Konferenzpaper

Erschienen in: Proceedings of the ACM Web Conference 2023

Herausgeberliste: Ding, Ying; Tang, Jie; Sequeda, Juan

Jahr der Veröffentlichung: 2023

Seiten: 4172-4177

eISBN: 978-1-4503-9416-1

DOI Link: https://doi.org/10.1145/3543507.3583857

Konferenz: The ACM Web Conference 2023 (WWW '23)

Abstract: 

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 work, we study how the perceived believability and harmfulness of misleading posts are associated with their virality on social media. Specifically, we analyze (and validate) 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 the most viral misleading posts are often not the ones that are 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.

Harvard-Zitierstil: Drolsbach, C. and Pröllochs, N. (2023) Believability and Harmfulness Shape the Virality of Misleading Social Media Posts, in Ding, Y., Tang, J. and Sequeda, J. (eds.) Proceedings of the ACM Web Conference 2023. New York: Association for Computing Machinery. pp. 4172-4177. https://doi.org/10.1145/3543507.3583857

APA-Zitierstil: Drolsbach, C., & Pröllochs, N. (2023). Believability and Harmfulness Shape the Virality of Misleading Social Media Posts. In Ding, Y., Tang, J., & Sequeda, J. (Eds.), Proceedings of the ACM Web Conference 2023. (pp. 4172-4177). Association for Computing Machinery. https://doi.org/10.1145/3543507.3583857