From conflicts and confusion to doubts: Examining review inconsistency for fake review detection

Inconsistency in online consumer reviews (OCRs) may cause uncertainty and confusion to consumers when they make purchase decisions. However, there is a lack of a systematic and empirical investigation of review inconsistency in the literature. This research characterizes review inconsistency from multiple aspects, including rating-sentiment, content, and language, and proposes hypotheses about their effects on fake OCR detection by drawing upon deception and attitude-behavior consistency theories. We characterize review inconsistency with 22 features, and test the hypotheses with machine learning models developed for fake OCR detection. Our empirical evaluation results using real OCRs not only confirm the presence of review inconsistency, but also demonstrate significant positive effects of review inconsistency on the performance of fake OCR detection. The research findings have important implications for improving the effectiveness of consumer decision making and the trustworthiness of OCRs.

Automated summary

This research examines the inconsistency in online consumer reviews (OCRs) and its impact on fake OCR detection. The study characterizes review inconsistency from multiple aspects and confirms its positive effects on the performance of fake OCR detection through empirical evaluation using real OCRs. The research findings have significant implications for improving consumer decision making and the trustworthiness of OCRs.