The Virality of Hate Speech on Social Media

被引：7
作者：
Maarouf A. [1 ]
Pröllochs N. [2 ]
Feuerriegel S. [1 ]
机构：
[1] LMU Munich, Munich Center for Machine Learning
来源：
Proceedings of the ACM on Human-Computer Interaction | 2024年 / 8卷 / CSCW1期
关键词：
content spreading; Hate speech; regression analysis; social media; Twitter/X; virality;
D O I：
10.1145/3641025
中图分类号：
学科分类号：
摘要：
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. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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