users to communicate with other individuals. The interactivity model suggested for the current study, thus, builds on this conceptualization of interactivity of online news publications.

In this study, the first aim is to examine whether indeed certain interactive features offered by newspaper websites share common ground and if these interactive features serve distinct functions. Thus, the following research question is posed:

RQ1: What categories of interactive features exist on online newspapers?

Interactivity on Online News Publications and Online News Audiences

Outing (1998) says for a news site to be truly interactive it must facilitate communication between humans through human interactive features. He argues that the Internet is a two-way medium, and for sites to excel at “interactivity,” they must bring people together and promote communication among Web users as well as communication between Web users and Web staff members and managers.

A number of studies have thus attempted to examine online newspapers’ use of both human and medium interactive features. However, the majority of these studies have discovered that online news publications have failed to fully take advantage of the unique characteristics of the Internet ( Schultz, 1999 ; Massey & Levy, 1999 ; Chan-Olmsted & Park, 2000 ; Kenney, Gorelik & Mwangi, 2000 ; Rosenberry, 2005 ; Ye & Li, 2006 ). Positive accounts of news publications’ implementation of interactivity with increasing sophistication in form and content over time exist ( Chung, 2004 ; Salwen, 2005 ; Greer & Mensing, 2006 ). Overall, however, this body of literature, which is predominantly based on the U.S. media environment, overwhelmingly continues to find that the use of various interactive features among online news sties is largely limited. Because of these findings, online news publications have been heavily criticized for the lack of exploitation of interactivity—especially human interactivity that encourages dialogic communication.

While studies suggest that news consumers welcome the interactive nature of online news and particularly value features that facilitate two-way communication ( Pew Research Center for the People and the Press, 1999 ), little is known about how online news audiences make use of the various interactive features offered by news publications. There exists a significant push for online publications to adopt interactivity, but how news audiences are using interactive features has not been examined in depth. What little we do know about online news audiences’ use of interactive features comes from studies reported by the Pew Research Foundations. The Pew Research Center for the People and the Press (2006) mentions online news audiences’ uses of search engines, news updates and alerts. Recent reports by the Pew Internet & American Life Project also briefly inform on the uses of online news videos (2007a) and posting comments to online news groups (2007b) . Yet the extent of what we know about online news readers and their uses of interactive features stops here, and little effort has been made to further examine online newspaper consumers’ experiences with interactivity.

Based on the dearth of information about online news audiences and their experiences of interactivity with online newspapers, this study also assesses how frequently online news audiences are making use of various types of interactive features.

RQ2: To what extent are online newspaper audiences making use of different types of interactive features as identified by RQ1?

User Characteristics and Backgrounds

Over the years, studies have offered an increasingly sophisticated portrait of the Internet news audience— who they are, what uses they make of online news sites, and why they visit online news sites ( Jeffres & Atkin, 1996 ; Hwang & He, 1999 ; Althaus & Tewksbury, 2000 ; Ferguson & Perse, 2000 ; Papacharissi & Rubin, 2000 ; Pew Internet & American Life Project, 2003 ; Pew Research Center for the People and the Press, 2004 , 2006 ). However, a discussion of online news audiences’ uses of interactive features and lifestyle associations are almost completely absent in the literature. In this study, user characteristics, attitudes and behaviors of news consumers are examined to find relationships between uses of various interactive features.

RQ3: What user attributes, if any, are associated with the use of different types of interactive features as identified by RQ1?

While audience characteristics related to uses of interactive features have not been examined, audience characteristics related to general online use, such as length of online experience, perceived Internet skill level and perceived media credibility, have been studied by researchers.

Studies have suggested that those who have been online longer have greater online know-how ( UCLA Internet Report, 2003 ) and have developed better strategies for finding information online ( Peterson, 1999 ). The Stanford Institute Study ( Nie & Ebring, 2000 ) found that people who spent more years online engage in more online activities. Johnson and Kaye (2004) found that number of years users have been online is positively correlated with Internet proficiency. These findings suggest that these individuals may feel more comfortable exploring newer ways to experience news through various interactive features.

H1: Years online will be a positive predictor of use of interactive features as identified by RQ1.

Researchers have also found that one’s level of perceived online skill level can influence how often the Internet is used ( Ferguson & Perse, 2000 ; UCLA Internet Report, 2003 ). Eastin and LaRose (2000) report that Internet self-efficacy, or “the belief in one’s capabilities to organize and execute courses of Internet actions required to produce given attainments,” was significantly related to Internet use. These findings suggest that one’s level of perceived online skill level may further influence various interactive feature usage.

H2: Perceived Internet skill level will be a positive predictor of use of interactive features as identified by RQ1.

In addition, past credibility studies suggest that perceived credibility of a medium is strongly related to how often one uses it ( Wanta & Hu, 1994 ; Carter & Greenberg, 1965 ; American Society of Newspaper Editors, 1985 ) and thus may further influence how one may engage in experiencing news through various forms of interactive features.

H3: Perceived credibility of online news will be a positive predictor of use of interactive features as identified by RQ1.

Furthermore, studies report connections between online and offline activity. Some studies have found that a relationship exists between civic engagement and political activity offline and Internet use. For example, recent studies have found that Internet use was positively associated with community engagement ( Katz, Rice & Aspen, 2001 ; Shah, McLeod, & Yoon, 2001 ; Shah, Kwak, Holbert, 2001 ; Shah et al., 2005 ). Specifically, the use of expressive features of the Internet contribute to greater civic involvement ( Shah et al, 2001 ; Shah et al., 2005 ).

Katz et al. (2001) found that Internet users were more likely than nonusers to engage in traditional political activity in the 1996 general election. Weber and Bergman (2001) also found that individuals who engaged in online activities, such as using chat-rooms, were more likely to be engaged in a variety of political activities. These associations seem likely in that politically engaged individuals are concerned with the free exchange of ideas and dialogic discussion as expressed by the foundations of a democratic society. Additionally, the Annual Report on American Journalism (2005) found strong correlations that indicated heavy users of the Internet also spend more time with newspapers, magazines and television than do medium or light Internet users, which suggest that “People bring to the Internet the activities, interests and behaviors that preoccupied them before the Web existed ( Pew Internet & American Life Project, 2005 , p. 58).” Thus, it might be reasonable to suggest that civic involvement and political engagement offline may have an impact on online activity, and thus, interactive feature usage.

H4: Civic involvement will be the strongest positive predictor of use of interactive features that facilitate the expression of ideas as identified by RQ1. H5: Political engagement will be the strongest positive predictor of use of interactive features that facilitate interpersonal communication as identified by RQ1.

To answer the above research questions and hypotheses, a Web-based survey was employed. Participants were recruited via online advertisements on an online newspaper in a medium-sized Midwestern city in the U.S. The survey was linked to the teaser, which was placed on the homepage of the participating online newspaper. The average weekly circulation of the newspaper was 42,672. In an attempt to increase response rate, the initial window after the link was clicked informed participants that upon completion of the survey, they would be automatically entered into a drawing for a prize. The link then led the survey participants to the Informed Consent form, which then made the link to the actual survey available at the bottom of the page. The survey was posted for roughly three weeks in the summer of 2005. E-mail and IP address information were collected in order to avoid duplicate submissions. The survey consisted of a self-administered questionnaire that asked respondents regarding their frequency of use of specific interactive features that represent interactivity. In addition to basic demographic questions, the questionnaire also assessed attitudinal measures of respondents’ perceived Internet skill level and perceived level of credibility of online news. Behavioral measures of community involvement and political engagement activities were also measured. The final sample size was 542 in which the survey completion rate was 77 percent.

Dependent Measures Scale Creation

Respondents were asked to indicate how frequently they use 22 unique interactive features generally offered by online newspapers. Features were selected based on prior interactivity studies that examined the use of distinct interactive features, particularly on news sites ( Massey & Levy, 1999 ; Schultz, 1999 ; Chan-Olmsted & Park, 2000 ; Greer & Mensing, 2006 ) but also political candidate homepages ( Stromer-Galley, 2000 , 2002 ), health communication sites ( McMillan, 2002 ; Noar et al., 2006 ) and business/marketing sites ( Ha & James, 1998 ; Aikat, 2000 ). This compilation of features offers a near exhaustive list of features that were offered at the time in which the survey instrument was developed. It also covers a wide range of interactivity, from features that encourage user control and choice options to features that facilitate customization of content and interpersonal communication based on the review of the literature. The response scale ranged from 1 (never) to 4 (frequently). These 22 items were then factor analyzed to identify specific interactivity categories. After assessing reliability of the factors (Cronbach α ranging from .78 to .91), they were summed and then averaged to create scales that represent the interactivity continuum. The scores for the scales ranged from 1 to 4, with smaller values indicating lower levels of use of interactivity.

Independent Measures Scale Creation

The “civic involvement scale” was constructed by adding five questions about involvement in local service organizations, community projects, parent-teacher organizations, church activities and organized sports activities (Cronbach α = .71). The variable derived from this scale, produced by the same method as that used for the dependent measures, had scores ranging from 1 to 4, with smaller values indicating lower levels of civic involvement.

The “political engagement” scale consists of five items that asked respondents about attending local government meetings, attending political rallies, making phone calls on behalf of candidates, staying in contact with elected officials and donating money to political campaigns (Cronbach α = .87). The variable derived from this scale, produced by the same method as that used for civic involvement, had scores ranging from 1 to 4, with smaller values indicating lower levels of political engagement.

Other independent measures included perceived Internet skill level and perceived credibility of online news. To assess perceived Internet skill level participants were asked “How do you consider your Internet skill level?” on a four-point response scale from not very skilled (1) to very skilled (4). To assess perceived credibility of online news participants were asked to answer the question “I consider online news as a credible source of information” on a five-point response scale from strongly disagree (1) to strongly agree (5).

In the regression analyses reported below, the three non-attitudinal variables —age, gender and online experience — were entered in the first block. The two attitudinal measures — perceived Internet skill level and perceived credibility of online news — were entered in the second block. The behavioral measures —“civic involvement” and “political engagement” scales were entered in the third block. Examination of the tolerance and VIF scores testing multicollinearity revealed that there were no high correlations among the independent variables.

