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. •Proposing three types of inconsistencies of online consumer reviews.•Proposing 22 features to measure the three types of review inconsistencies.•Improving the performance of fake online consumer review detection by incorporating review inconsistency features.•Examining the importance of review inconsistency features to fake online consumer review detection.