发表机构
IBM; Carnegie Mellon University; University of Colorado Boulder（IBM; 卡内基梅隆大学; 科罗拉多大学博尔德分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出首个大规模检测对抗叙事的框架，包含三维分类体系与多阶段流水线，在25,549条Reddit帖子中识别出1,312条对抗叙事，揭示发言者身份与语境对叙事方式的影响。
AI 中文摘要
对抗叙事（counter-storytelling）是人们用来挑战主流叙事的一种强大机制。与计算社会科学中已被广泛研究的其他形式的对抗言论（counterspeech）不同，对抗叙事在很大程度上被忽视了。对抗叙事难以自动检测；它们具有关系性（相对于种族刻板印象的表达而定义）和结构多样性（借鉴描述生活经历、目击事件、范例和假设情景的故事）。我们首次引入了一个用于大规模检测和刻画针对种族刻板印象的对抗叙事的框架。这包括（1）一个基于叙事学和批判种族理论的三维分类体系，以及（2）一个多阶段流水线，用于在嘈杂的Reddit话语中识别刻板印象与对抗叙事的关系对。利用该流水线，我们对来自615个社区的25,549条Reddit帖子进行了标注，并识别出1,312条对抗叙事。我们的分析表明，发言者身份和帖子语境塑造了对抗叙事的讲述方式。例如，群体内作者倾向于使用第一人称证词，通常扮演自我反思的内部人角色。我们的工作展示了计算方法如何扩展定性方法，以识别和刻画作为情境化叙事实践的对抗叙事，这对内容审核、叙事学和种族话语分析具有重要意义。
英文摘要
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.
CommentsAccepted 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: https://github.com/UmaGunturi/counter_story_detection
