mlsanityData to 14 Oct 2026

How Do People Challenge Racial Stereotypes Online? Counter-Story Detection Across Reddit Communities

arXiv 2610.04803 ↗First posted 3 Oct 2026cs.CLcited by 0

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Abstract (arXiv)

Counter-storytelling is a powerful mechanism people use to challenge dominant narratives. Unlike other forms of counterspeech that have been widely studied in computational social science, counter-storytelling has largely been overlooked. Counter-stories are difficult to detect automatically; they are relational (defined with respect to expressions of racial stereotypes) and structurally diverse (drawing on stories that describe lived experiences, witnessed events, exemplars, and hypotheticals). We introduce a first framework for detecting and characterizing counter-storytelling against racial stereotypes at scale. This includes (1) a three-dimensional taxonomy grounded in narratology and Critical Race Theory and (2) a multi-stage pipeline that identifies relational pairs of stereotypes and counter-stories in noisy Reddit discourse. Using this pipeline, we annotate 25,549 Reddit posts across 615 communities and identify 1,312 counter-stories. Our analysis shows that speaker identity and post context shape how counter-stories are told. For example, in-group writers favor first-person testimony, often adopting the role of self-reflective insiders. Our work shows how computational methods can scale qualitative approaches to identify and characterize counter-storytelling as a contextual narrative practice, with implications for content moderation, narratology, and racial discourse analysis.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 64 references that are in the map. First posted on arXiv 3 Oct 2026.

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22 of 64 references.

References · 22

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2019Social Bias Frames: Reasoning about Social and Power Implications of Languagecited by 380
  3. 2019A Benchmark Dataset for Learning to Intervene in Online Hate Speechcited by 129
  4. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 125
  5. 2019CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speechcited by 110
  6. 2021Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speechcited by 82
  7. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 75
  8. 2020ConvoKit: A Toolkit for the Analysis of Conversationscited by 72
  9. 2020Generating Counter Narratives against Online Hate Speech: Data and Strategiescited by 59
  10. 2021Towards Knowledge-Grounded Counter Narrative Generation for Hate Speechcited by 42
  11. 2022CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeechcited by 29
  12. 2023Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Languagecited by 24
10 more
  1. 2024NLP for Counterspeech against Hate: A Survey and How-To Guidecited by 22
  2. 2024NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helpscited by 20
  3. 2023Where Do People Tell Stories Online? Story Detection Across Online Communitiescited by 17
  4. 2024Counterspeakers' Perspectives: Unveiling Barriers and AI Needs in the Fight against Online Hatecited by 15
  5. 2021Multilingual Counter Narrative Type Classificationcited by 10
  6. 2023StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variablescited by 5
  7. 2025When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Modelscited by 5
  8. 2024Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Mediacited by 2
  9. 2024Behind the Counter: Exploring the Motivations and Barriers of Online Counterspeech Writingcited by 2
  10. 2025Racism, Resistance, and Reddit: How Popular Culture Sparks Online Reckoningscited by 1

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