The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Opinion spam and analysis
Abstract
Evaluative texts on the Web have become a valuable source of opinions on products, services, events, individuals, etc. Recently, many researchers have studied such opinion sources as product reviews, forum posts, and blogs. However, existing research has been focused on classification and summarization of opinions using natural language processing and data mining techniques. An important issue that has been neglected so far is opinion spam or trustworthiness of online opinions. In this paper, we study this issue in the context of product reviews, which are opinion rich and are widely used by consumers and product manufacturers. In the past two years, several startup companies also appeared which aggregate opinions from product reviews. It is thus high time to study spam in reviews. To the best of our knowledge, there is still no published study on this topic, although Web spam and email spam have been investigated extensively. We will see that opinion spam is quite different from Web spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that opinion spam in reviews is widespread. This paper analyzes such spam activities and presents some novel techniques to detect them
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“Disinformation can take forms beyond the production of hoaxes or “fake news” (Lazer et al., 2018); and include other types of manipulation of social systems, such as Google bombs to associate a keyword to a URL by repeatedly searching for the keyword and clicking on the URL, or review spam (Jindal and Liu, 2008) to manipulate the reputation of a product/service.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Jindal and Liu, 2008
Citations
Cites
Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793
Self-citationThumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
Peter, P., & Turney (2002) · Cited by 3,646
Turney, P. D. (2001) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1073083.1073153 · Cited by 2,354
Dave, K. S., Lawrence, S., & Pennock, D. M. (2003) · International World Wide Web Conference (WWW) · doi:10.1145/775152.775226 · Cited by 1,925
On the resemblance and containment of documents
Broder, A. (2002) · Compression and Complexity of Sequences (SEQUENCES) · doi:10.1109/sequen.1997.666900 · Cited by 1,818
Extracting product features and opinions from reviews
Popescu, A.-M., & Etzioni, O. (2005) · doi:10.3115/1220575.1220618 · Cited by 1,714
A Bayesian Approach to Filtering Junk E-Mail
Sahami, M., Dumais, S., Heckerman, D. E., & Horvitz, E. (1998) · Cited by 1,212
Web data mining: exploring hyperlinks, contents, and usage data
(2012) · Choice Reviews Online · doi:10.5860/choice.49-2718 · Cited by 1,181
Mining the Web: Discovering Knowledge from Hypertext Data
Chakrabarti, S. (2002) · Thammasat University Digital Collections · Cited by 696
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Items 201–300 of 1,002
GSLDA: LDA-based group spamming detection in product reviews
Wang, Z., Gu, S., & Xu, X. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1142-1 · Cited by 76
Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training
Wang, J., Kan, H., Meng, F., Mu, Q., Shi, G., & Xiao, X. (2020) · IEEE Access · doi:10.1109/access.2020.3028588 · Cited by 78
Towards Collusive Fraud Detection in Online Reviews
Xu, C., & Zhang, J. (2015) · Proceedings · doi:10.1109/icdm.2015.62 · Cited by 77
Adapting social spam infrastructure for political censorship
Thomas, K., Grier, C., & Paxson, V. (2012) · Cited by 73
Personalised rating prediction for new users using latent factor models
Seroussi, Y., Fabian Bohnert, A., & Ingrid Zukerman, A. (2011) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/1995966.1995976 · Cited by 75
Opinion Mining and Sentiment Analysis
Aggarwal, C. C. (2018) · Springer eBooks · doi:10.1007/978-3-319-73531-3_13 · Cited by 75
The App Sampling Problem for App Store Mining
Martin, W., Harman, M., Jia, Y., Sarro, F., & Zhang, Y. (2015) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2015.19 · Cited by 74
Kaur, R., & Kautish, S. (2019) · International Journal of Service Science Management Engineering and Technology · doi:10.4018/ijssmet.2019040103 · Cited by 73
