Behind the Stars: The Effects of User and Expert Reputation Ratings on Users’ Belief in Fake News on Social Media
53 Pages Posted: 22 Dec 2017 Last revised: 10 Oct 2018
Date Written: December 18, 2017
The rise of fake news has become a major concern for social media and their users. Many researchers are working on approaches to fact-check articles, but article fact-checking usually appears several days after a fake article has been published. Based on reputation theory, 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.
Keywords: Fake news, misinformation, social media, article rating, source rating, expert rating, user rating, fact-checking
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