用大模型解析虚假评论中的隐含语言模式，提升可读性与可信度判断。
Why is "Chicago" Predictive of Deceptive Reviews? Using LLMs to Discover Language Phenomena from Lexical Cues
- 通过猜想-验证框架，将复杂词元转化为人类可理解的语言现象。
- 发现'Chicago'等词汇与虚假评论强相关，预测准确率显著优于纯先验知识。
- 适合关注在线信誉评估、内容可信度分析的研究者与平台管理者。
虚假评论误导消费者，损害企业利益，破坏在线市场信任。机器学习分类器可通过大量数据区分真假评论，但其识别出的特征往往微妙、零散且难以理解，影响用户认知与信任。本文研究大语言模型（LLMs）能否将这些难以解读的词元线索转化为人类可懂的语言现象。提出‘猜想-验证’框架，结果表明，此类语言现象在数据中具有实证基础，跨领域可泛化，且比基于模型先验知识或上下文学习所得现象更具预测力。该方法有助于在无检测模型可用的场景下，辅助人们批判性评估网络评论可信度。
原文摘要 · Abstract (English)
Deceptive reviews mislead consumers, harm businesses, and undermine trust in online marketplaces. Machine learning classifiers can learn from large amounts of data to distinguish deceptive reviews from genuine ones. However, the distinguishing features learned by these classifiers are often subtle, fragmented, and difficult for humans to interpret, which can hinder user understanding and trust. In this work, we study whether large language models (LLMs) can translate such unintuitive lexical cues into human-understandable language phenomena. We propose a conjecture-then-validate framework, and show that language phenomena obtained in this manner are empirically grounded in data, generalizable across similar domains, and more predictive than phenomena derived from LLMs' prior knowledge or in-context learning. Such phenomena can aid people in critically assessing the credibility of online reviews in environments where deception detection classifiers are unavailable.
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