用大模型分析社交媒体与市政会议，发现公众对无家可归者存在严重偏见。
"Not in My Backyard": LLMs Uncover Online and Offline Social Biases Against Homelessness
- 构建首个跨领域无家可归者偏见语料库，涵盖16类标签
- 大模型普遍高估‘别在我家门口’情绪（+11.5个百分点）
- 揭示模型误判机制，适合城市政策研究与偏见监测
无家可归是全球性社会难题，2025年美国记录的无家可归者超过87.6万人。社会偏见显著阻碍缓解该问题，影响公众认知与政策制定。在线文本媒体与线下市政议会讨论均反映并塑造公众意见，是识别与追踪无家可归者偏见的重要信号源。本文发布首个多领域无家可归者偏见语料库，采用16类别多标签分类体系：包含由合作训练标注员标注的1,698条分层金标准样本，以及48,389条GPT-4.1标注文本，数据来源覆盖10个美国城市（2015–2025）的Reddit、X（原Twitter）、新闻报道与市政会议记录。在金标准集上评估六种提示式大模型，结合F1分数与流行率差距审计。结果显示，尽管平均F1表现中等，但普遍存在严重校准偏差：所有模型均过度标记‘别在我家门口’（NIMBY）倾向（+11.5个百分点），同时低估事实性陈述（-30.5个百分点）。共识误报分析表明，模型将住房相关词汇与疑问句形式误认为反对信号，导致在支持服务的文本中产生大量假阳性。该语料库与审计协议可支持市政层面无家可归者污名监测，无需将标注者标签视为绝对真实。
原文摘要 · Abstract (English)
Homelessness is a persistent social challenge, impacting millions worldwide. Over 876,000 people experiencing homelessness (PEH) were recorded in the U.S. in 2025. Social bias is a significant barrier to alleviating homelessness, shaping public perception and influencing policymaking. Because online textual media and offline city council discourse both reflect and influence public opinion, they provide valuable signals for identifying and tracking social biases against PEH. We release the first multi-domain PEH bias corpus with a 16-category multi-label taxonomy: a 1,698-item stratified gold-standard set annotated by partner-trained raters, plus 48,389 GPT-4.1-labeled texts, drawn from Reddit, X (formerly Twitter), news, and council meeting transcripts across ten U.S. cities (2015-2025). We benchmark six prompted LLMs on the gold-standard set and complement F1 with prevalence-gap audits. Moderate F1 coexists with large miscalibration: every model over-tags "not in my backyard" (NIMBY) (+11.5 pp) and under-detects factual claims (-30.5 pp). Error analysis on consensus false positives reveals that models treat housing vocabulary and question form as opposition proxies, producing NIMBY false positives on pro-service text. The corpus and audit protocol support municipal PEH stigma monitoring without treating teacher labels as ground truth.
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