Journalartikel

Jahr der Veröffentlichung: 2024

Zeitschrift: Proceedings of the ACM on Human-Computer Interaction

Bandnummer: 8

Heftnummer: 1

DOI Link: https://doi.org/10.1145/3641025

Verlag: Association for Computing Machinery (ACM)

Abstract: 

Online hate speech is responsible for violent attacks such as, e.g., the Pittsburgh synagogue shooting in 2018, thereby posing a significant threat to vulnerable groups and society in general. However, little is known about what makes hate speech on social media go viral. In this paper, we collect N = 25,219 cascades with 65,946 retweets from X (formerly known as Twitter) and classify them as hateful vs. normal. Using a generalized linear regression, we then estimate differences in the spread of hateful vs. normal content based on author and content variables. We thereby identify important determinants that explain differences in the spreading of hateful vs. normal content. For example, hateful content authored by verified users is disproportionally more likely to go viral than hateful content from non-verified ones: hateful content from a verified user (as opposed to normal content) has a 3.5 times larger cascade size, a 3.2 times longer cascade lifetime, and a 1.2 times larger structural virality. Altogether, we offer novel insights into the virality of hate speech on social media.

Harvard-Zitierstil: Maarouf, A., Pröllochs, N. and Feuerriegel, S. (2024) The Virality of Hate Speech on Social Media, Proceedings of the ACM on human-computer interaction, 8(1), Article 186. https://doi.org/10.1145/3641025

APA-Zitierstil: Maarouf, A., Pröllochs, N., & Feuerriegel, S. (2024). The Virality of Hate Speech on Social Media. Proceedings of the ACM on human-computer interaction. 8(1), Article 186. https://doi.org/10.1145/3641025