Why is "Chicago" Predictive of Deceptive Reviews? Using LLMs to Discover Language Phenomena from Lexical Cues
Year: 2026
Type: conference-paper
Abstract: 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-thenvalidate 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.
Authors: Jiaming Qu, Mengtian Guo, Yue Wang
Institution: University of North Carolina at Chapel Hill
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416660112