The characteristics of the sample are in accord with the user profile of the participating online newspaper. The newspaper readership demographics ( Belden Interactive, 2007 ) indicate that 92 percent of its site visitors are white and 60 percent are female. The median income is $56,000, and 43 percent have college degrees or an advanced degree. The median age is 45. Similarly, 94 percent of the survey respondents were white and 62 percent were female. About 25 percent of the survey participants earned more than $50,000 annual household income, and 55 percent of them had college degrees or above. The mean age of the respondents was 40 years old (SD = 14.36).

Categories of Interactive Features

To identify specific categories of interactive features (RQ1), a principal components factor analysis was conducted using Varimax rotation. Items (interactive features) that cross-loaded on two or more factors and those with factor loadings lower than .50 were eliminated. In addition, the log-in feature did not fit conceptually, thus, it was dropped from the analysis. The analysis yielded a reduced scale of 15 items that loaded on four factors, and thus, seven of the original 22 items were not included in any of the factors. The four factors accounted for 67 percent of the variance. As shown in Table 1 , the factors were then subsequently created as four interactivity scales. The scales were labeled medium, medium/human, human/medium and human interactivity, building on the literature. The four extracted categories of interactive features are also in agreement with an interactivity continuum: medium interactive features generally allow readers more control or choice options in experiencing news stories; medium/human interactive features allow users to customize news to their liking; human/medium interactive features allow users to express their personal opinions; human interactive features facilitate interpersonal communication online. The medium interactivity scale consists of two features: audio files and video files. The medium/human interactivity scale consists of five features: customized weather, customized topics, customized headlines, search features and e-mail updates/alerts. The human/medium interactivity scale consists of five features: “submit stories,” “submit photos,” “submit news tips,” letters-to-the-editor features and reporter/editor e-mail links. The letters-to-the-editor feature and reporter/editor e-mail links are generally considered to be features that facilitate interpersonal communication. However, these two features may have loaded on this factor in that human/medium interactive features allow the expression of ideas and may not result in direct human-to-human communication, which may be the predominant case when users submit letters-to-the-editor or write messages to reporters/editors of their local online news site. The human interactivity scale consists of three features: chat functions, message boards and Q&A features.

Use of Interactive Features

The second research question sought to examine the extent to which online news audiences make use of different types of interactive features (RQ2). In order to access patterns of use of different forms of interactive features, the scales for medium, medium/human, human/medium and human interactive features were used to calculate mean scores. Table 1 includes the overall means of the four groups of interactivity. A repeated measures ANOVA was conducted to investigate whether respondents showed significantly different uses of interactive features on online newspaper sites. The results indicate that there were indeed meaningful usage differences based on type of interactive feature as identified above, Wilk’s λ= .36, F (3, 472) = 274.29, p < .00. Specifically, the medium/human interactive features (M = 2.33, S.D. = .76) and medium interactive features (M = 2.27, S.D. = .92) scored the highest usage mean scores followed by human interactive features (M = 1.57, S.D. = .70) and human/medium interactive features (M = 1.41, S.D. = .57). Features that facilitate two-way communication and features that allow the audience to express their views were used least. Overall, the online newspaper audience was not using the interactive features frequently.

Predictors of Use of Interactive Features

The summated scales were used in the following analyses to examine what factors predict the use of the four distinct types of interactive features. Thus, the relationships between user characteristics/backgrounds and usage frequencies of interactive features are the focus of the third research question (RQ3). Four separate independent hierarchical multiple regression analyses were conducted after entering demographic, attitudinal and behavioral variables.

Use of Medium Interactive Features

Overall , this model accounted for about 8 percent of the variance in the dependent measure (use of medium interactive features). In the first regression, gender surfaced as a predictor (β = .19, p < .001) of use of medium interactive features. When the two attitudinal variables were added to the regression equation, the model was significantly improved, R 2 = .067, R 2 change = .031, p < .01. Gender remained a significant predictor (β = .18, p < .001), and the attitudinal variables of perceived Internet skill level (β = .16, p < .01) and perceived credibility of online news (β = .12, p < .05) were also significant predictors of use of medium interactive features. The addition of the behavioral measures did not improve the model. However, gender (β = .16, p < .01), perceived Internet skill level (β = .15, p < .01) and perceived credibility of online news (β = .11, p < .05) remained as significant predictors of use of medium interactive features. Among the three predictors, standardized beta coefficients indicate that gender was the strongest predictor of use of medium interactive features. Gender was dummy coded (female = 0 and male =1), so the results showed that male users are likely to use medium interactive features on an online newspaper. Table 2 shows the regression model for use of medium interactive features.

Hierarchical Regression Analysis of Factors Influencing Use of Medium Interactive Features

p < .05.

p < .01.

p < .001.

adjusted R 2 is .07.

Gender: Dummy-coded with female = 0 and male = 1.

Perceived Internet skill level: four-point response scale from not very skilled (1) to very skilled (4).

Perceived credibility of online news: five-point response scale from strongly disagree (1) to strongly agree (5).

Civic involvement: four-point response scale from never (1) to frequently (4).

Political engagement: four-point response scale from never (1) to frequently (4).

Use of Medium/Human Interactive Features

This model accounted for about 9 percent of the variance overall in the dependent measure (use of medium/human interactive features). In the first regression, no predictors emerged. The addition of the attitudinal measures, however, improved the regression model (R 2 = .038, R 2 change = .007, p < .01) as perceived Internet skill level (β = .15, p < .01) surfaced as a positive predictor. When the two behavioral variables were added, the model was again significantly improved (R 2 = .089, R 2 change = .051, p < .001). While perceived Internet skill level remained a significant positive predictor (β = .12, p < .05), political engagement (β = .18, p < .01) also surfaced as a significant predictor of use of medium/human interactive features. Standardized beta coefficients indicate that political engagement was the stronger predictor of use of medium/human interactive features. Table 3 shows the regression model for use of medium/human interactive features.

Hierarchical Regression Analysis of Factors Influencing Use of Medium/Human Interactive Features

p < .001

Use of Human/Medium Interactive Features

This model explained about 25 percent of the variance in the dependent measure (use of human/medium interactive features). In the first regression, gender (β = .15, p < .01) surfaced as a significant positive predictor of use of human/medium interactive features. The addition of the two attitudinal variables significantly improved the explanatory power of the overall model (R 2 = .078, R 2 change = .049, p < .001). In this second regression, gender (β = .15, p < .01) remained a significant positive predictor. In addition, perceived Internet skill level (β = .13, p < .05) and perceived credibility of online news (β = .19, p < .001) also surfaced as positive predictors. The addition of the behavioral measures greatly improved the explanatory power of the model (R 2 = .249, R 2 change = .171, p < .001) and yielded age (β = −.17, p < .01), perceived credibility of online news (β = .18, p < .001), civic involvement (β = .16, p < .01) and political engagement (β = .35, p < .001) as positive predictors of use of human/medium interactive features. Gender and perceived Internet skill level, however, disappeared as significant positive predictors of the dependent measure. Among the four significant variables, standardized beta coefficients reveal that political engagement was by far the strongest predictor of use of human/medium interactive features. Table 4 shows the regression model for use of human/medium interactive features.

Hierarchical Regression Analysis of Factors Influencing Use of Human/Medium Interactive Features

adjusted R 2 is .24.

Use of Human Interactive Features

The overall model explained about 11 percent of the variance in the dependent measure (use of human interactive features). The first regression yielded gender (β = .13, p < .05) as a significant positive predictor for use of human interactive features. The addition of the attitudinal variables significantly improved the regression model (R 2 = .097, R 2 change = .074, p < .001). Gender (β = .15, p < .01), remained a significant predictor while perceived Internet skill level (β = .12, p < .05) and perceived credibility of online news (β = .26, p < .001) surfaced as predictors. The addition of the behavioral variables significantly improved the model (R 2 = .123, R 2 change = .026, p < .01). While perceived Internet skill level disappeared as a predictor, both gender (β = .11, p < .05) and perceived credibility of online news (β = .26, p < .001) remained as predictor variables. Political engagement (β = .11 p < .05) also emerged as a positive predictor of use of human interactive features. Standardized beta coefficients indicate that among the three predictor variables, perceived credibility of online news was the strongest predictor of use of human interactive features. Table 5 shows the regression model for use of human interactive features.

Hierarchical Regression Analysis of Factors Influencing Use of Human Interactive Features

adjusted R 2 is .11.

In sum, these findings show that hypothesis 1 was not supported as years online was not a predictor for any of the four types of interactive features. Hypothesis 2 was partially supported in that perceived Internet skill level was a positive predictor for use of medium interactive features, which provide extended choice options, and medium/human interactive features, which allow for personalization of content. Thus, Internet skill level was associated with use of interactive features that were based on the technological application itself rather than on human communication. Hypothesis 3 was also partially supported in that perceived credibility of online news was a positive predictor for use of all types of interactive features with the exception of medium/human interactive features. In addition, civic involvement was indeed a positive predictor for use of human/medium interactive features that allow the personalized expression of ideas. This was the only incident in which civic involvement surfaced as a predictor variable, but it was not the strongest predictor of use of human/medium interactive features. Therefore, hypothesis 4 was not supported. Political engagement was also a positive predictor for use of human interactive features, but it, too, was not the strongest predictor. Therefore, hypothesis 5 was not supported. Based on these findings it is apparent that the four types of interactive features are each used for different purposes by individuals with distinct characteristics, and more extensive research is necessary to clearly identify factors that predict uses of distinct types of interactive features.

It appears that the potential of interactivity afforded through online news publications was cast in a rosy light with exaggerated excitement over audience adoption of interactive features. The findings from this study suggest that online audiences are not using interactive features extensively contrary to anticipation by media scholars and the news industry. These findings indicate that online news producers need not worry about adopting all types of interactivity that are promoted through various interactive features.

A factor analysis extracted four distinct factors that identified four categories of interactive features that promote interactivity—medium, medium/human, human/medium, and human interactive features. It appears that the interactivity continuum consists of four unique types of interactive features instead of the three that were proposed previously. Two categories of interactive features — medium/human and human/medium interactive features — fall between the medium and human interactivity extremes. For example, interactive customization options allow users to tailor their news consumption experiences to their liking by providing personal information to the website. These medium/human interactive features can be deemed first-order personalization options and are more a function of the technology. Here, users input information about themselves through a medium in order to customize news, such as local weather and news topics, to their own interests. In addition, interactive features that allow users to submit their opinions or stories to the news sites provide the audience with a sense of ownership. Sharing something personal obliges the user to put that much more at stake. These human/medium interactive features allow users to share their own perspectives and become personally involved in the creation of content. These second-order personalization options share interpersonal communication qualities but do not necessarily facilitate human-to-human communication. These two additional categories of interactivity complete the full spectrum of interactivity as a continuum. Thus, the findings from this study expand the model of interactivity promoted through interactive features on online newspapers.