Liu, Y., Pang, B., & Wang, X. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.013 · Cited by 73
How People Evaluate Online Reviews
DeAndrea, D. C., Heide, B. L. V. D., Vendemia, M. A., & Vang, M. H. (2015) · Communication Research · doi:10.1177/0093650215573862 · Cited by 73
TopicSpam: a Topic-Model-Based Approach for Spam Detection
Li, J., Cardie, C., & Li, S. (2013) · Cited by 74
In Defense of MinHash Over SimHashPreprint
Shrivastava, A., & Li, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.4416 · Cited by 73
Toward a Language Modeling Approach for Consumer Review Spam Detection
Lai, C. L., Xu, K., Lau, R. Y. K., Li, Y., & Jing, L. (2010) · IEEE International Conference on e-Business Engineering (ICEBE) · doi:10.1109/icebe.2010.47 · Cited by 72
Unveiling the cloak of deviance: Linguistic cues for psychological processes in fake online reviews
Li, L., Lee, K. Y., Lee, M., & Yang, S. (2020) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2020.102468 · Cited by 74
Detecting Spam Review through Sentiment Analysis
Peng, Q., & Zhong, M. (2014) · Journal of Software · doi:10.4304/jsw.9.8.2065-2072 · Cited by 74
Opinion Spam Detection in Web Forum
Chen, Y., & Chen, H. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2736277.2741085 · Cited by 71
Learning to Detect Deceptive Opinion Spam: A Survey
Ren, Y., & Ji, D. (2019) · IEEE Access · doi:10.1109/access.2019.2908495 · Cited by 75
Synthetic review spamming and defense
Sun, H., Morales, A., & Yan, X. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487688 · Cited by 71
Spam review detection using self attention based CNN and bi-directional LSTM
Bhuvaneshwari, P., Rao, A. N., & Robinson, H. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10602-y · Cited by 71
Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM
Liu, Y., Wang, L., Shi, T., & Li, J. (2021) · Information Systems · doi:10.1016/j.is.2021.101865 · Cited by 71
Mining opinion from text documents: A survey
Khan, K., Baharudin, B., Khan, A., & Fazal-e-Malik (2009) · IEEE International Conference on Digital Ecosystems and Technologies · doi:10.1109/dest.2009.5276756 · Cited by 71
Selecting a characteristic set of reviews
Lappas, T., Crovella, M. E., & Terzi, E. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339663 · Cited by 70
Identifying Multiple Userids of the Same Author
Qian, T., & Liu, B. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1113 · Cited by 70
Self-citationBen Jabeur, S., Ballouk, H., Ben Arfi, W., & Sahut, J.-M. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113631 · Cited by 70
Opinion spam detection framework using hybrid classification scheme
Asghar, M. Z., Ullah, A., Ahmad, S., & Khan, A. (2019) · Soft Computing · doi:10.1007/s00500-019-04107-y · Cited by 73
Discovery of Ranking Fraud for Mobile Apps
Zhu, H., Xiong, H., Ge, Y., & Chen, E. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2320733 · Cited by 71
Fake Review Detection From a Product Review Using Modified Method of Iterative Computation Framework
Wahyuni, E. D., & Djunaidy, A. (2016) · MATEC Web of Conferences · doi:10.1051/matecconf/20165803003 · Cited by 69
Kumar, A., Gopal, R. D., Shankar, R., & Tan, K. H. (2022) · Decision Support Systems · doi:10.1016/j.dss.2021.113728 · Cited by 72
An empirical study on detecting fake reviews using machine learning techniques
Elmurngi, E. I., & Gherbi, A. (2017) · International Conference on Innovative Computing Technology · doi:10.1109/intech.2017.8102442 · Cited by 68
Elmurngi, E. I., & Gherbi, A. (2018) · Journal of Computer Science · doi:10.3844/jcssp.2018.714.726 · Cited by 67
Assessing the Unacquainted: Inferred Reviewer Personality and Review Helpfulness
Liu, A. X., Li, Y., & Xu, S. X. (2021) · MIS Quarterly · doi:10.25300/misq/2021/14375 · Cited by 70
State-of-art approaches for review spammer detection: a survey
Dewang, R. K., & Singh, A. K. (2017) · Journal of Intelligent Information Systems · doi:10.1007/s10844-017-0454-7 · Cited by 68
Detecting Spammer Groups From Product Reviews: A Partially Supervised Learning Model
Zhang, L., Wu, Z., & Cao, J. (2017) · IEEE Access · doi:10.1109/access.2017.2784370 · Cited by 66
Feature Analysis for Fake Review Detection through Supervised Classification
Fontanarava, J., Pasi, G., & Viviani, M. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.51 · Cited by 71
