Detecting Fake Reviews: Exploring the Linguistic Characteristics by Computerized Text Analysis

Detecting Fake Reviews: Exploring the Linguistic Characteristics by Computerized Text Analysis

Online consumer reviews have become the most important basis for online shopping and product sales. Fake reviews are generated to boost sales because online consumer reviews play a vital role in consumers' decision making. The prevalence of fake reviews violates the regulations of the online business environment and misleads consumers in decision making. Thus, the present research investigates the effects of reviews' linguistic characteristics (i.e., analytical thinking, authenticity) on review fakeness. Specifically, this research examines whether (1) the level of analytical thinking is lower for fake (vs. genuine) reviews (hypothesis 1) and (2) the level of authenticity is lower for fake (vs. genuine) reviews (hypothesis 2). This research analyzed user-generated hotel reviews (genuine reviews, fake reviews) collected from MTurk. Linguistic Inquiry and Word Count (LIWC) 2022 was adopted to code review contents, and the hypotheses were tested using logistic regression. Consistent with the hypotheses 1 and 2, the results indicate that (1) analyticial thinking is negatively associated with review fakeness; and (2) authenticity is negatively associated with review fakeness. The findings provide important implications to identify fake reviews based on linguistic characteristics.