Bimodal Distribution and Co-Bursting in Review Spam Detection
Open accessAbstract
Online reviews play a crucial role in helping consumers evaluate and compare products and services. This critical importance of reviews also incentivizes fraudsters (or spammers) to write fake or spam reviews to secretly promote or demote some target products and services. Existing approaches to detecting spam reviews and reviewers employed review contents, reviewer behaviors, star rating patterns, and reviewer-product networks for detection. In this research, we further discovered that reviewers' posting rates (number of reviews written in a period of time) also follow an interesting distribution pattern, which has not been reported before. That is, their posting rates are bimodal. Multiple spammers also tend to collectively and actively post reviews to the same set of products within a short time frame, which we call co-bursting. Furthermore, we found some other interesting patterns in individual reviewers' temporal dynamics and their co-bursting behaviors with other reviewers. Inspired by these findings, we first propose a two-mode Labeled Hidden Markov Model to model spamming using only individual reviewers' review posting times. We then extend it to the Coupled Hidden Markov Model to capture both reviewer posting behaviors and co-bursting signals. Our experiments show that the proposed model significantly outperforms state-of-the-art baselines in identifying individual spammers. Furthermore, we propose a co-bursting network based on co-bursting relations, which helps detect groups of spammers more effectively than existing approaches.
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Identifying organic ASO activities is an open problem due to the ability of such workers to evade existing detection solutions.
“Identifying organic ASO activities is an open problem due to the ability of such workers to evade existing detection solutions, e.g., that leverage lockstep behaviors [32, 43, 51, 72, 73, 78, 85, 86, 89, 92, 93] or review bursts [27, 28, 35, 39, 40, 42, 44, 45, 50-53, 53, 5759, 65, 66, 84, 87, 88, 92].”
From RacketStore (2021) · cites this paper as [51]
The paper found rapid review bursts in both qualitative and quantitative investigations, consistent with assumptions in prior fraud-detection work, and interview participants revealed adaptive rate control as an avoidance technique.
“We found ASO workers who post fake reviews in rapid bursts in both our qualitative and quantitative investigations.This is consistent with assumptions made in previous fraud detection work, e.g., [26, 28, 32, 36-38, 40, 42, 43, 51, 52, 59-61, 63, 65, 92, 95, 96, 100].”
From The Art and Craft of Fraudulent App Promotion in Google Play (2019) · cites this paper as [32]
Earlier academic work created gold-standard fraud datasets using rule-based heuristics that assume fraudsters post reviews within short intervals, from one IP address, or with skewed rating distributions.
“academic work has built gold standard fraud datasets using rule-based heuristics, assuming that e.g., fraudsters post reviews in a short period of time [32,33,40,44,87], from the same IP address [40], or have a skewed rating distribution [58,87].”
From Fraud De-Anonymization for Fun and Profit (2018) · cites this paper as [33]
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743 · see citing works
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works
Forney, G. D. (1973) · Proceedings of the IEEE · doi:10.1109/proc.1973.9030 · Cited by 5,713 · see citing works
A Comparison of Document Clustering Techniques
Steinbach, M. S., Karypis, G., & Kumar, V. (2000) · Cited by 2,532 · see citing works
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 · see citing works
Self-citationBaum, L. E., & Eagon, J. A. (1967) · Bulletin of the American Mathematical Society · doi:10.1090/s0002-9904-1967-11751-8 · Cited by 1,053 · see citing works
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 · see citing works
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Amigó, E., Gonzalo, J. A., Artiles, J., et al. (2008) · Information Retrieval · doi:10.1007/s10791-008-9066-8 · Cited by 849 · see citing works
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 · see citing works
Self-citationDetecting product review spammers using rating behaviors
Lim, E., Nguyen, V.-A., Jindal, N., Liu, B., & Lauw, H. W. (2010) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1871437.1871557 · Cited by 720 · see citing works
Self-citationProceedings of the National Conference on Artificial Intelligence
Jensen, S., Boley, D. L., Gini, M. L., & Schrater, P. (2005) · Innovative Applications of Artificial Intelligence · Cited by 612 · see citing works
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 · see citing works
What Yelp Fake Review Filter Might Be Doing?
Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14389 · Cited by 564 · see citing works
Self-citationReview spam detection via temporal pattern discovery
Xie, S., Wang, G., Lin, S., & Yu, P. S. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339662 · Cited by 398 · see citing works
Opinion Fraud Detection in Online Reviews by Network Effects
Akoglu, L., Chandy, R., & Faloutsos, C. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14380 · Cited by 398 · see citing works
Learning to identify review spam
Li, F., Huang, M., Yang, Y., & Zhu, X. (2011) · doi:10.5591/978-1-57735-516-8/ijcai11-414 · Cited by 391 · see citing works
Review Graph Based Online Store Review Spammer Detection
Wang, G., Xie, S., Liu, B., & Yu, P. S. (2011) · Proceedings · doi:10.1109/icdm.2011.124 · Cited by 390 · see citing works
Self-citationTowards a General Rule for Identifying Deceptive Opinion Spam
Li, J., Ott, M., Cardie, C., & Hovy, E. H. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1147 · Cited by 375 · see citing works
Exploiting Burstiness in Reviews for Review Spammer Detection
Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., & Ghosh, R. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14400 · Cited by 352 · see citing works
Self-citationMihalcea, R. F., & Strapparava, C. (2009) · doi:10.3115/1667583.1667679 · Cited by 341 · see citing works
CopyCatch: stopping group attacks by spotting lockstep behavior in social networks
Beutel, A., Xu, W., Guruswami, V., Palow, C., & Faloutsos, C. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488400 · Cited by 335 · see citing works
Castillo, C., Donato, D., Gionis, A., et al. (2007) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1277741.1277814 · Cited by 314 · see citing works
Text mining and probabilistic language modeling for online review spam detection
Lau, R. Y. K., Liao, S. S., Kwok, R. C.-W., Xu, K. S. J., Xia, Y., & Li, Y. (2011) · ACM Transactions on Management Information Systems · doi:10.1145/2070710.2070716 · Cited by 224 · see citing works
Distributional Footprints of Deceptive Product Reviews
Feng, S., Xing, L., Gogar, A., & Choi, Y. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14275 · Cited by 217 · see citing works
Spotting Fake Reviews via Collective Positive-Unlabeled Learning
Li, H., Chen, Z., Liu, B., Wei, X., & Shao, J. (2014) · Proceedings · doi:10.1109/icdm.2014.47 · Cited by 168 · see citing works
Self-citationUncovering collusive spammers in Chinese review websites
Xu, C., Zhang, J., Chang, K., & Long, C. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505700 · Cited by 159 · see citing works
Chirita, P., Diederich, J., & Nejdl, W. (2005) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1099554.1099671 · Cited by 156 · see citing works
Detecting Spam Blogs: A Machine Learning Approach
Kolari, P., Java, A., Finin, T., Oates, T., & Joshi, A. (2006) · Maryland Shared Open Access Repository (USMAI Consortium) · doi:10.13016/m27m0444d · Cited by 127 · see citing works
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
Hooi, B., Shah, N., Beutel, A., Günnemann, S., Akoglu, L., Kumar, M., Makhija, D., & Faloutsos, C. (2016) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611974348.56 · Cited by 118 · see citing works
Combating spam in tagging systems
Koutrika, G., Effendi, F. A., Gyöngyi, Z., et al. (2007) · doi:10.1145/1244408.1244420 · Cited by 116 · see citing works
Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns
Li, H., Chen, Z., Mukherjee, A., Liu, B., & Shao, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14652 · Cited by 116 · see citing works
Self-citationA Statistical Language Modeling Approach to Online Deception Detection
Zhou, L., Shi, Y., & Zhang, D. (2008) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.190624 · Cited by 98 · see citing works