Upon scale creation, a repeated measures ANOVA revealed that certain interactive features were used significantly more, or less, than others. About half of the interactive features assessed in this study had a mean use score of less than 2. Among the less frequently used features are the human interactive features and the human/medium interactive features. It appears that the news audience does not actively engage in various uses of interactive features on news websites, especially the features that facilitate communication and the expression of ideas—features that require more effort to be utilized.

Further analysis attempted to identify factors that predict the use of specific interactive features. The findings show that men, those who perceived themselves as having adept Internet skills, and those who perceived online news to be credible were more likely to use medium interactive features. This makes intuitive sense in that the medium interactive features scale consists of the use of audio and video files. Individuals who were confident of their Internet skills would more likely attempt to use these types of interactive features. The results of the perceived Internet skill level variable, a self-efficacy measure, show that if news organizations are truly interested in providing their audiences with various story telling options through multimedia, perhaps, it is necessary for news organizations to educate their audiences about using the Internet and accessing news on the World Wide Web through various technologies. This will help individuals gain confidence in their Internet skills. While perceived credibility of online news also surfaced as a positive predictor, the largest predictor here was being male, and men may also be characterized as having qualities that are linked to familiarity and ease with technology.

The analysis also found that those who were politically engaged were most likely to use medium/human interactive features. Thus, it appears that individuals who were politically active were likely to customize news headlines, topics and weather information to their liking. Individuals who perceived themselves to have adept Internet skills were also likely users of medium/human interactive features, but political engagement was the stronger predictor.

The findings further revealed that younger individuals, those who perceived online news to be a credible source of information and those who were involved with their communities and are politically engaged are likely to use human/medium interactive features. Here, the strongest predictor by far was political engagement. Because these human/medium interactive features allow the audience to express their views, it seems reasonable that those who are socially active would also be active online participants. While most news audiences are not using interactive features extensively, those who are taking advantage of the human/medium interactive features are individuals who are the movers and shakers of their communities. They are also individuals who are political activists who attend local government meetings and donate money to political campaigns. Thus, news organizations should consider adopting human/medium interactive features to provide a forum for those news audiences who are interested in communicating their opinions. In addition, it is worth noting that younger individuals are less shy in expressing their views online and making use of human/medium interactive features that facilitate social expression.

Finally, this study revealed that those who perceived online news to be a credible source of information, men and politically engaged individuals were most likely to use human interactive features that facilitate two-way interpersonal communication. Human interactive features are what make online news truly different from news delivered through traditional media channels, but it appears that they are generally used infrequently. Most interestingly, individuals who found online news to be credible were most likely to engage in dynamic human-to-human interaction online—even more so than politically engaged individuals. Overall, politically engaged individuals and those who perceived online news to be credible were most likely to use all forms of interactive features and were consequentially making the most out of online news.

Not all news readers are politically engaged or have a positive perception of online news credibility, however, and news organizations may seek to first understand who their online audiences are and then subsequently provide interactive features according to their audiences’ characteristics and backgrounds. For example, an online newspaper serving readers who are mostly younger female audiences may spend less time on adopting much of the technology driven medium interactive features but place more focus on the human/medium interactive features that allow for personal expression. Additionally, online newspapers may seek to educate their online audiences about how to use various interactive features on their sites and also make efforts to build their reputation for credible news. This study, thus, points to the importance of quality news reporting that will in turn build credibility of the news organization and subsequently encourage audiences to actively participate in their online news consumption experiences.

While this study provides somewhat discouraging results to the initial enthusiasm about online news and the application of interactivity through the adoption of interactive features, news organizations that are sincerely interested in communicating with their news audiences should not discard their efforts in applying interactivity. On the other hand, it may be helpful to be aware that providing all forms of interactive features may not be the most effective approach to engage online news audiences as the interactive features serve distinct purposes in the news consumption experiences of online audiences.

While this study provides critical findings toward audiences’ use of interactive features, it also suffers from several shortcomings. The initial list of 22 interactive features may not be an exhaustive list as new information communication technologies (ICTs) are constantly developing, and several features did not load on the four extracted factors. Therefore, the analysis was limited to the use of 15 interactive features as rigorous scale construction was employed for the creation of the four categories of interactivity scales. Future studies should further examine and identify various other forms of interactive features to refine and solidify the interactive feature scales. This process will contribute to the understanding of different categories of interactive features and how online newspaper audiences use them.

The regression models accounted for about 8 to 25 percent of the variance. The model for use of human/medium interactive features is somewhat effective in explanatory power, but a large portion of the variance is still left unexplained—especially in the regression models for use of medium and medium/human interactive features. Future studies should further identify possible predictors of uses of interactive features.

This study was conducted through the assistance of a medium-sized local newspaper located in a Midwestern city in the U.S. Thus, the generalizability of the study is limited to the geographic scope of the sample. In addition, the sample populations consisted of predominantly white and female participants. Future studies should make efforts to sample from more diverse populations, which may provide differing results. The online survey method itself may also be cited as a weakness of the study. Individuals who participated in this survey were self-selected, thus, those who felt more comfortable online to begin with may have been more likely to participate in the survey. Thus, the inherent nature of the online survey itself also contributes to the limitation of the generalizability of the findings as it may be likely that individuals who participated in this study represent a specialized subset of the online news population.

More importantly, the unsupported hypotheses point to the need for much extensive research in order to better identify factors that are associated with the uses of distinct type of interactivity. The findings from this report provide an important foundation about news audiences’ uses of interactive features. It may help online news publications better target their online audiences with greater understanding of how to engage them as active information consumers.

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Deborah S. Chung [ [email protected] ] is an assistant professor in the School of Journalism and Telecommunications at the University of Kentucky. Her research interests include the impact of information communication technologies on journalism practice, culture and education and how ICTs potentially empower information consumers.

Address: School of Journalism and Telecommunications, 215 Grehan Building, University of Kentucky, Lexington, KY 40506-0042, USA

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Research Article

A systematic review on fake news research through the lens of news creation and consumption: Research efforts, challenges, and future directions

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Intelligence Computing, Hanyang University, Seoul, Republic of Korea

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Affiliation College of Information Sciences and Technology, Pennsylvania State University, State College, PA, United States of America

Roles Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Bogoan Kim, 
• Aiping Xiong, 
• Dongwon Lee, 
• Kyungsik Han

• Published: December 9, 2021
• https://doi.org/10.1371/journal.pone.0260080
• Reader Comments

28 Dec 2023: The PLOS One Staff (2023) Correction: A systematic review on fake news research through the lens of news creation and consumption: Research efforts, challenges, and future directions. PLOS ONE 18(12): e0296554. https://doi.org/10.1371/journal.pone.0296554 View correction

Although fake news creation and consumption are mutually related and can be changed to one another, our review indicates that a significant amount of research has primarily focused on news creation. To mitigate this research gap, we present a comprehensive survey of fake news research, conducted in the fields of computer and social sciences, through the lens of news creation and consumption with internal and external factors.

We collect 2,277 fake news-related literature searching six primary publishers (ACM, IEEE, arXiv, APA, ELSEVIER, and Wiley) from July to September 2020. These articles are screened according to specific inclusion criteria (see Fig 1). Eligible literature are categorized, and temporal trends of fake news research are examined.

As a way to acquire more comprehensive understandings of fake news and identify effective countermeasures, our review suggests (1) developing a computational model that considers the characteristics of news consumption environments leveraging insights from social science, (2) understanding the diversity of news consumers through mental models, and (3) increasing consumers’ awareness of the characteristics and impacts of fake news through the support of transparent information access and education.

We discuss the importance and direction of supporting one’s “digital media literacy” in various news generation and consumption environments through the convergence of computational and social science research.

Citation: Kim B, Xiong A, Lee D, Han K (2021) A systematic review on fake news research through the lens of news creation and consumption: Research efforts, challenges, and future directions. PLoS ONE 16(12): e0260080. https://doi.org/10.1371/journal.pone.0260080

Editor: Luigi Lavorgna, Universita degli Studi della Campania Luigi Vanvitelli, ITALY

Received: March 24, 2021; Accepted: November 2, 2021; Published: December 9, 2021

Copyright: © 2021 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: This research was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (2019-0-01584, 2020-0-01373).

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

The spread of fake news not only deceives the public, but also affects society, politics, the economy and culture. For instance, Buzzfeed ( https://www.buzzfeed.com/ ) compared and analyzed participation in 20 real news and 20 fake news articles (e.g., likes, comments, share activities) that spread the most on Facebook during the last three months of the 2016 US Presidential Election. According to the results, the participation rate of fake news (8.7 million) was higher than that of mainstream news (7.3 million), and 17 of the 20 fake news played an advantageous role in winning the election [ 1 ]. Pakistan’s ministry of Defense posted a tweet fiercely condemning Israel after coming to believe that Israel had threatened Pakistan with nuclear weapons, which was later found to be false [ 2 ]. Recently, the spread of the absurd rumor that COVID-19 propagates through 5G base stations in the UK caused many people to become upset and resulted in a base station being set on fire [ 3 ].

Such fake news phenomenon has been rapidly evolving with the emergence of social media [ 4 , 5 ]. Fake news can be quickly shared by friends, followers, or even strangers within only a few seconds. Repeating a series of these processes could lead the public to form the wrong collective intelligence [ 6 ]. This could further develop into diverse social problems (i.e., setting a base station on fire because of rumors). In addition, some people believe and propagate fake news due to their personal norms, regardless of the factuality of the content [ 7 ]. Research in social science has suggested that cognitive bias (e.g., confirmation bias, bandwagon effect, and choice-supportive bias) [ 8 ] is one of the most pivotal factors in making irrational decisions in terms of the both creation and consumption of fake news [ 9 , 10 ]. Cognitive bias greatly contributes to the formation and enhancement of the echo chamber [ 11 ], meaning that news consumers share and consume information only in the direction of strengthening their beliefs [ 12 ].

