通过嵌入空间分析用户词汇关联，区分真假账号与思想倾向
POLAR:A Per-User Association Test in Embedding Space
- 用微调语言模型的嵌入向量表示作者，投影到人工标注词轴上
- 在推特和极端论坛数据中准确识别机器人账号与右倾趋势
- 模块化设计，适合社交科学研究者快速部署新属性检测
大多数内在关联探测器在词、句子或语料层面运行，掩盖了作者级别的差异。我们提出POLAR（按用户词汇关联报告），一种在轻度微调掩码语言模型嵌入空间中运行的用户级词汇关联测试。作者由私有的确定性标记表示；POLAR将这些向量投影到人工构建的词汇轴上，并报告经过置换检验的p值与Benjamini-Hochberg校正后的标准化效应。在平衡的机器人-真人推特基准测试中，POLAR能清晰区分大型语言模型驱动的机器人与真实账户；在极端主义论坛中，它量化了与侮辱性词汇库的高度对齐，并揭示了时间上的右倾漂移。该方法可灵活扩展至新属性集，为计算社会科学提供简洁的单用户诊断工具。所有代码公开于 https://github.com/pedroaugtb/POLAR-A-Per-User-Association-Test-in-Embedding-Space。
原文摘要 · Abstract (English)
Most intrinsic association probes operate at the word, sentence, or corpus level, obscuring author-level variation. We present POLAR (Per-user On-axis Lexical Association Re-port), a per-user lexical association test that runs in the embedding space of a lightly adapted masked language model. Authors are represented by private deterministic to-kens; POLAR projects these vectors onto curated lexicalaxes and reports standardized effects with permutation p-values and Benjamini--Hochberg control. On a balanced bot--human Twitter benchmark, POLAR cleanly separates LLM-driven bots from organic accounts; on an extremist forum,it quantifies strong alignment with slur lexicons and reveals rightward drift over time. The method is modular to new attribute sets and provides concise, per-author diagnostics for computational social science. All code is publicly avail-able at https://github.com/pedroaugtb/POLAR-A-Per-User-Association-Test-in-Embedding-Space.
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