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Don't be deceived: Using linguistic analysis to learn how to discern online review authenticity
Author(s) -
Banerjee Snehasish,
Chua Alton Y. K.,
Kim JungJae
Publication year - 2017
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23784
Subject(s) - exaggeration , guideline , intervention (counseling) , set (abstract data type) , selection (genetic algorithm) , psychology , test (biology) , computer science , linguistic analysis , natural language processing , linguistics , artificial intelligence , applied psychology , medicine , paleontology , philosophy , pathology , psychiatry , biology , programming language
This article uses linguistic analysis to help users discern the authenticity of online reviews. Two related studies were conducted using hotel reviews as the test case for investigation. The first study analyzed 1,800 authentic and fictitious reviews based on the linguistic cues of comprehensibility, specificity, exaggeration, and negligence. The analysis involved classification algorithms followed by feature selection and statistical tests. A filtered set of variables that helped discern review authenticity was identified. The second study incorporated these variables to develop a guideline that aimed to inform humans how to distinguish between authentic and fictitious reviews. The guideline was used as an intervention in an experimental setup that involved 240 participants. The intervention improved human ability to identify fictitious reviews amid authentic ones.