Research using computational techniques (e.g., machine or deep learning) has been actively conducted for the past decade to investigate the current state of fake news and detect it effectively [ 13 ]. In particular, research into text-based feature selection and the development of detection models has been very actively and extensively conducted [ 14 – 17 ]. Research has been also active in the collection of fake news datasets [ 18 , 19 ] and fact-checking methodologies for model development [ 20 – 22 ]. Recently, Deepfake, which can manipulate images or videos through deep learning technology, has been used to create fake news images or videos, significantly increasing social concerns [ 23 ], and a growing body of research is being conducted to find ways of mitigating such concerns [ 24 – 26 ]. In addition, some research on system development (i.e., a game to increase awareness of the negative aspects of fake news) has been conducted to educate the public to avoid and prevent them from the situation where they could fall into the echo chamber, misunderstandings, wrong decision-making, blind belief, and propagating fake news [ 27 – 29 ].

While the creation and consumption of fake news are clearly different behaviors, due to the characteristics of the online environment (e.g., information can be easily created, shared, and consumed by anyone at anytime from anywhere), the boundaries between fake news creators and consumers have started to become blurred. Depending on the situation, people can quickly change their roles from fake news consumers to creators, or vice versa (with or without their intention). Furthermore, news creation and consumption are the most fundamental aspects that form the relationship between news and people. However, a significant amount of fake news research has positioned in news creation while considerably less research focus has been placed in news consumption (see Figs 1 & 2 ). This suggests that we must consider fake news as a comprehensive aspect of news consumption and creation .

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https://doi.org/10.1371/journal.pone.0260080.g001

The papers were published in IEEE, ACM, ELSEVIER, arXiv, Wiley, APA from 2010 to 2020 classified by publisher, main category, sub category, and evaluation method (left to right).

https://doi.org/10.1371/journal.pone.0260080.g002

In this paper, we looked into fake news research through the lens of news creation and consumption ( Fig 3 ). Our survey results offer different yet salient insights on fake news research compared with other survey papers (e.g., [ 13 , 30 , 31 ]), which primarily focus on fake news creation. The main contributions of our survey are as follows:

• We investigate trends in fake news research from 2010 to 2020 and confirm a need for applying a comprehensive perspective to fake news phenomenon.
• We present fake news research through the lens of news creation and consumption with external and internal factors.
• We examine key findings with a mental model approach, which highlights individuals’ differences in information understandings, expectations, or consumption.
• We summarize our review and discuss complementary roles of computer and social sciences and potential future directions for fake news research.

We investigate fake news research trend (Section 2), and examine fake news creation and consumption through the lenses of external and internal factors. We also investigate research efforts to mitigate external factors of fake news creation and consumption: (a) indicates fake news creation (Section 3), and (b) indicates fake news consumption (Section 4). “Possible moves” indicates that news consumers “possibly” create/propagate fake news without being aware of any negative impact.

https://doi.org/10.1371/journal.pone.0260080.g003

2 Fake news definition and trends

There is still no definition of fake news that can encompass false news and various types of disinformation (e.g., satire, fabricated content) and can reach a social consensus [ 30 ]. The definition continues to change over time and may vary depending on the research focus. Some research has defined fake news as false news based on the intention and factuality of the information [ 4 , 15 , 32 – 36 ]. For example, Allcott and Gentzkow [ 4 ] defined fake news as “news articles that are intentionally and verifiably false and could mislead readers.” On the other hand, other studies have defined it as “a news article or message published and propagated through media, carrying false information regardless of the means and motives behind it” [ 13 , 37 – 43 ]. Given this definition, fake news refers to false information that causes an individual to be deceived or doubt the truth, and fake news can only be useful if it actually deceives or confuses consumers. Zhou and Zafarani [ 31 ] proposed a broad definition (“Fake news is false news.”) that encompasses false online content and a narrow definition (“Fake news is intentionally and verifiably false news published by a news outlet.”). The narrow definition is valid from the fake news creation perspective. However, given that fake news creators and consumers are now interchangeable (e.g., news consumers also play a role of gatekeeper for fake news propagation), it has become important to understand and investigate the fake news through consumption perspectives. Thus, in this paper, we use the broad definition of fake news.

Our research motivation for considering news creation and consumption in fake news research was based on the trend analysis. We collected 2,277 fake news-related literature using four keywords (i.e., fake news, false information, misinformation, rumor) to identify longitudinal trends of fake news research from 2010 to 2020. The data collection was conducted from July to September 2020. The criteria of data collection was whether any of these keywords exists in the title or abstract. To reflect diverse research backgrounds/domains, we considered six primary publishers (ACM, IEEE, arXiv, APA, ELSEVIER, and Wiley). The number of papers collected for each publisher is as follows: 852 IEEE (37%), 639 ACM (28%), 463 ELSEVIER (20%), 142 arXiv (7%), 141 Wiley (6%), 40 APA (2%). We excluded 59 papers that did not have the abstract and used 2,218 papers for the analysis. We then randomly chose 200 papers, and two coders conducted manual inspection and categorization. The inter-coder reliability was verified by the Cohen’s Kappa measurement. The scores for each main/sub-category were higher than 0.72 (min: 0.72, max: 0.95, avg: 0.85), indicating that the inter-coder reliability lies between “substantial” to “perfect” [ 44 ]. Through the coding procedure, we excluded non-English studies (n = 12) and reports on study protocol only (n = 6), and 182 papers were included in synthesis. The PRISMA flow chart depicts the number of articles identified, included, and excluded (see Fig 1 ).

The papers were categorized into two main categories: (1) creation (studies with efforts to detect fake news or mitigate spread of fake news) and (2) consumption (studies that reported the social impacts of fake news on individuals or societies and how to appropriately handle fake news). Each main category was then classified into sub-categories. Fig 4 shows the frequency of the entire literature by year and the overall trend of fake news research. It appears that the consumption perspective of fake news still has not received sufficient attention compared with the creation perspective ( Fig 4(a) ). Fake news studies have exploded since the 2016 US Presidential Election, and the trend of increase in fake news research continues. In the creation category, the majority of papers (135 out of 158; 85%) were related to the false information (e.g., fake news, rumor, clickbait, spam) detection model ( Fig 4(b) ). On the other hand, in the consumption category, much research pertains to data-driven fake news trend analysis (18 out of 42; 43%) or fake content consumption behavior (16 out of 42; 38%), including studies for media literacy education or echo chamber awareness ( Fig 4(c) ).

We collected 2,277 fake news related-papers and randomly chose and categorized 200 papers. Each marker indicates the number of fake news studies per type published in a given year. Fig 4(a) shows a research trend of news creation and consumption (main category). Fig 4(b) and 4(c) show a trend of the sub-categories of news creation and consumption. In Fig 4(b), “Miscellaneous” includes studies on stance/propaganda detection and a survey paper. In Fig 4(c), “Data-driven fake news trend analysis” mainly covers the studies reporting the influence of fake news that spread around specific political/social events (e.g., fake news in Presidential Election 2016, Rumor in Weibo after 2015 Tianjin explosions). “Conspiracy theory” refers to an unverified rumor that was passed on to the public.

https://doi.org/10.1371/journal.pone.0260080.g004

3 Fake news creation

Fake news is no longer merely propaganda spread by inflammatory politicians; it is also made for financial benefit or personal enjoyment [ 45 ]. With the development of social media platforms people often create completely false information for reasons beyond satire. Further, there is a vicious cycle of this false information being abused by politicians and agitators.

Fake news creators are indiscriminately producing fake news while considering the behavioral and psychological characteristics of today’s news consumers [ 46 ]. For instance, the sleeper effect [ 47 ] refers to a phenomenon in which the persuasion effect increases over time, even though the pedigree of information shows low reliability. In other words, after a long period of time, memories of the pedigree become poor and only the content tends to be remembered regardless of the reliability of the pedigree. Through this process, less reliable information becomes more persuasive over time. Fake news creators have effectively created and propagated fake news by targeting the public’s preference for news consumption through peripheral processing routes [ 35 , 48 ].

Peripheral routes are based on the elaboration likelihood model (ELM) [ 49 ], one of the representative psychological theories that handles persuasive messages. According to the ELM, the path of persuasive message processing can be divided into the central and the peripheral routes depending on the level of involvement. On one hand, if the message recipient puts a great deal of cognitive effort into processing, the central path is chosen. On the other hand, if the process of the message is limited due to personal characteristics or distractions, the peripheral route is chosen. Through a peripheral route, a decision is made based on other secondary cues (e.g., speakers, comments) rather than the logic or strength of the argument.

Wang et al. [ 50 ] demonstrated that most of the links shared or mentioned in social media have never even been clicked. This implies that many people perceive and process information in only fragmentary way, such as via news headlines and the people sharing news, rather than considering the logical flow of news content.

In this section, we closely examined each of the external and internal factors affecting fake news creation, as well as the research efforts carried out to mitigate the negative results based on the fake news creation perspective.

3.1 External factors: Fake news creation facilitators

We identified two external factors that facilitate fake news creation and propagation: (1) the unification of news creation, consumption, and distribution, (2) the misuse of AI technology, and (3) the use of social media as a news platform (see Fig 5 ).

We identify two external factors—The unification of news and the misuse of AI technology—That facilitate fake news creation.

https://doi.org/10.1371/journal.pone.0260080.g005

3.1.1 The unification of news creation, consumption, and distribution.

The public’s perception of news and the major media of news consumption has gradually changed. The public no longer passively consumes news exclusively through traditional news organizations with specific formats (e.g., the inverted pyramid style, verified sources) nor view those news simply as a medium for information acquisition. The public’s active news consumption behaviors began in earnest with the advent of citizen journalism by implementing journalistic behavior based on citizen participation [ 51 ] and became commonplace with the emergence of social media. As a result, the public began to prefer interactive media, in which new information could be acquired, their opinions can be offered, and they can discuss the news with other news consumers. This environment has motivated the public to make content about their beliefs and deliver the content to many people as “news.” For example, a recent police crackdown video posted in social media quickly spread around the world that influenced protesters and civic movements. Then, it was reported later by the mainstream media [ 52 ].