Positive Unlabeled Learning for Deceptive Reviews Detection
Ren, Y., Ji, D., & Zhang, H. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1055 · Cited by 96 · see citing works
Jin, X. Y., Lin, C. X., Luo, J., & Han, J. (2011) · Proceedings of the VLDB Endowment · doi:10.14778/3402755.3402795 · Cited by 94 · see citing works
Discovering Opinion Spammer Groups by Network Footprints
Ye, J., & Akoglu, L. (2015) · Proceedings of the 2015 ACM on Conference on Online Social Networks · doi:10.1145/2817946.2820606 · Cited by 84 · see citing works
Spotting Fake Reviews using Positive-Unlabeled Learning
Li, H., Liu, B., Mukherjee, A., & Shao, J. (2014) · Computación y Sistemas · doi:10.13053/cys-18-3-2035 · Cited by 82 · see citing works
Self-citationCharacterizing individual communication patterns
Malmgren, R. D., Hofman, J. M., Amaral, L. A. N., et al. (2009) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557088 · Cited by 77 · see citing works
Towards Collusive Fraud Detection in Online Reviews
Xu, C., & Zhang, J. (2015) · Proceedings · doi:10.1109/icdm.2015.62 · Cited by 75 · see citing works
On the Temporal Dynamics of Opinion Spamming
Santosh, K. C., & Mukherjee, A. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883087 · Cited by 62 · see citing works
Self-citationDetecting Campaign Promoters on Twitter Using Markov Random Fields
Li, H., Mukherjee, A., Liu, B., Kornfield, R., & Emery, S. (2014) · Proceedings · doi:10.1109/icdm.2014.59 · Cited by 61 · see citing works
Self-citationRobust multivariate autoregression for anomaly detection in dynamic product ratings
Günnemann, N., Günnemann, S., & Faloutsos, C. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2566486.2568008 · Cited by 61 · see citing works
Temporal Opinion Spam Detection by Multivariate Indicative Signals
Ye, J., Kumar, S., & Akoglu, L. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14801 · Cited by 51 · see citing works
Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution
Günnemann, S., Günnemann, N., & Faloutsos, C. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623721 · Cited by 47 · see citing works
Detecting Deceptive Groups Using Conversations and Network Analysis
Yu, D., Tyshchuk, Y., Ji, H., & Wallace, W. A. (2015) · doi:10.3115/v1/p15-1083 · Cited by 15 · see citing works
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Zheng, H., Xue, M., Lu, H., Hao, S., Zhu, H., Liang, X., & Ross, K. (2018) · Proceedings 2018 Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23009 · Cited by 61 · see citing works
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“Our work is particularly relevant in light of findings that some ASO workers have evolved strategies [67, 94] to evade detection by both app stores and academic solutions, e.g., [27, 28, 32, 32, 35, 39, 40, 42, 44, 45, 48, 50, 51, 51–53, 53, 57–59, 72, 73, 78, 84– 89, 92, 92, 93].”
Background
- Authors
- Li, H., Fei, G., Wang, S., Liu, B., Shao, W., Mukherjee, A., & Shao, J.
- Affiliations
- University of Illinois Chicago (US)
- University of Houston (US)
- Dianping Inc., Shanghai, China
- Venue
- Proceedings of the 26th International Conference on World Wide Web
- Published
- 2017-04-03
- Pages
- 1063-1072
- OpenAlex
- W2604992511
- Cited by
- 141
- References
- 44
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 9 · 2023: 18 · 2022: 13 · 2021: 24 · 2020: 25 · 2019: 19 · 2018: 17 · 2017: 4
- FWCI
- 13.65
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- Funders
- National Science Foundation (NSF) (IIS-1650900, IIS-1407927, CNS-1527364)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- spam review detection, spammer detection, coupled hidden Markov models, online reviews
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