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Cited by 15Hate Speech and Cyberbullying DetectionComplex Network Analysis TechniquesMisinformation and Its Impacts

Disrupting networks of hate: characterising hateful networks and removing critical nodes

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Alorainy, Wafa, Burnap, Pete, Liu, Han, Williams, Matthew, Giommoni, Luca·2022·Social Network Analysis and Mining
DOI 10.1007/s13278-021-00818-z
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Abstract

Hateful individuals and groups have increasingly been using the Internet to express their ideas, spread their beliefs and recruit new members. Understanding the network characteristics of these hateful groups could help understand individuals’ exposure to hate and derive intervention strategies to mitigate the dangers of such networks by disrupting communications. This article analyses two hateful followers’ networks and three hateful retweet networks of Twitter users who post content subsequently classified by human annotators as containing hateful content. Our analysis shows similar connectivity characteristics between the hateful followers networks and likewise between the hateful retweet networks. The study shows that the hateful networks exhibit higher connectivity characteristics when compared to other “risky” networks, which can be seen as a risk in terms of the likelihood of exposure to, and propagation of, online hate. Three network performance metrics are used to quantify the hateful content exposure and contagion: giant component (GC) size, density and average shortest path. In order to efficiently identify nodes whose removal reduced the flow of hate in a network, we propose a range of structured node-removal strategies and test their effectiveness. Results show that removing users with a high degree is most effective in reducing the hateful followers network connectivity (GC, size and density), and therefore reducing the risk of exposure to cyberhate and stemming its propagation.

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  • En las redes de odio, la interacción intensiva entre usuarios que retuitean mensajes dentro del grupo genera un efecto de contagio que acelera la difusión del contenido.

    “Un aspecto crucial de estas redes es la interacción intensiva entre los usuarios, quienes tienden a retuitear los mensajes de otros dentro del grupo, lo que genera un efecto de contagio que acelera la difusión del contenido (Alorainy et al., 2022).”

    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 Alorainy et al., 2022

    Section 4.2 reports reciprocal retweeting and substantial engagement with hateful messages; the Conclusion describes greater reach of content contagion through these networks. Those passages support retweet-driven propagation, but they measure reach and network structure, not whether content spreads faster over time. Page numbers are unavailable in our copy. (LLM, conf=0.85)

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Cited by

Sorted by citations

Authors
Alorainy, W., Burnap, P., Liu, H., Williams, M., & Giommoni, L.
Affiliations
  • Shaqra University (SA)
  • Cardiff University (GB)
  • Shenzhen University (CN)
Corresponding author
  • Alorainy, Wafa
  • Burnap, Pete
  • Liu, Han
  • Williams, Matthew
  • Giommoni, Luca
ORCID
Venue
Social Network Analysis and Mining
Publisher
Springer Science and Business Media LLC
Published
2022-01-31
Volume
12
Issue
1
ISSN
  • 1869-5450 (ISSN-L)
  • 1869-5469
DOI
10.1007/s13278-021-00818-z
OpenAlex
W3216275653
Cited by
15
References
3
Citations by year
2026: 6 · 2025: 3 · 2024: 3 · 2023: 1 · 2022: 2
FWCI
0.74
Type
journal article
Language
en
Open access
Licence
Locations
  • Social Network Analysis and Mining (publishedVersion, cc-by)
  • ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
Funders
Centre for Cyberhate Research and Policy: Real-Time Scalable Methods & Infrastructure for Modelling the Spread of Cyberhate on Social Media (ES/P010695/1)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Keywords
online hate, hate speech, Twitter, retweet networks, network connectivity, giant component, node removal, degree centrality, network disruption

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