The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews
Abstract
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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Jindal, N., & Liu, B. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341560 · Cited by 1,542
Media, Tasks, and Communication Processes: A Theory of Media Synchronicity1
Dennis, A. R., Fuller, R. M., & Valacich, J. S. (2008) · MIS Quarterly · doi:10.2307/25148857 · Cited by 1,511
Spotting fake reviewer groups in consumer reviews
Mukherjee, A., Liu, B., & Glance, N. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187863 · Cited by 773
Spotting opinion spammers using behavioral footprints
Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487580 · Cited by 426
Cited by
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Zhou, X., & Zafarani, R. (2021) · ACM Computing Surveys · doi:10.1145/3395046 · Cited by 1,184
Using natural language processing to analyse text data in behavioural science
Feuerriegel, S., Maarouf, A., Bär, D., Geissler, D., Schweisthal, J., Pröllochs, N., Robertson, C. E., Rathje, S., Hartmann, J., Mohammad, S. M., et al. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-024-00392-z · Cited by 140
Baker, M. A., & Kim, K. (2019) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-03-2018-0247 · Cited by 122
Ben Jabeur, S., Ballouk, H., Ben Arfi, W., & Sahut, J.-M. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113631 · Cited by 70
Kovács, B. (2024) · Marketing Letters · doi:10.1007/s11002-024-09729-3 · Cited by 44
Metadata
- Authors
- Zhang, D., Zhou, L., Kehoe, J. L., & Kilic, I. Y.
- Venue
- Journal of Management Information Systems
- Publisher
- Informa UK Limited
- Published
- 2016-04-02
- Volume
- 33
- Issue
- 2
- Pages
- 456-481
- ISSN
- 0742-1222 (ISSN-L)
- 1557-928X
- OpenAlex
- W2554367394
- Cited by
- 315
- References
- 4
- FWCI
- 30.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, D., Zhou, L., Kehoe, J. L., & Kilic, I. Y. (2016). What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews. Journal of Management Information Systems. https://doi.org/10.1080/07421222.2016.1205907
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