The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Towards a General Rule for Identifying Deceptive Opinion Spam
Open accessAbstract
Consumers' purchase decisions are increasingly influenced by user-generated online reviews.Accordingly, there has been growing concern about the potential for posting deceptive opinion spamfictitious reviews that have been deliberately written to sound authentic, to deceive the reader.In this paper, we explore generalized approaches for identifying online deceptive opinion spam based on a new gold standard dataset, which is comprised of data from three different domains (i.e.Hotel, Restaurant, Doctor), each of which contains three types of reviews, i.e. customer generated truthful reviews, Turker generated deceptive reviews and employee (domain-expert) generated deceptive reviews.Our approach tries to capture the general difference of language usage between deceptive and truthful reviews, which we hope will help customers when making purchase decisions and review portal operators, such as TripAdvisor or Yelp, investigate possible fraudulent activity on their sites.1
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Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
DePaulo, B. M., Lindsay, J. J., Malone, B. E., Muhlenbruck, L., Charlton, K., & Cooper, H. (2003) · Psychological Bulletin · doi:10.1037/0033-2909.129.1.74 · Cited by 2,479
Accuracy of Deception Judgments
Bond, C. F., & DePaulo, B. M. (2006) · Personality and Social Psychology Review · doi:10.1207/s15327957pspr1003_2 · Cited by 1,953
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
Finding Deceptive Opinion Spam by Any Stretch of the Imagination
Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011) · arXiv · doi:10.48550/arxiv.1107.4557 · Cited by 1,008
Self-citationSpotting 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
False Information on Web and Social Media: A Survey
Kumar, S., & Shah, N. (2018) · arXiv · doi:10.48550/arxiv.1804.08559 · Cited by 289
When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications
Netzer, O., Lemaire, A., & Herzenstein, M. (2019) · Journal of Marketing Research · doi:10.1177/0022243719852959 · Cited by 238
Learning from crowdsourced labeled data: a survey
Zhang, J., Wu, X., & Sheng, V. S. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9491-9 · Cited by 203
Hajek, P., Barushka, A., & Munk, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04757-2 · Cited by 186
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Yao, Y., Viswanath, B., Cryan, J., Zheng, H., & Zhao, B. Y. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133990 · Cited by 150
Metadata
- Authors
- Li, J., Ott, M., Cardie, C., & Hovy, E.
- Venue
- Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2014
- Pages
- 1566-1576
- OpenAlex
- W2251645975
- Cited by
- 362
- References
- 7
- FWCI
- 19.66
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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