Journal
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
Volume 33, Issue 2, Pages 456-481Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/07421222.2016.1205907
Keywords
deception detection; eWoM; electronic word of mouth; feature pruning; fake online reviews; online reviewer behavior; user-generated content
Funding
- National Science Foundation [695SES 1527684]
- Divn Of Social and Economic Sciences [1527684] Funding Source: National Science Foundation
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The value and credibility of online consumer reviews are compromised by significantly increasing yet difficult-to-identify fake reviews. Extant models for automated online fake review detection rely heavily on verbal behaviors of reviewers while largely ignoring their nonverbal behaviors. This research identifies a variety of nonverbal behavioral features of online reviewers and examines their relative importance for the detection of fake reviews in comparison to that of verbal behavioral features. The results of an empirical evaluation using real-world online reviews reveal that incorporating nonverbal features of reviewers can significantly improve the performance of online fake review detection models. Moreover, compared with verbal features, nonverbal features of reviewers are shown to be more important for fake review detection. Furthermore, model pruning based on a sensitivity analysis improves the parsimony of the developed fake review detection model without sacrificing its performance.
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