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

2026-10-03

AI Summary

This study addresses the challenge of automatically detecting online counter-narratives against racial stereotypes by proposing the first large-scale computational framework for counter-narrative detection. Methodologically, it integrates narratology with critical race theory to construct a taxonomy and develops a multi-stage natural language processing pipeline that operationalizes qualitative analysis into scalable text mining. By annotating 25,000 Reddit posts, the framework successfully identifies 1,312 counter-narrative instances. This work effectively bridges the gap between qualitative theory and quantitative methods in computational social science, elucidates how identity and context shape narrative strategies, and establishes a novel paradigm for intervening in online prejudice.

Abstract

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.

Problem

• counter-storytelling
• racial stereotypes
• automatic detection
• computational social science
• online discourse

Innovation

• Counter-storytelling
• Racial stereotypes
• Multi-stage pipeline
• Taxonomy
• Computational social science