Opinion fraud detection via neural autoencoder decision forest
Dong, M., Yao, L., Wang, X., Benatallah, B., Huang, C., & Ning, X. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.07.013 · Cited by 72
Towards online anti-opinion spam: Spotting fake reviews from the review sequence
Lin, Y., Zhu, T., Wu, H., Zhang, J., Wang, X., & Zhou, A. (2014) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2014.6921594 · Cited by 70
A burst-based unsupervised method for detecting review spammer groups
Ji, S., Zhang, Q., Li, J., Chiu, D. K. W., Xu, S., Lei, Y., & Gong, M. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.084 · Cited by 66
Identifying helpful reviews based on customer’s mentions about experiences
Min, H.-J., & Park, J. C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.116 · Cited by 66
Preference-based clustering reviews for augmenting e-commerce recommendation
Chen, L., & Wang, F. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.05.006 · Cited by 66
An Attention-Based Unsupervised Adversarial Model for Movie Review Spam Detection
Gao, Y., Gong, M., Xie, Y., & Qin, A. K. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2990085 · Cited by 66
Identifying spam in the iOS app store
Chandy, R., & Gu, H. (2012) · doi:10.1145/2184305.2184317 · Cited by 64
Phrase-based extraction of user opinions in mobile app reviews
Vu, P. M., Pham, H. V., Nguyen, T. T., & Nguyen, T. T. (2016) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/2970276.2970365 · Cited by 64
Analysis of concept drift in fake reviews detection
Mohawesh, R., Tran, S. N., Ollington, R., & Xu, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114318 · Cited by 67
Use of Social Media Data to Explore Crisis Informatics
Ukkusuri, S. V., Zhan, X., Sadri, A. M., & Ye, Q. (2014) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2459-13 · Cited by 64
Revealing the unrevealed: Mining smartphone users privacy perception on app markets
Hatamian, M., Serna, J., & Rannenberg, K. (2019) · Computers & Security · doi:10.1016/j.cose.2019.02.010 · Cited by 64
Personalized Review Recommendation based on Users’ Aspect Sentiment
ChunLi, H., Jiang, W., Wu, J., & Wang, G. (2020) · ACM Transactions on Internet Technology · doi:10.1145/3414841 · Cited by 63
Detecting Fake Reviews Utilizing Semantic and Emotion Model
Li, Y., Feng, X., & Zhang, S. (2016) · International Conference on Information Science and Control Engineering · doi:10.1109/icisce.2016.77 · Cited by 63
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 64
A unified framework for detecting author spamicity by modeling review deviation
Liu, Y., & Pang, B. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.06.028 · Cited by 65
Deceptive consumer review detection: a survey
Vidanagama, D. U., Silva, T., & Karunananda, A. S. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09697-5 · Cited by 71
A Survey on Fake Review Detection using Machine Learning Techniques
Patel, N. A., & Patel, R. (2018) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2018.8777594 · Cited by 66
Detecting 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
Self-citationLinguistic characteristics of shill reviews
Ong, T. C., Mannino, M. V., & Gregg, D. G. (2013) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2013.10.002 · Cited by 61
An Approach for Detecting Spam in Arabic Opinion Reviews
Hammad, A. A., & El-Halees, A. M. (2015) · The International Arab Journal of Information Technology · Cited by 61
Detection of spam reviews: a sentiment analysis approach
Saumya, S., & Singh, J. P. (2018) · CSI Transactions on ICT · doi:10.1007/s40012-018-0193-0 · Cited by 65
Artificial Intelligence for Social Good: A SurveyPreprint
Shi, Z. R., Wang, C., & Fang, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.01818 · Cited by 131
Multiview Semi-Supervised Learning with Consensus
Li, G., Chang, K., & Hoi, S. C. H. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.160 · Cited by 60
Conceptual level similarity measure based review spam detection
Algur, S. P., Patil, A. P., Hiremath, P. S., & Shivashankar, S. (2010) · doi:10.1109/icsip.2010.5697509 · Cited by 60
Adversarial Detection with Model Interpretation
Liu, N., Yang, H., & Hu, X. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220027 · Cited by 61
