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BWL XI: Paper accepted at WWW

A new research paper has been accepted for publication at The Web Conference (WWW). The Web Conference is a flagship conference in data science with a very low acceptance rate (CORE Ranking A*).

Title: Believability and Harmfulness Shape the Virality of Misleading Social Media Posts 
Authors: Drolsbach C, Pröllochs N



Misinformation on social media presents a major threat to modern societies. While previous research has analyzed the virality across true and false social media posts, not every misleading post is necessarily equally viral. Rather, misinformation has different characteristics and varies in terms of its believability and harmfulness – which might influence its spread. In this study, we study how the perceived believability and harmfulness of misleading posts are associated with their virality on social media. Specifically, we empirically analyze a large sample of crowd-annotated social media posts from Twitter's Birdwatch platform, on which users can rate the believability and harmfulness of misleading tweets. To address our research questions, we implement an explanatory regression model and link the crowd ratings for believability and harmfulness to the virality of misleading posts on Twitter. Our findings imply that misinformation that is (i) easily believable and (ii) not particularly harmful is associated with more viral resharing cascades. These results offer insights into how different kinds of crowd fact-checked misinformation spreads and suggest that viral misinformation on social media is often not particularly concerning from the perspective of public safety. From a practical view, our findings may help platforms to develop more effective strategies to curb the proliferation of misleading posts on social media.

A preprint of the paper will become available soon.