用新方法从社交媒体中挖掘隐藏的政治暗语
Making FETCH! Happen: Finding Emergent Dog Whistles Through Common Habitats
- 结合向量数据库与大模型，自动搜寻新型隐晦表达
- 在三个社交平台案例中，现有系统基本失效
- 适合关注舆情监控与内容安全的研究者
狗哨（Dog whistles）是具有双重含义的编码表达：对外公开传递普通信息，对特定群体则传递隐含意图。此类表达常被用于暗示争议性政治立场，同时规避内容审核。现有识别方法依赖人工维护词典，难以及时更新。本文提出FETCH!任务，旨在从海量社交媒体数据中发现新型狗哨。实验表明，当前主流系统在三个不同社交平台案例中均无法取得有效结果。为此，我们提出EarShot，一种融合向量数据库与大语言模型优势的强基线系统，可高效准确地识别新出现的狗哨表达。
原文摘要 · Abstract (English)
WARNING: This paper contains content that maybe upsetting or offensive to some readers. Dog whistles are coded expressions with dual meanings: one intended for the general public (outgroup) and another that conveys a specific message to an intended audience (ingroup). Often, these expressions are used to convey controversial political opinions while maintaining plausible deniability and slip by content moderation filters. Identification of dog whistles relies on curated lexicons, which have trouble keeping up to date. We introduce FETCH!, a task for finding novel dog whistles in massive social media corpora. We find that state-of-the-art systems fail to achieve meaningful results across three distinct social media case studies. We present EarShot, a strong baseline system that combines the strengths of vector databases and Large Language Models (LLMs) to efficiently and effectively identify new dog whistles.
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