The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Text mining and probabilistic language modeling for online review spam detection
Abstract
In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.
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Cites
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Self-citation
Cited by
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
Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics
Saura, J. R. (2021) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2020.08.001 · Cited by 473
Fake online reviews: Literature review, synthesis, and directions for future research
Wu, Y., Ngai, E. W. T., Wu, P., & Wu, C. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113280 · Cited by 359
Credibility in social media: opinions, news, and health information—a survey
Viviani, M., & Pasi, G. (2017) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1209 · Cited by 221
Metadata
- Authors
- Lau, R. Y. K., Liao, S. Y., Kwok, R. C.-W., Xu, K., Xia, Y., & Li, Y.
- Venue
- ACM Transactions on Management Information Systems
- Published
- 2011-12
- Volume
- 2
- Issue
- 4
- Pages
- 1-30
- ISSN
- 2158-656X (ISSN-L)
- 2158-6578
- OpenAlex
- W2168069066
- Cited by
- 224
- References
- 9
- FWCI
- 21.27
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lau, R. Y. K., Liao, S. Y., Kwok, R. C.-W., Xu, K., Xia, Y., & Li, Y. (2011). Text mining and probabilistic language modeling for online review spam detection. ACM Transactions on Management Information Systems. https://doi.org/10.1145/2070710.2070716
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