Skip to content
aiqbench takeAI & Society

aiqbench analysis

This research presents a novel computational framework for detecting counter-storytelling, a specific form of counterspeech used to challenge racial stereotypes within noisy online discourse. By combining narratology with Critical Race Theory, the study develops a scalable pipeline that identifies relational pairs of stereotypes and lived-experience narratives across thousands of Reddit communities. The work demonstrates how advanced NLP techniques can operationalize qualitative social science methods to better understand and moderate complex racial dynamics on the internet.
Source coveringarXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

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

arXiv2 min read

arXiv:2610.04803v1 Announce Type: new 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.

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.

Accepted to EMNLP 2026 (Main Conference). 28 pages, 12 figures, 24 tables. Content warning: this paper contains examples of racial stereotypes that may be upsetting or offensive. Code: this https URL

Computation and Language (cs.CL)

arXiv:2610.04803 [cs.CL]

(or arXiv:2610.04803v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.04803 arXiv-issued DOI via DataCite (pending registration)

Full text under the publisher's open licence · 230 words

Related coverage