The boundaries between professional journalists and amateurs, as well as between news consumers and creators, are disappearing. This has led to a potential increase in deceptive communications, making news consumers suspicious and misinterpreted the reality. Online platforms (e.g., YouTube, Facebook) that allow users to freely produce and distribute content have been growing significantly. As a result, fake news content can be used to attract secondary income (e.g., multinational enterprises’ advertising fees), which contributes to accelerating fake news creation and propagation. An environment in which the public can only consume news that suits their preferences and personal cognitive biases has made it much easier for fake news creators to achieve their specific purposes (e.g., supporting a certain political party or a candidate they favor).

3.1.2 The misuse of AI technology.

The development of AI technology has made it easier to develop and utilize tools for creating fake news, and many studies have confirmed the impact of these technologies— (1) social bots, (2) trolls, and (3) fake media —on social networks and democracy over the past decade.

3.1.2.1 Social bots . Shao et al. [ 53 ] analyzed the pattern of fake news spread and confirmed that social bots play a significant role in fake news propagation and social bot-based automated accounts were largely affected by the initial stage of spreading fake news. In general, it is uneasy for the public to determine whether such accounts are people or bots. In addition, social bots are not illegal tools and many companies legally purchase them as a part of marketing, thus it is not easy to curb the use of social bots systematically.

3.1.2.2 Trolls . The term “trolls” refers to people who deliberately cause conflict or division by uploading inflammatory, provocative content or unrelated posts to online communities. They work with the aim of stimulating people’s feelings or beliefs and hindering mature discussions. For example, the Russian troll army has been active in social media to advance its political agenda and cause social turmoil in the US [ 54 ]. Zannettou et al. [ 55 ] confirmed how effectively the Russian troll army has been spreading fake news URLs on Twitter and its significant impact on making other Twitter users believe misleading information.

3.1.2.3 Fake media . It is now possible to manipulate or reproduce content in 2D or even 3D through AI technology. In particular, the advent of fake news using Deepfake technology (combining various images on an original video and generating a different video) has raised another major social concern that had not been imagined before. Due to the popularity of image or video sharing on social media, such media types have become the dominant form of news consumption, and the Deepfake technology itself is becoming more advanced and applied to images and videos in a variety of domains. We witnessed a video clip of former US President Barack Obama criticizing Donald Trump, which was manipulated by the US online media company BuzzFeed to highlight the influence and danger of Deepfake, causing substantial social confusion [ 56 ].

3.2 Internal factors: Fake news creation purposes

We identified three main purposes for fake news creation— (1) ideological purposes, (2) monetary purposes, and (3) fear/panic reduction .

3.2.1 Ideological purpose.

Fake news has been created and propagated for political purposes by individuals or groups that positively affect the parties or candidates they support or undermine those who are not on the same side. Fake news with this political purpose has shown to negatively influence people and society. For instance, Russia created a fake Facebook account that caused many political disputes and enhanced polarization, affecting the 2016 US Presidential Election [ 57 ]. As polarization has intensified, there has also been a trend in the US that “unfriending” people who have different political tendencies [ 58 ]. This has led the public to decide whether to trust the news or not regardless of its factuality and has resulted in worsening in-group biases. During the Brexit campaign in the UK, many selective news articles were exposed on Facebook, and social bots and trolls were also confirmed as being involved in creating public opinions [ 59 , 60 ].

3.2.2 Monetary purpose.

Financial benefit is another strong motivation for many fake news creators [ 34 , 61 ]. Fake news websites usually reach the public through social media and make profits through posted advertisements. The majority of fake websites are focused on earning advertising revenue by spreading fake news that would attract readers’ attention, rather than political goals. For example, during the 2016 US Presidential Election in Macedonia, young people in their 10s and 20s used content from some extremely right-leaning blogs in the US to mass-produce fake news, earning huge advertising revenues [ 62 ]. This is also why fake news creators use provocative titles, such as clickbait headlines, to induce clicks and attempt to produce as many fake news articles as possible.

3.2.3 Fear and panic reduction.

In general, when epidemics become more common around the world, rumors of absurd and false medical tips spread rapidly in social media. When there is a lack of verified information, people feel great anxious and afraid and easily believe such tips, regardless of whether they are true [ 63 , 64 ]. The term infodemic , which first appeared during the 2003 SARS pandemics, describes this phenomenon [ 65 ]. Regarding COVID-19, health authorities have recently announced that preventing the creation and propagation of fake news about the virus is as important as alleviating the contagious power of COVID-19 [ 66 , 67 ]. The spread of fake news due to the absence of verified information has become more common regarding health-related social issues (e.g., infectious diseases), natural disasters, etc. For example, people with disorders affecting cognition (e.g., neurodegenerative disorder) are tend to easily believe unverified medical news [ 68 – 70 ]. Robledo and Jankovic [ 68 ] confirmed that many fake or exaggerated medical journals are misleading people with Parkinson’s disease by giving false hopes and unfounded fake articles. Another example is a rumor that climate activists set fire to raise awareness of climate change quickly spread as fake news [ 71 ], when a wildfire broke out in Australia in 2019. As a result, people became suspicious and tended to believe that the causes of climate change (e.g., global warming) may not be related to humans, despite scientific evidence and research data.

3.3 Fake news detection and prevention

The main purpose of fake news creation is to make people confused or deceived regardless of topic, social atmosphere, or timing. Due to this purpose, it appears that fake news tends to have similar frames and structural patterns. Many studies have attempted to mitigate the spread of fake news based on these identifiable patterns. In particular, research on developing computational models that detect fake information (text/images/videos), based on machine or deep learning techniques has been actively conducted, as summarized in Table 1 . Other modeling studies include the credibility of weblogs [ 84 , 85 ], communication quality [ 88 ], susceptibility level [ 90 ], and political stance [ 86 , 87 ]. The table was intended to characterize a research scope and direction of the development of fake information creation (e.g., the features employed in each model development), not to present an exhaustive list.

https://doi.org/10.1371/journal.pone.0260080.t001

3.3.1 Fake text information detection.

Research has considered many text-based features, such as structural (e.g., website URLs and headlines with all capital letters or exclamations) and linguistic information (e.g., grammar, spelling, and punctuation errors) about the news. Research has also considered the sentiments of news articles, the frequency of the words used, user information, and who left comments on the news articles, and social network information among users (who were connected based on activities of commenting, replying, liking or following) were used as key features for model development. These text-based models have been developed for not only fake news articles but also other types of fake information, such as clickbaits, fake reviews, spams, and spammers. Many of the models developed in this context performed a binary classification that distinguished between fake and non-fake articles, with the accuracy of such models ranging from 86% to 93%. Mainstream news articles were used to build most models, and some studies used articles on social media, such as Twitter [ 15 , 17 ]. Some studies developed fake news detection models by extracting features from images, as well as text, in news articles [ 16 , 17 , 75 ].

3.3.2 Fake visual media detection.

The generative adversary network (GAN) is an unsupervised learning method that estimates the probability distribution of original data and allows an artificial neural network to produce similar distributions [ 109 ]. With the advancement of GAN, it has become possible to transform faces in images into those of others. However, photos of famous celebrities have been misused (e.g., being distorted into pornographic videos), increasing concerns about the possible misuse of such technology [ 110 ] (e.g., creating rumors about a certain political candidate). To mitigate this, research has been conducted to develop detection models for fake images. Most studies developed binary classification models (fake image or not), and the accuracy of fake image detection models was high, ranging from 81% to 97%. However, challenges still exist. Unlike fake news detection models that employ fact-checking websites or mainstream news as data verification or ground-truth, fake image detection models were developed using the same or slightly modified image datasets (e.g., CelebA [ 97 ], FFHQ [ 99 ]), asking for the collection and preparation of a large amount of highly diverse data.

4 Fake news consumption

4.1 external factors: fake news consumption circumstances.

The implicit social contract between civil society and the media has gradually disintegrated in modern society, and accordingly, citizens’ trust in the media began to decline [ 111 ]. In addition, the growing number of digital media platforms has changed people’s news consumption environment. This change has increased the diversity of news content and the autonomy of information creation and sharing. At the same time, however, it blurred the line between traditional mainstream media news and fake news in the Internet environment, contributing to polarization.

Here, we identified three external factors that have forced the public to encounter fake news: (1) the decline of trust in the mainstream media, (2) a high-choice media environment, and (3) the use of social media as a news platform .

4.1.1 Fall of mainstream media trust.

Misinformation and unverified or biased reports have gradually undermined the credibility of the mainstream media. According to the 2019 American mass media trust survey conducted by Gallup, only 13% of Americans said they trusted traditional mainstream media: newspapers or TV news [ 112 ]. The decline in traditional media trust is not only a problem for the US, but also a common concern in Europe and Asia [ 113 – 115 ].

4.1.2 High-choice media environment.

Over the past decade, news consumption channels have been radically diversified, and the mainstream has shifted from broadcasting and print media to mobile and social media environments. Despite the diversity of news consumption channels, personalized preferences and repetitive patterns have led people to be exposed to limited information and continue to consume such information increasingly [ 116 ]. This selective news consumption attitude has enhanced the polarization of the public in many multi-media environments [ 117 ]. In addition, the commercialization of digital platforms have created an environment in which cognitive bias can be easily strengthened. In other words, a digital platform based on recommended algorithms has the convenience of providing similar content continuously after a given type of content is consumed. As a result, it may be easy for users to fall into the echo chamber because they only access recommended content. A survey of 1,000 YouTube videos found that more than two-thirds of the videos contained content in favor of a particular candidate [ 118 ].

News consumption in social media does not simply mean the delivery of messages from creators to consumers. The multi-directionality of social media has blurred the boundaries between information creators and consumers. In other words, users are already interacting with one another in various fashions, and when a new interaction type emerges and is supported by the platform, users will display other types of new interactions, which will also influence ways of consuming news information.

4.1.3 Use of social media as news platform.

Here we focus on the most widely used social media platforms—YouTube, Facebook, and Twitter—where each has characteristics of encouraging limited news consumption.

