74篇论文分析揭示NLP反仇恨言论研究与真实社区脱节
Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech
- 系统回顾74项研究，追踪利益相关方参与度
- 发现现有方法多依赖旧数据集，缺乏受影响群体意见
- 与5个性别暴力防治组织合作，提出社区驱动的生成方案
本文系统回顾了74篇关于反仇恨言论（counterspeech）的NLP研究，分析利益相关方在数据构建、模型开发和评估中的参与程度。为补充该分析，我们与5家专注于在线性别暴力（oGBV）的非政府组织开展参与式案例研究，提炼出符合实际需求的反仇恨言论生成实践。结果表明，当前NLP研究与最受网络暴力影响的群体之间存在日益加剧的脱节。文章最后提出具体建议，呼吁将利益相关方的专业知识重新置于反仇恨言论研究的核心位置。
原文摘要 · Abstract (English)
Counterspeech, i.e. the practice of responding to online hate speech, has gained traction in NLP as a promising intervention. While early work emphasised collaboration with non-governmental organisation stakeholders, recent research trends have shifted toward automated pipelines that reuse a small set of legacy datasets, often without input from affected communities. This paper presents a systematic review of 74 NLP studies on counterspeech, analysing the extent to which stakeholder participation influences dataset creation, model development, and evaluation. To complement this analysis, we conducted a participatory case study with five NGOs specialising in online Gender-Based Violence (oGBV), identifying stakeholder-informed practices for counterspeech generation. Our findings reveal a growing disconnect between current NLP research and the needs of communities most impacted by toxic online content. We conclude with concrete recommendations for re-centring stakeholder expertise in counterspeech research.
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