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Cited by 35Hate Speech and Cyberbullying DetectionMisinformation and Its ImpactsSocial Media and Politics

The Virality of Hate Speech on Social Media

Open access
Maarouf, Abdurahman, Pröllochs, Nicolas, Feuerriegel, Stefan·2024·Proceedings of the ACM on Human-Computer Interaction
DOI 10.1145/3641025
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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.

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  • Los mensajes de odio publicados por cuentas verificadas han mostrado tener una mayor probabilidad de alcanzar un público amplio debido a su alta credibilidad percibida.

    “los mensajes de odio publicados por estas cuentas han mostrado tener una mayor probabilidad de alcanzar un público amplio debido a su alta credibilidad percibida (Maarouf et al., 2024;Mathew et al., 2020).”

    From Redes de odio y contranarrativas en Twitter sobre el ataque a iglesias católicas en Algeciras: Dinámicas de conectividad, influencia y polarización (2026) · cites this paper as Maarouf et al., 2024

    The Results (p. 9) find that hateful content from verified users spreads more widely. The Introduction (p. 1) proposes greater credibility and trustworthiness as a reason others may share it, but the paper does not establish perceived credibility as the cause of the observed spread. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 38

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Cited by

Sorted by citations

Authors
Maarouf, A., Pröllochs, N., & Feuerriegel, S.
Affiliations
  • Munich Center for Machine Learning (DE)
  • Justus-Liebig-Universität Gießen (DE)
ORCID
Venue
Proceedings of the ACM on Human-Computer Interaction
Publisher
Association for Computing Machinery (ACM)
Published
2024-04-17
Volume
8
Issue
CSCW1
Pages
1-22
ISSN
2573-0142 (ISSN-L)
DOI
10.1145/3641025
OpenAlex
W4396218551
Cited by
35
References
3
Citations by year
2026: 12 · 2025: 14 · 2024: 5 · 2023: 4
FWCI
4.58
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Communication › Social Media and Politics
Keywords
online hate speech, virality, social media, retweets, cascade size, user verification, Twitter

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