First, YouTube is the most unidirectional of social media. Many YouTube creators tend to convey arguments in a strong, definitive tone through their videos, and these content characteristics make viewers judge the objectivity of the information via non-verbal elements (e.g., speaker, thumbnail, title, comments) rather than facts. Furthermore, many comments often support the content of the video, which may increase the chances of viewers accepting somewhat biased information. In addition, a YouTube video recommendation algorithm causes users who watch certain news to continuously be exposed to other news containing the same or similar information. This behavior and direction on the part of isolated content consumption could undermine the viewer’s media literacy, and is likely to create a screening effect that blocks the user’s eyes and ears.

Second, Facebook is somewhat invisible regarding the details of news articles because this platform ostensibly shows only the title, the number of likes, and the comments of the posts. Often, users have to click on the article and go to the URL to read the article. This structure and consumptive content orientation on the part of Facebook presents obstacles that prevent users from checking the details of their posts. As a result, users have become likely to make limited and biased judgments and perceive content through provocative headlines and comments.

Third, the largest feature of Twitter is anonymity because Twitter asks users to make their own pseudonyms [ 119 ]. Twitter has a limited number of letters to upload, and compared to other platforms, users can produce and spread indiscriminate information anonymously and do not know who is behind the anonymity [ 120 , 121 ]. On the other hand, many accounts on Facebook operate under real names and generally share information with others who are friends or followers. Information creators are not held accountable for anonymous information.

4.2 Internal factors: Cognitive mechanism

Due to the characteristics of the Internet and social media, people are accustomed to consuming information quickly, such as reading only news headlines and checking photos in news articles. This type of news consumption practice could lead people to consider news information mostly based on their beliefs or values. This practice can make it easier for people to fall into an echo chamber and further social confusion. We identified two internal factors affecting fake news consumption: (1) cognitive biases and (2) personal traits (see Fig 6 ).

https://doi.org/10.1371/journal.pone.0260080.g006

4.2.1 Cognitive biases.

Cognitive bias is an observer effect that is broadly recognized in cognitive science and includes basic statistical and memory errors [ 8 ]. However, this bias may vary depending on what factors are most important to affect individual judgments and choices. We identified five cognitive biases that affect fake news consumption: confirmation bias, in-group bias, choice-supportive bias, cognitive dissonance, and primacy effect.

Confirmation bias relates to a human tendency to seek out information in line with personal thoughts or beliefs, as well as to ignore information that goes against such beliefs. This stems from the human desire to be reaffirmed, rather than accept denials of one’s opinion or hypothesis. If the process of confirmation bias is repeated, a more solid belief is gradually formed, and the belief remains unchanged even after encountering logical and objective counterexamples. Evaluating information with an objective attitude is essential to properly investigating any social phenomenon. However, confirmation bias significantly hinders this. Kunda [ 122 ] discussed experiments that investigated the cognitive processes as a function of accuracy goals and directional goals. Her analysis demonstrated that people use different cognitive processes to achieve the two different goals. For those who pursue accuracy goals (reaching a “right conclusion”), information is used as a tool to determine whether they are right or not [ 123 ], and for those with directional goals (reaching a desirable conclusion), information is used as a tool to justify their claims. Thus, biased information processing is more frequently observed by people with directional goals [ 124 ].

People with directional goals have a desire to reach the conclusion they want. The more we emphasize the seriousness and omnipresence of fake news, the less people with directional goals can identify fake news. Moreover, their confirmation bias through social media could result in an echo chamber, triggering a differentiation of public opinion in the media. The algorithm of the media platform further strengthens the tendency of biased information consumption (e.g., filter bubble).

In-group bias is a phenomenon in which an individual favors a group that he or she belongs to. The causes of in-group bias are two [ 125 ]. One is a categorization process, which exaggerates the similarities between members within one category (the internal group) and differences with others (the external groups). Consequently, positive reactions towards the internal group and negative reactions (e.g., hostility) towards the external group are both increased. The other reason is self-respect based on social identity theory. To positively evaluate the internal group, a member tends to perceive that other group members are similar to himself or herself.

In-group bias has a significant impact on fake news consumption because of radical changes in the media environment [ 126 ]. The public recognizes and forms groups based on issues through social media. The emotions and intentions of such groups of people online can be easily transferred or developed into offline activities, such as demonstrations and rallies. Information exchanges within such internal groups proceeds similarly to the situation with confirmation bias. If confirmation bias is keeping to one’s beliefs, in-group bias equates the beliefs of my group with my beliefs.

Choice-supportive bias refers to an individual’s tendency to justify his or her decision by highlighting the evidence that he or she did not consider in making the decision [ 127 ]. For instance, people sometimes have no particular purpose when they purchase a certain brand of products or service, or support a particular politician or political party. They emphasize that their choices at the time were right and inevitable. They also tend to focus more on positive aspects than negative effects or consequences to justify their choice. However, these positive aspects can be distorted because they are mainly based on memory. Thus, choice-supportive bias, can be regarded as the cognitive errors caused by memory distortion.

The behavioral condition of choice-supportive bias is used to justify oneself, which usually occurs in the context of external factors (e.g., maintaining social status or relationships) [ 7 ]. For example, if people express a certain political opinion within a social group, people may seek information with which to justify the opinion and minimize its flaws. In this procedure, people may accept fake news as a supporting source for their opinions.

Cognitive dissonance was based on the notion that some psychological tension would occur when an individual had two perceptions that were inconsistent [ 128 ]. Humans have a desire to identify and resolve the psychological tension that occurs when a cognitive dissonance is established. Regarding fake news consumption, people easily accept fake news if it is aligned with their beliefs or faith. However, if such news is seen as working against their beliefs or faith, people define even real news as fake and consume biased information in order to avoid cognitive dissonance. This is quite similar to cognitive bias. Selective exposure to biased information intensifies its extent and impact in social media. In these circumstances, an individual’s cognitive state is likely to be formed by information from unclear sources, which can be seen as a negative state of perception. In that case, information consumers selectively consume only information that can be in harmony with negative perceptions.

Primacy effect means that information presented previously will have a stronger effect on the memory and decision-making than information presented later [ 129 ]. The “interference theory [ 130 ]” is often referred to as a theoretical basis for supporting the primacy effect, which highlights the fact that the impression formed by the information presented earlier influences subsequent judgments and the process of forming the next impression.

The significance of the primary effect for fake news consumption is that it can be a starting point for biased cognitive processes. If an individual first encounters an issue in fake news and does not go through a critical thinking process about that information, he or she may form false attitudes regarding the issue [ 131 , 132 ]. Fake news is a complex combination of facts and fiction, making it difficult for information consumers to correctly judge whether the news is right or wrong. These cognitive biases induce the selective collection of information that feels more valid for news consumers, rather than information that is really valid.

4.2.2 Personal traits.

We two aspects of personal characteristics or traits can influence one’s behaviors in terms of news consumption: susceptibility and personality.

4.2.2.1 Susceptibility . The most prominent feature of social media is that consumers can be also creators, and the boundaries between the creators and consumers of information become unclear. New media literacy (i.e., the ability to critically and suitably consume messages in a variety of digital media channels, such as social media) can have a significant impact on the degree of consumption and dissemination of fake news [ 133 , 134 ]. In other words, the higher new media literacy is, the higher the probability that an individual is likely to take a critical standpoint toward fake news. Also, the susceptibility level of fake news is related to one’s selective news consumption behaviors. Bessi et al. [ 35 ] studied misinformation on Facebook and found that users who frequently interact with alternative media tend to interact with intentionally false claims more often.

Personality is an individual’s traits or behavior style. Many scholars have agreed that the personality can be largely divided into five categories (Big Five)—extraversion, agreeableness, neuroticism, openness, and conscientiousness [ 135 , 136 ]—and used them to understand the relationship between personality and news consumption.

Extroversion is related to active information use. Previous studies have confirmed that extroverts tend to use social media and that their main purpose of use is to acquire information [ 137 ] and better determine the factuality of news on social media [ 138 ]. Furthermore, people with high agreeableness, which refers to how friendly, warm, and tactful, tend to trust real news than fake news [ 138 ]. Neuroticism refers to a broad personality trait dimension representing the degree to which a person experiences the world as distressing, threatening, and unsafe. People with high neuroticism usually show negative emotions or information sharing behavior [ 139 ]. Neuroticism is positively related to fake news consumption [ 138 ]. Openness refers to the degree of enjoying new experiences. High openness is associated with high curiosity and engagement in learning [ 140 ], which enhances critical thinking ability and decreases negative effects of fake news consumption [ 138 , 141 ]. Conscientiousness refers to a person’s work ethic, being orderly, and thoroughness [ 142 ]. People with high conscientiousness tend to regard social media use as distraction from their tasks [ 143 – 145 ].

4.3 Fake news awareness and prevention

4.3.1 decision-making support tools..

News on social media does not go through the verification process, because of its high degree of freedom to create, share, and access information. The study reported that most citizens in advanced countries will have more fake information than real information in 2022 [ 146 ]. This indicates that potential personal and social damage from fake news may increase. Paradoxically, many countries that suffer from fake news problems strongly guarantee the freedom of expression under their constitutions; thus, it would be very difficult to block all possible production and distribution of fake news sources through laws and regulations. In this respect, it would be necessary to put in place not only technical efforts to detect and prevent the production and dissemination of fake news but also social efforts to make news consumers aware of the characteristics of online fake information.

Inoculation theory highlights that human attitudes and beliefs can form psychological resistance by being properly exposed to arguments against belief in advance. To have the ability to strongly protest an argument, it is necessary to expose and refute the same sort of content with weak arguments first. Doris-Down et al. [ 147 ] asked people who were from different political backgrounds to communicate directly through mobile apps and investigated whether these methods alleviated their echo-chamberness. As a result, the participants made changes, such as realizing that they had a lot in common with people who had conflicting political backgrounds and that what they thought was different was actually trivial. Karduni et al. [ 148 ] provided comprehensive information (e.g., connections among news accounts and a summary of the location entities) to study participants through the developed visual analytic system and examined how they accepted fake news. Another study was conducted to confirm how people determine the veracity of news by establishing a system similar to social media and analyzing the eye tracking of the study participants while reading fake news articles [ 28 ].

Some research has applied the inoculation theory to gamification. A “Bad News” game was designed to proactively warn people and expose them to a certain amount of false information through interactions with the gamified system [ 29 , 149 ]. The results confirmed the high effectiveness of inoculation through the game and highlighted the need to educate people about how to respond appropriately to misinformation through computer systems and games [ 29 ].

