Combating Fake News on Social Media with Source Ratings: The Effects of User and Expert Reputation Ratings

Abstract

As a remedy against fake news on social media, we examine the effectiveness of three different mechanisms for source ratings that can be applied to articles when they are initially published: expert rating (where expert reviewers fact-check articles, which are aggregated to provide a source rating), user article rating (where users rate articles, which are aggregated to provide a source rating), and user source rating (where users rate the sources themselves). We conducted two experiments and found that source ratings influenced social media users’ beliefs in the articles and that the rating mechanisms behind the ratings mattered. Low ratings, which would mark the usual culprits in spreading fake news, had stronger effects than did high ratings. When the ratings were low, users paid more attention to the rating mechanism, and, overall, expert ratings and user article ratings had stronger effects than did user source ratings. We also noticed a second-order effect, where ratings on some sources led users to be more skeptical of sources without ratings, even with instructions to the contrary. A user’s belief in an article, in turn, influenced the extent to which users would engage with the article (e.g., read, like, comment and share). Lastly, we found confirmation bias to be prominent; users were more likely to believe — and spread — articles that aligned with their beliefs. Overall, our results show that source rating is a viable measure against fake news and propose how the rating mechanism should be designed.

Key words and phrases:

• fake news
• online misinformation
• social media
• combating fake news
• online article rating
• online source rating
• online expert rating
• online user rating
• fact-checking

Notes

1 In this work, by “source,” we mean any sites where “news” articles are published. Similar to how a “seller” on e-commerce site may be an individual or a corporation, we do not make the distinction between a site run by an individual or an institution. For instance, infowars.com is a well-known fake news site founded and run by a radio host Alex Jones. On the other hand, rt.com is a website run by the Russian government to spread propaganda.

2 We note that the term “fake news” has recently been employed by some as part of their strategy to discredit credible news or scientific sources. However, here, we use its proper definition of misinformation and disinformation.

3 We do not make the distinction between expert article and expert source as we do for user-rating methods. By definition, “experts” are professionals who conduct an extensive research to learn about the sources. Hence, an expert rates sources by considering many different aspects such as the sources’ backgrounds and the accuracy and reliability of their past articles [58]; a rating without considering the past articles would not fall into the category of “expert” rating. Therefore, it is not appropriate, nor meaningful, to have expert source ratings that ignore past articles.

4 All analyses were also checked by operationalizing confirmation bias as we did in Study 1. We observed no changes to our statistical conclusions.

Additional information

Notes on contributors

Antino Kim

Antino Kim ([email protected]; corresponding author) is an assistant professor of Information Systems and Grant Thornton Scholar at the Kelley School of Business, Indiana University. He earned his Ph.D. in Information Systems from the Foster School of Business at the University of Washington, His research interests include misinformation and social media, digital piracy and policy implications, supply chain of information goods, and IT and worker displacement. Dr. Kim’s work has appeared in Journal of Management Information Systems, Management Science, and MIS Quarterly, among other outlets.

Patricia L. Moravec

Patricia L. Moravec ([email protected]) is an assistant professor of Information Management at the McCombs School of Business, University of Texas at Austin. She earned her Ph.D. in Information Systems from the Kelley School of Business at Indiana University. Her research interests include misinformation and disaster response on social media. She previously served as the managing editor for MIS Quarterly Executive.

Alan R. Dennis

Alan R. Dennis ([email protected]) is Professor of Information Systems and holds the John T. Chambers Chair of Internet Systems in the Kelley School of Business at Indiana University. He has written more than 200 research papers and has won numerous awards for his theoretical and applied research. His research focuses on four main themes: fake news on social media; team collaboration; digital nudging; and information security. Dr. Dennis is the co-Editor-in-Chief of AIS Transactions on Replication Research. He also has written four books (two on data communications and networking, and two on systems analysis and design). He is a Fellow and President Elect of the Association for Information Systems.