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. L ow 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.