Don't be deceived: Using linguistic analysis to learn how to discern online review authenticity

Snehasish Banerjee, Alton Y.K. Chua, Jung Jae Kim

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1525-1538
Number of pages14
JournalJournal of the Association for Information Science and Technology
Volume68
Issue number6
DOIs
Publication statusPublished - Jun 1 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 ASIS&T

ASJC Scopus Subject Areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

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