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Toxic Synergy Between Hate Speech and Fake News Exposure

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arxiv 2404.08110 v1 pith:ZZZZAHYC submitted 2024-04-11 cs.CY cs.SI

Toxic Synergy Between Hate Speech and Fake News Exposure

classification cs.CY cs.SI
keywords hatespeechnewssourcesexposurefakefindlow-credibility
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Hate speech on social media is a pressing concern. Understanding the factors associated with hate speech may help mitigate it. Here we explore the association between hate speech and exposure to fake news by studying the correlation between exposure to news from low-credibility sources through following connections and the use of hate speech on Twitter. Using news source credibility labels and a dataset of posts with hate speech targeting various populations, we find that hate speakers are exposed to lower percentages of posts linking to credible news sources. When taking the target population into account, we find that this association is mainly driven by anti-semitic and anti-Muslim content. We also observe that hate speakers are more likely to be exposed to low-credibility news with low popularity. Finally, while hate speech is associated with low-credibility news from partisan sources, we find that those sources tend to skew to the political left for antisemitic content and to the political right for hate speech targeting Muslim and Latino populations. Our results suggest that mitigating fake news and hate speech may have synergistic effects.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchanges

    cs.CL 2026-06 unverdicted novelty 8.0

    Presents a new expert-curated dataset of multi-turn counterspeech dialogues in five languages targeting hate against seven groups, with span annotations linking to verified external knowledge for RAG applications.

  2. Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation

    cs.CL 2026-05 conditional novelty 6.0

    LLMs generate adequate counterspeech for co-occurring hate and misinformation in 40% of cases, with a mixed knowledge strategy from fact-checkers and NGOs proving most effective after expert revision.