4.3.2 Fake information propagation analysis.

Fake information tends to show a certain pattern in terms of consumption and propagation, and many studies have attempted to identify the propagation patterns of fake information (e.g., the count of unique users, the depth of a network) [ 150 – 153 ].

4.3.2.1 Psychological characteristics . The theoretical foundation of research intended to examine the diffusion patterns of fake news lies in psychology [ 154 , 155 ] because psychological theories explain why and how people react to fake news. For instance, a news consumer who comes across fake news will first have doubts, judge the news against his background knowledge, and want to clarify the sources in the news. This series of processes ends when sufficient evidence is collected. Then the news consumer ends in accepting, ignoring, or suspecting the news. The psychological elements that can be defined in this process are doubts, negatives, conjectures, and skepticism [ 156 ].

4.3.2.2 Temporal characteristics . Fake news exhibits different propagation patterns from real news. The propagation of real news tends to slowly decrease over time after a single peak in the public’s interest, whereas fake news does not have a fixed timing for peak consumption, and a number of peaks appear in many cases [ 157 ]. Tambuscio et al. [ 151 ] proved that the pattern of the spread of rumors is similar to the existing epidemic model [ 158 ]. Their empirical observations confirmed that the same fake news reappears periodically and infects news consumers. For example, rumors that include the malicious political message that “Obama is a Muslim” are still being spread a decade later [ 159 ]. This pattern of proliferation and consumption shows that fake news may be consumed for a certain purpose.

5 A mental-model approach

We have examined news consumers’ susceptibility to fake news due to internal and external factors, including personal traits, cognitive biases, and the contexts. Beyond an investigation on the factor level, we seek to understand people’s susceptibility to misinformation by considering people’s internal representations and external environments holistically [ 5 ]. Specifically, we propose to comprehend people’s mental models of fake news. In this section, we first briefly introduce mental models and discuss their connection to misinformation. Then, we discuss the potential contribution of using a mental-model approach to the field of misinformation.

5.1 Mental models

A mental model is an internal representation or simulation that people carry in their minds of how the world works [ 160 , 161 ]. Typically, mental models are constructed in people’s working memory, in which information from long-term memory and the environments are combined [ 162 ]. They also indicate that individuals represent complex phenomena with somewhat abstraction based on their own experiences and understanding of the contexts. People rely on mental models to understand and predict their interactions with environments, artifacts and computing systems, as well as other individuals [ 163 , 164 ]. Generally, individuals’ ability to represent the continually changing environments is limited and unique. Thus, mental models tend to be functional and dynamic but not necessarily accurate or complete [ 163 , 165 ]. Mental models also differ between various groups and in particular between experts and novices [ 164 , 166 ].

5.2 Mental models and misinformation

Mental models have been proposed to understand human behaviors in spatial navigation [ 167 ], learning [ 168 , 169 ], deductive reasoning [ 170 ], mental presentations of real or imagined situations [ 171 ], risk communication [ 172 ], and usable cybersecurity and privacy [ 166 , 173 , 174 ]. People use mental models to facilitate their comprehension, judgment, and actions, and can be the basis of individual behaviors. In particular, the connection between a mental-model approach and misinformation has been revealed in risk communication regarding vaccines [ 175 , 176 ]. For example, Downs et al. [ 176 ] interviewed 30 parents from three US cities to understand their mental models about vaccination for their children aged 18 to 23 months. The results revealed two mental models about vaccination: (1) heath oriented : parents who focused on health-oriented topics trusted anecdotal communication more than statistical arguments; and (2) risk oriented : parents with some knowledge about vaccine mechanisms trusted communication with statistical arguments more than anecdotal information. Also, the authors found that many parents, even those favorable to vaccination, can be confused by ongoing debate, suggesting somewhat incompleteness of their mental models.

5.3 Potential contributions of a mental-model approach

Recognizing and dealing with the plurality of news consumers’ perception, cognition and actions is currently considered as key aspects of misinformation research. Thus, a mental model approach could significantly improve our understanding of people’s susceptibility to misinformation, as well as inform the development of mechanisms to mitigate misinformation.

One possible direction is to investigate the demographic differences in the context of mental models. As more Americans have adopted social media, the social media users have become more representative for the population. Usage by older adults has increased in recent years, with the use rate of about 12% in 2012 to about 35% in 2016 ( https://www.pewresearch.org/internet/fact-sheet/social-media/ ). Guess et al. (2019) analyzed participants’ profiles and their sharing activity on Facebook during the 2016 US Presidential campaign. A strong age effect was revealed. While controlled the effects of ideology and education, their results showed that Facebook users who are over 65 years old were associated with sharing nearly seven times as many articles from fake news domains on Facebook as those who are between 18–29 years old, or about 2.3 times as many as those in the age between 45 to 65.

Besides older adults, college students were shown more susceptibility to misinformation [ 177 ]. We can identify which mental models a particular age group ascribes to, and compare the incompleteness or incorrectness of the mental models by age. On the other hand, such comparison might be informative to design general mechanisms to mitigate misinformation independent of the different concrete mental models possessed by different types of users.

Users’ actions and decisions are directed by their mental models. We can also explore news consumers’ mental models and discover unanticipated and potentially risky human system interactions, which will inform the development and design of user interactions and education endeavors to mitigate misinformation.

A mental-model approach supplies an important, and as yet unconsidered, dimension to fake news research. To date, research on people’s susceptibility to fake news in social media has lagged behind research on computational aspect research on fake news. Scholars have not considered issues of news consumers’ susceptibility across the spectrum of their internal representations and external environments. An investigation from the mental model’s perspective is a step toward addressing such need.

6 Discussion and future work

In this section, we highlight the importance of balancing research efforts on fake news creation and consumption and discuss potential future directions of fake news research.

6.1 Leveraging insights of social science to model development

Developing fake news detection models has achieved great performance. Feature groups used in the model are diverse including linguistics, vision, sentiment, topic, user, and network, and many models used multiple groups to increase the performance. By using datasets with different size and characteristics, research has demonstrated the effectiveness of the models through a comparison analysis. However, much research has considered and used the features that are easily quantifiable, and many of them tend to have unclear justification or rationale of being used in modeling. For example, what is the relationship between the use of question (?), exclamation (!), or quotation marks (“…”) and fake news?, what does it mean by a longer description relates to news trustworthiness?. There are also many important aspects that can be used as additional features for modeling and have not yet found a way to be quantified. For example, journalistic styles are important characteristics that determine a level of information credibility [ 156 ], but it is challenging to accurately and reliably quantified them. There are many intentions (e.g., ideological standpoint, financial gain, panic creation) that authors may implicitly or explicitly display in the post but measuring them is uneasy and not straightforward. Social science research can play a role in here coming up with a valid research methodology to measure such subjective perceptions or notions considering various types and characteristics of them depending on a context or environment. Some research efforts in this research direction include quantifying salient factors of people’s decision-making identified in social science research and demonstrating the effectiveness of using the factors in improving model performance and interpreting model results [ 70 ]. Yet more research that applies socio-technical aspects in model development and application would be needed to better study complex characteristics of fake news.

6.1.1 Future direction.

Insights from social science may help develop transparent and applicable fake news detection models. Such socio-technical models may allow news consumers to have a better understanding of fake news detection results and its application as well as to take more appropriate actions to control fake news phenomenon.

6.2 Lack of research on fake news consumption

Regarding fake news consumption, we confirmed that only few studies involve the development of web- or mobile-based technology systems to help consumers aware possible dangers of fake news. Those studies [ 28 , 29 , 147 , 148 ] tried to demonstrate the feasibility of developed self-awareness systems through user studies. However, due to the limited number of study participants (min: 11, max: 60) and their lack of demographic diversity (i.e., recruited only college students of one school, the psychology research pool at the authors’ institution), the generalization and applicability of these systems are still questionable. On the other hand, research that involves the development of fake news detection models or network analysis to identify the pattern of fake news propagation has been relatively active. These results can be used to identify people (or entities) who intentionally create malicious fake content; however, it is still challenging to restrict people who originally had not shown any behaviors or indications of sharing or creating fake information but later manipulated real news to fake or disseminated fake news with their malicious intention or cognitive biases.

In other words, although fake news detection models have shown great, promising performance, the influence of the models may be exerted in limited cases. This is because fake news detection models heavily rely on the data that were labeled as fake by other fact-checking institutions or sites. If someone manipulates the news that were not covered by fact-checking, the format or characteristics of the manipulated news may be different from those (i.e., conventional features) that are identified and managed in the detection model. Such differences may not be captured by the model. Therefore, to prevent fake news phenomenon more effectively, research needs to consider changes of news consumption.

6.2.1 Future direction.

It may be desirable to support people recognizing that their news consumption behaviors (e.g., like, comment, share) can have a significant ripple effect. Developing a system that tracks activities of people’s news consumption and creation, measures similarity and differences between those activities, and presents behaviors or patterns of news consumption and creation to people would be helpful.

6.3 Limited coverage of fact-checking websites and regulatory approach

Some of the well-known fact-checking websites (e.g., snopes.com, politifact.com) cover news shared mostly on the Internet and label the authenticity or deficiencies of the content (e.g., miscaptioned, legend, misattributed). However, these fact-checking websites may show limited coverage in that they are only used for those who are willing to check the veracity of certain news articles. Social media platforms have been making continuous efforts to mitigate the spread of fake news. For example, Facebook shows that content that has been falsely assessed by fact-checkers is relatively less exposed to news feeds or shows warning indicators [ 178 ]. Instagram has also changed the way that warning labels are displayed when users attempt to view the content that has been falsely assessed [ 179 ]. However, this type of an interface could lead news consumers to relying on algorithmic decision-making rather than self-judgment because these ostensible regulations (e.g., warning labels) tend to lack transparency of the decision. As we explained previously, this is related to filter bubbles. Therefore, it is important to provide a more clear and transparent communicative interface for news consumers to access and understand underlying information of the algorithm results.

6.3.1 Future direction.

It is necessary to create a news consumption circumstance that gives a wider coverage of fake news and more transparent information of algorithmic decisions on news credibility. This will help news consumers preemptively avoid fake news consumption and contribute more to preventing fake news propagation. Consumers also make more proper and accurate decisions based on their understanding of the news.

