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.