The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Distributional Footprints of Deceptive Product Reviews
Open accessAbstract
This paper postulates that there are natural distributions of opinions in product reviews. In particular, we hypothesize that for a given domain, there is a set of representative distributions of review rating scores. A deceptive business entity that hires people to write fake reviews will necessarily distort its distribution of review scores, leaving distributional footprints behind. In order to validate this hypothesis, we introduce strategies to create dataset with pseudo-gold standard that is labeled automatically based on different types of distributional footprints. A range of experiments confirm the hypothesized connection between the distributional anomaly and deceptive reviews. This study also provides novel quantitative insights into the characteristics of natural distributions of opinions in the TripAdvisor hotel review and the Amazon product review domains.
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Cites
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-citation
Cited by
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
Collective Opinion Spam Detection: Bridging Review Networks and Metadata
Rayana, S., & Akoglu, L. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783370 · Cited by 604
Detecting opinion spams and fake news using text classification
Ahmed, H., Traore, I., & Saad, S. (2018) · SECURITY AND PRIVACY · doi:10.1002/spy2.9 · Cited by 521
Survey of review spam detection using machine learning techniques
Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N., & Al Najada, H. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0029-9 · Cited by 502
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
Metadata
- Authors
- Feng, S., Xing, L., Gogar, A., & Choi, Y.
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-08-03
- Volume
- 6
- Issue
- 1
- Pages
- 98-105
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2202307757
- Cited by
- 216
- References
- 2
- FWCI
- 7.44
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Feng, S., Xing, L., Gogar, A., & Choi, Y. (2021). Distributional Footprints of Deceptive Product Reviews. Proceedings of the International AAAI Conference on Web and Social Media. https://doi.org/10.1609/icwsm.v6i1.14275
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