6.4 New media literacy

With the diversification of news channels, we can easily consume news. However, we are also in a media environment that asks us to self-critically verify news content (e.g., whether the news title reads like a clickbait, whether the news title and content are related), which in reality is hard to be done. Moreover, in social media, news consumers can be news creators or reproducers. During this process, news information could be changed based on a consumer’s beliefs or interests. A problem here is that people may not know how to verify news content or not be aware of whether the information could be distorted or biased. As the news consumer environment changes rapidly and faces modern media deluge, the importance of media literacy education is high. Media literacy refers to the ability to decipher media content, but in a broad sense, to understand the principles of media operation and media content sensibly and critically, and in turn to the ability to utilize and creatively reproduce content. Being a “lazy thinker” is more susceptible to fake news than having a “partisan bias” [ 32 ]. As “screen time” (i.e., time spent looking at smartphone, computer, or television screens) has become more common, people are consuming only stimulating (e.g., sensual pleasure and excitement) information [ 180 ]. This could gradually lower one’s ability of critical, reasonable thinking, leading to making wrong judgments and actions. In France, when fake news problem became more serious, and a great amount of efforts were made to create “European Media Literacy Week” in schools [ 181 ]. The US is also making legislative efforts to add media literacy to the general education curriculum [ 182 ]. However, the acquisition of new media literacy through education may be limited to people in school (e.g., young students) and would be challenging to be expanded to wider populations. Thus, there is also a need for supplementary tools and research efforts to support more people to critically interpret and appropriately consume news.

In addition, more critical social attention is needed because visual content (e.g., images, videos), which had been naturally accepted as facts, can be easily manipulated in a malicious fashion and looked very natural. We have seen that people prefer to watch YouTube videos for news consumption rather than reading news articles. This visual content makes it relatively easy for news consumers to trust the content compared to text-based information and makes it easier to obtain information simply by playing the video. Since visual content will become a more dominant medium in future news consumption, educating and inoculating news consumers about potential threats of fake information in such news media would be important. More attention and research are needed on the technology supporting fake visual content awareness.

6.4.1 Future direction.

Research in both computer science and social science should find ways (e.g., developing a game-based education system or curriculum) to help news consumers aware of their practice of news consumption and maintain right news consumption behaviors.

7 Conclusion

We presented a comprehensive summary of fake news research through the lenses of news creation and consumption. The trends analysis indicated a growing increase in fake news research and a great amount of research focus on news creation compared to news consumption. By looking into internal and external factors, we unpacked the characteristics of fake news creation and consumption and presented the use of people’s mental models to better understand people’s susceptibility to misinformation. Based on the reviews, we suggested four future directions on fake news research—(1) a socio-technical model development using insights from social science, (2) in-depth understanding of news consumption behaviors, (3) preemptive decision-making and action support, and (4) educational, new media literacy support—as ways to reduce the gap between news creation and consumption and between computer science and social science research and to support healthy news environments.

Supporting information

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https://doi.org/10.1371/journal.pone.0260080.s001

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Internet Research

ISSN : 1066-2243

Article publication date: 29 March 2022

Issue publication date: 7 September 2022

The extensive distribution of fake news on the internet (FNI) has significantly affected many lives. Although numerous studies have recently been conducted on this topic, few have helped us to systematically understand the antecedents and consequences of FNI. This study contributes to the understanding of FNI and guides future research.

Design/methodology/approach

Drawing on the input–process–output framework, this study reviews 202 relevant articles to examine the extent to which the antecedents and consequences of FNI have been investigated. It proposes a conceptual framework and poses future research questions.

First, it examines the “what”, “why”, “who”, “when”, “where” and “how” of creating FNI. Second, it analyses the spread features of FNI and the factors that affect the spread of FNI. Third, it investigates the consequences of FNI in the political, social, scientific, health, business, media and journalism fields.

Originality/value

The extant reviews on FNI mainly focus on the interventions or detection of FNI, and a few analyse the antecedents and consequences of FNI in specific fields. This study helps readers to synthetically understand the antecedents and consequences of FNI in all fields. This study is among the first to summarise the conceptual framework for FNI research, including the basic relevant theoretical foundations, research methodologies and public datasets.

• Literature review
• Input–process–output framework
• Antecedents and consequences

Acknowledgements

The authors thank the editor and three anonymous reviewers for their constructive comments, criticisms and help in improving the paper. Yuanyuan Wu was supported in part by Joint PhD programmes (PolyU-HIT) leading to Dual Awards. Pengkun Wu was supported in part by National Natural Science Foundation of China (62001314), MOE (Ministry of Education in China) Project of Humanity and Social Science (20YJC630159), Foundational Research Funds of the Central Universities (YJ202008, SXYPY202106), From 0 to 1 Project of Sichuan University (2021CXC19), and International Visiting Program for Excellent Young Scholars of Sichuan University. Chong Wu was supported in part by National Natural Science Foundation of China (72131005).

Wu, Y. , Ngai, E.W.T. , Wu, P. and Wu, C. (2022), "Fake news on the internet: a literature review, synthesis and directions for future research", Internet Research , Vol. 32 No. 5, pp. 1662-1699. https://doi.org/10.1108/INTR-05-2021-0294

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• UConn Library
• Literature Review: The What, Why and How-to Guide
• Introduction

Literature Review: The What, Why and How-to Guide — Introduction

• Getting Started
• How to Pick a Topic
• Strategies to Find Sources
• Evaluating Sources & Lit. Reviews
• Tips for Writing Literature Reviews
• Writing Literature Review: Useful Sites
• Citation Resources
• Other Academic Writings

What are Literature Reviews?

So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries." Taylor, D.  The literature review: A few tips on conducting it . University of Toronto Health Sciences Writing Centre.

Goals of Literature Reviews

What are the goals of creating a Literature Review?  A literature could be written to accomplish different aims:

• To develop a theory or evaluate an existing theory
• To summarize the historical or existing state of a research topic
• Identify a problem in a field of research 

Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews .  Review of General Psychology , 1 (3), 311-320.

What kinds of sources require a Literature Review?

• A research paper assigned in a course
• A thesis or dissertation
• A grant proposal
• An article intended for publication in a journal

All these instances require you to collect what has been written about your research topic so that you can demonstrate how your own research sheds new light on the topic.

Types of Literature Reviews

What kinds of literature reviews are written?

Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified. The review ends with a conclusion section which summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.

• Example : Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework:  10.1177/08948453211037398  

Systematic review : "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139). Nelson, L. K. (2013). Research in Communication Sciences and Disorders . Plural Publishing.

• Example : The effect of leave policies on increasing fertility: a systematic review:  10.1057/s41599-022-01270-w

Meta-analysis : "Meta-analysis is a method of reviewing research findings in a quantitative fashion by transforming the data from individual studies into what is called an effect size and then pooling and analyzing this information. The basic goal in meta-analysis is to explain why different outcomes have occurred in different studies." (p. 197). Roberts, M. C., & Ilardi, S. S. (2003). Handbook of Research Methods in Clinical Psychology . Blackwell Publishing.

• Example : Employment Instability and Fertility in Europe: A Meta-Analysis:  10.1215/00703370-9164737

Meta-synthesis : "Qualitative meta-synthesis is a type of qualitative study that uses as data the findings from other qualitative studies linked by the same or related topic." (p.312). Zimmer, L. (2006). Qualitative meta-synthesis: A question of dialoguing with texts .  Journal of Advanced Nursing , 53 (3), 311-318.

• Example : Women’s perspectives on career successes and barriers: A qualitative meta-synthesis:  10.1177/05390184221113735

Literature Reviews in the Health Sciences

• UConn Health subject guide on systematic reviews Explanation of the different review types used in health sciences literature as well as tools to help you find the right review type
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• Last Updated: Sep 21, 2022 2:16 PM
• URL: https://guides.lib.uconn.edu/literaturereview

Libraries | Research Guides

Literature reviews, what is a literature review, learning more about how to do a literature review.

• Planning the Review
• The Research Question
• Choosing Where to Search
• Organizing the Review
• Writing the Review

A literature review is a review and synthesis of existing research on a topic or research question. A literature review is meant to analyze the scholarly literature, make connections across writings and identify strengths, weaknesses, trends, and missing conversations. A literature review should address different aspects of a topic as it relates to your research question. A literature review goes beyond a description or summary of the literature you have read. 

• Sage Research Methods Core Collection This link opens in a new window SAGE Research Methods supports research at all levels by providing material to guide users through every step of the research process. SAGE Research Methods is the ultimate methods library with more than 1000 books, reference works, journal articles, and instructional videos by world-leading academics from across the social sciences, including the largest collection of qualitative methods books available online from any scholarly publisher. – Publisher

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• Last Updated: May 2, 2024 10:39 AM
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Discovering the evolution of online reviews: A bibliometric review

• Research Paper
• Published: 22 September 2023
• Volume 33 , article number  49 , ( 2023 )

Cite this article

• Yucheng Zhang 1 ,
• Zhiling Wang 1 ,
• Lin Xiao   ORCID: orcid.org/0000-0002-2259-6357 2 ,
• Lijun Wang 3 &
• Pei Huang 4  

1002 Accesses

3 Citations

1 Altmetric

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As a rapidly developing topic, online reviews have aroused great interest among researchers. Although the existing research can help to explain issues related to online reviews, the scattered and diversified nature of previous research hinders an overall understanding of this area. Based on bibliometrics, this study analyzes 3089 primary articles and 100,783 secondary articles published between 2003 and 2022. We comprehensively and objectively describe the development status of online reviews, show the evolutionary process of the knowledge structure of online reviews, and suggest research directions based on the analysis results. This article validates and expands previous literature reviews, helps scholars understand relevant knowledge about online reviews, and contributes to the development of online reviews.

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Systematic Reviews: Characteristics and Impact

Constructing and evaluating automated literature review systems

Differences in Online Review Content between Old and New Products: An Abstract

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Acknowledgements

Yucheng Zhang recieved financial support provided by the national science foundation of china (grant nos. 71972065, 72272048, and 71872077) and the ministry of education of Project (grant no. 21JhQ088).

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Zhang, Y., Wang, Z., Xiao, L. et al. Discovering the evolution of online reviews: A bibliometric review. Electron Markets 33 , 49 (2023). https://doi.org/10.1007/s12525-023-00667-y

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