The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Opinion Fraud Detection in Online Reviews by Network Effects
Open accessAbstract
User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However,review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets. Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike the vast majority of existing methods that focus on review text or behavioral analysis, (2) it consists of two complementary steps; scoring users and reviews for fraud detection, and grouping for visualization and sensemaking, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available, and (4) it is scalable to large datasets as its run time grows linearly with network size. We demonstrate the effectiveness of our framework on syntheticand real datasets; where FRAUDEAGLE successfully reveals fraud-bots in a large online app review database.
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Thumbs up? Sentiment Classification using Machine Learning Techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0205070 · Cited by 2,191
Authoritative sources in a hyperlinked environment
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Markov Random Fields and Their Applications
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Finding Deceptive Opinion Spam by Any Stretch of the Imagination
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Twitter under crisis: can we trust what we RT?
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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
Self-citationCollective 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
Self-citationLai, V., & Tan, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287590 · Cited by 377
Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.04.013 · Cited by 368
Self-citationFRAUDAR: Bounding Graph Fraud in the Face of Camouflage
Hooi, B., Song, H. A., Beutel, A., Shah, N., Shin, K., & Faloutsos, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939747 · Cited by 305
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Metadata
- Authors
- Akoglu, L., Chandy, R., & Faloutsos, C.
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-08-03
- Volume
- 7
- Issue
- 1
- Pages
- 2-11
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2282288858
- Cited by
- 391
- References
- 26
- FWCI
- 20.34
- Type
- conference paper
- Language
- en
- Open access
- diamond
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