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

2021
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.
DECISION SUPPORT SYSTEMS
卷号:144
ISSN:0167-9236
收录类型
SSCI
发表日期
2021
学科领域
循证管理学
国家
美国
语种
英语
DOI
10.1016/j.dss.2021.113513
EISSN
1873-5797
资助机构
U.S. National Science FoundationNational Science Foundation (NSF) [CNS 1704800, SES 1527684]
资助信息
This research is partially supported by U.S. National Science Foundation (Award #s: CNS 1704800 and SES 1527684) . Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of NSF.
被引频次(WOS)
1
被引更新日期
2022-01
来源机构
Pennsylvania Commonwealth System of Higher Education (PCSHE) Temple University University of North Carolina University of North Carolina Charlotte
关键词
Online consumer reviews (OCRs) Review inconsistency Fake review detection Sentiment analysis