Detecting Opinion Spammer Groups Through Community Discovery and Sentiment Analysis
Choo, E., Yu, T., & Chi, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20810-7_11 · Cited by 60
Detection of Opinion Spam with Character n-grams
Fusilier, D. H., Montes-y-Gómez, M., Rosso, P., & Guzmán-Cabrera, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18117-2_21 · Cited by 60
Building Fake Review Detection Model Based on Sentiment Intensity and PU Learning
Zhang, S., Zhu, A., Guangli, Z., Wei, Z., & Li, K. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3234427 · Cited by 59
Fake Reviews: The Malicious Perspective
Lappas, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31178-9_3 · Cited by 61
Reflections on Sentiment/Opinion Analysis
Li, J., & Hovy, E. H. (2017) · Socio-affective computing · doi:10.1007/978-3-319-55394-8_3 · Cited by 59
Explicit aspects extraction in sentiment analysis using optimal rules combination
Tubishat, M., Idris, N. B., & Abushariah, M. A. M. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.08.019 · Cited by 61
Lexical and Hierarchical Topic Regression
Nguyen, V.-A., Boyd-Graber, J. L., & Resnik, P. (2013) · Cited by 58
Alsubari, S. N., Deshmukh, S. N., Al-Adhaileh, M. H., Alsaade, F. W., & Aldhyani, T. H. H. (2021) · Applied Bionics and Biomechanics · doi:10.1155/2021/5522574 · Cited by 60
RetractedSimultaneously detecting fake reviews and review spammers using factor graph model
Lu, Y., Zhang, L., Xiao, Y., & Li, Y. (2013) · ACM Web Science Conference (WebSci) · doi:10.1145/2464464.2464470 · Cited by 60
Opinion-Driven Matrix Factorization for Rating Prediction
Pero, Š., & Horváth, T. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38844-6_1 · Cited by 57
Sentiment extraction from natural audio streams
Kaushik, L., Sangwan, A., & Hansen, J. H. L. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639321 · Cited by 57
Evaluating user reputation in online rating systems via an iterative group-based ranking method
Gao, J. G., & Zhou, T. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.01.055 · Cited by 58
Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data
Hai, Z., Zhao, P., Cheng, P., Yang, P., Li, X., & Li, G. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1187 · Cited by 59
DynaSent: A Dynamic Benchmark for Sentiment Analysis
Potts, C., Wu, Z., Geiger, A., & Kiela, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.186 · Cited by 56
EdgeCentric: Anomaly Detection in Edge-Attributed Networks
Shah, N., Beutel, A., Hooi, B., Akoglu, L., Günnemann, S., Makhija, D., Kumar, M., & Faloutsos, C. (2016) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2016.0053 · Cited by 62
A Novel Stream Clustering Framework for Spam Detection in Twitter
Tajalizadeh, H., & Boostani, R. (2019) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2019.2910818 · Cited by 57
Sobkowicz, P., & Sobkowicz, A. (2012) · Social Science Computer Review · doi:10.1177/0894439312436512 · Cited by 51
A review of feature selection techniques in sentiment analysis
Ahmad, S. R., Bakar, A. A., & Yaakub, M. R. (2019) · Intelligent Data Analysis · doi:10.3233/ida-173763 · Cited by 54
Role of sentiment analysis in social media security and analytics
Sharma, S., & Jain, A. (2020) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1366 · Cited by 54
Survey on aspect detection for aspect-based sentiment analysis
Truşcǎ, M. M., & Frăsincar, F. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10252-y · Cited by 53
Review quality aware collaborative filtering
Raghavan, S., Gunasekar, S., & Ghosh, J. (2012) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2365952.2365978 · Cited by 53
Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN
Zhao, S., Xu, Z., Liu, L., Guo, M., & Yun, J. (2018) · Mathematical Problems in Engineering · doi:10.1155/2018/2410206 · Cited by 53
A Method for the Detection of Fake Reviews Based on Temporal Features of Reviews and Comments
Liu, W., He, J., Han, S., Cai, F., Yang, Z., & Zhu, N. (2019) · IEEE Engineering Management Review · doi:10.1109/emr.2019.2928964 · Cited by 55
Systematic analysis of centralized online reputation systems
Liu, L., & Munro, M. (2011) · Decision Support Systems · doi:10.1016/j.dss.2011.10.003 · Cited by 51
Applauses in hotel reviews: Genuine or deceptive?
Banerjee, S., & Chua, A. Y. K. (2014) · Science and Information Conference · doi:10.1109/sai.2014.6918299 · Cited by 52
Exploring performance of clustering methods on document sentiment analysis
Ma, B., Yuan, H., & Wu, Y. (2015) · Journal of Information Science · doi:10.1177/0165551515617374 · Cited by 52
Generating behavior features for cold-start spam review detection with adversarial learning
Tang, X., Qian, T., & You, Z. (2020) · Information Sciences · doi:10.1016/j.ins.2020.03.063 · Cited by 55
A non-convex semi-supervised approach to opinion spam detection by ramp-one class SVM
Tian, Y., Mirzabagheri, M., Tirandazi, P., & Bamakan, S. M. H. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102381 · Cited by 52
Learning to Represent Review with Tensor Decomposition for Spam Detection
Wang, X., Liu, K., He, S., & Zhao, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1083 · Cited by 53
Worth its Weight in Likes: Towards Detecting Fake Likes on Instagram
Sen, I., Aggarwal, A., Mian, S., Singh, S., Kumaraguru, P., & Datta, A. (2018) · Proceedings of the 10th ACM Conference on Web Science · doi:10.1145/3201064.3201105 · Cited by 52
A Survey on Assessment and Ranking Methodologies for User-Generated Content on the Web
Momeni, E., Cardie, C., & Diakopoulos, N. A. (2015) · ACM Computing Surveys · doi:10.1145/2811282 · Cited by 50
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
Mukherjee, S., Malu, A., Balamurali, A. R., & Bhattacharyya, P. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398684 · Cited by 49
Spam review detection using LSTM autoencoder: an unsupervised approach
Saumya, S., & Singh, J. P. (2020) · Electronic Commerce Research · doi:10.1007/s10660-020-09413-4 · Cited by 50
Budhi, G. S., Chiong, R., Wang, Z., & Dhakal, S. (2021) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2021.101048 · Cited by 52
Hsieh, Y. P., & Murphy, J. (2017) · doi:10.1002/9781119041702.ch2 · Cited by 49
Combating Product Review Spam Campaigns via Multiple Heterogeneous Pairwise Features
Xu, C., & Zhang, J. (2015) · Proceedings of the 2015 SIAM International Conference on Data Mining · doi:10.1137/1.9781611974010.20 · Cited by 51
Learning Review Representations from user and Product Level Information for Spam Detection
Yuan, C., Zhou, W. J., Ma, Q., Lv, S., Han, J., & Hu, S. (2019) · Proceedings · doi:10.1109/icdm.2019.00188 · Cited by 48
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Wang, J., Ghose, A., & Ipeirotis, P. G. (2012) · Cited by 49
Hong, Y., Lu, J., Yao, J., Zhu, Q., & Zhou, G. (2012) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2348283.2348351 · Cited by 48
A Review for Weighted MinHash Algorithms
Wu, W., Li, B., Chen, L., Gao, J., & Zhang, C. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3021067 · Cited by 51
Metadata
- Authors
- Jindal, N., & Liu, B.
- Affiliations
- University of Illinois Chicago (US)
- ORCID
- Liu, Bing
- Venue
- Proceedings of the international conference on Web search and web data mining - WSDM '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 219
- OpenAlex
- W2047756776
- Cited by
- 1,542
- References
- 29
- Citations by year
- 2026: 36 · 2025: 64 · 2024: 71 · 2023: 78 · 2022: 91 · 2021: 112 · 2020: 124 · 2019: 118 · 2018: 128 · 2017: 137 · 2016: 99 · 2015: 137 · 2014: 103 · 2013: 77 · 2012: 77
- Type
- conference abstract
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- 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
- opinion spam, spam detection, product reviews, online reviews, review aggregation, Amazon reviews
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