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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Dissecting Genuine and Deceptive Kudos: The Case of Online Hotel Reviews
Banerjee, S., & Chua, A. Y. K. (2014) · International Journal of Advanced Computer Science and Applications · doi:10.14569/specialissue.2014.040304 · Cited by 5
Informationssysteme für „Wicked Problems“
Schoder, D., Putzke, J., Metaxas, P., Gloor, P. A., & Fischbach, K. (2014) · WIRTSCHAFTSINFORMATIK · doi:10.1007/s11576-013-0395-x · Cited by 5
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Ding, Z., Gong, Y., Zhou, Y., Zhang, Q., & Huang, J. X. (2013) · Cited by 5
Kawamae, N. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2882981 · Cited by 5
Dewang, R. K., Singh, P. K., & Singh, A. K. (2016) · International Conference on Information and Communication Technology for Competitive Strategies (ICTCS) · doi:10.1145/2905055.2905081 · Cited by 5
A Real Case Analytics on Social Network of Opinion Spammers
Wang, C., Day, M.-Y., & Lin, Y. (2016) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2016.89 · Cited by 5
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Anchiêta, R. T., de Sousa, R. F., Moura, R. S., & Pardo, T. A. S. (2017) · Cited by 5
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Khalifa, M. B., Elouedi, Z., & Lefèvre, É. (2019) · doi:10.33965/ac2019_201912l015 · Cited by 5
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Li, S.-H., & Cheng, W. (2022) · Neural computing & applications (Print) · doi:10.1007/s00521-022-07492-y · Cited by 5
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Li, X., & Yan, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39937-9_8 · Cited by 5
de-la-Peña-Sordo, J., Pastor-López, I., Ugarte-Pedrero, X., Santos, I., & Bringas, P. G. (2016) · Logic Journal of IGPL · doi:10.1093/jigpal/jzw043 · Cited by 5
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de-la-Peña-Sordo, J., Pastor-López, I., Ugarte-Pedrero, X., Santos, I., & Bringas, P. G. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-07995-0_51 · Cited by 5
Identifying Deceptive Review Comments with Rumor and Lie Theories
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Identifying Deceptive Opinion Spam using Aspect-based Emotions and Human Behavior Modeling
Saini, M., & Sharan, A. (2017) · International Journal of Hybrid Information Technology · doi:10.14257/ijhit.2017.10.1.36 · Cited by 6
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Prabakaran, A., & Chen, M. (2019) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2019.8685490 · Cited by 5
dos Santos, A. S., Camargo, L. F. R., & Lacerda, D. P. (2020) · Gestão & Produção · doi:10.1590/0104-530x4672-20 · Cited by 7
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Tseng, Y.-D., & Chen, C. C. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04769-5_9 · Cited by 5
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Estimating global opinions by keeping users from fraud in online review systems
Centeno, R., & Hermoso, R. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1089-2 · Cited by 5
Opinion mining of online customer reviews
Gowtamreddy, P. (2014) · Cited by 5
Study of authentic and fictitious online reviews
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Resource Burning for Permissionless SystemsPreprint
Gupta, D., Saia, J., & Young, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04865 · Cited by 5
The Development of Sentiment Analysis from a linguistic perspective
Assem, S. A. (2022) · The Egyptian Journal of Language Engineering /The Egyptian Journal of Language Engineering · doi:10.21608/ejle.2022.159650.1034 · Cited by 4
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YouTube comment sentiment analysis using NLP approach
Visariya, D., Prajapati, P., & Bhatt, N. (2025) · AIP conference proceedings · doi:10.1063/5.0254716 · Cited by 4
Measures of Abnormal User Activities in Online Comments Based on Cosine Similarity
Kim, M. J., & Lee, S. (2014) · 정보보호학회논문지 · doi:10.13089/jkiisc.2014.24.2.335 · Cited by 4
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Yang, M., Lu, Z., Chen, X., & Xu, F. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11063 · Cited by 4
Research on fake reviews detection based on graph neural network
Ren, X., Yuan, Z., & Huang, J. (2022) · International Symposium on Computer Applications and Information Systems (ISCAIS) · doi:10.1117/12.2639534 · Cited by 5
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Obot, O. U., Attai, K. F., Onwodi, G. O., James, I., & John, A. E. (2024) · Advances in information quality and management · doi:10.4018/978-1-6684-7366-5.ch057 · Cited by 4
Signed Latent Factors for Spamming Activity Detection
Liu, Y. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3516573 · Cited by 6
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Santosh, K. C., Sarkar, S. D., & Mukherjee, A. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508291 · Cited by 4
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Self-citationResearch of integrated algorithm establishment of a spam detection system
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Wang, Z., Sun, H., & Fan, B. (2020) · International Journal of Crowd Science · doi:10.1108/ijcs-02-2020-0003 · Cited by 4
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Ahire, K., Bagul, M., Dhanawate, S., & Panicker, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-33-6546-9_31 · Cited by 4
Caione, A., Guido, A. L., Paiano, R., Pandurino, A., & Pasanisi, S. (2016) · ICST Transactions on e-Education and e-Learning · doi:10.4108/eai.10-3-2016.151121 · Cited by 4
Sentiment-Preserving Reduction for Social Media Analysis
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Duan, H., & Zirn, C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34210-3_15 · Cited by 4
Cross-Media Sentiment Classification and Application to Box-Office Forecasting
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Tetmeyer, A., Hein, D., & Saiedian, H. (2014) · International Journal of Secure Software Engineering · doi:10.4018/ijsse.2014100102 · Cited by 4
Deception detection in human reasoning
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Mittal, M., Wu, W., Rubin, S., Madden, S. R., & Hartmann, B. (2012) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2141512.2141570 · Cited by 4
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Review spamicity based on rank and content of the review
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A multi source product reputation model
Farooq, U., Nongaillard, A., Ouzrout, Y., & Qadir, M. A. (2016) · Computers in Industry · doi:10.1016/j.compind.2016.08.002 · Cited by 4
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Noekhah, S., Salim, N. B., & Zakaria, N. H. (2017) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-59427-9_9 · Cited by 4
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Li, P., & Konig, C. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1108.0895 · Cited by 4
CoRE KernelsPreprint
Li, P. (2014) · arXiv (Cornell University) · Cited by 4
WITHDRAWN: Deceptive reviewer detection by analyzing web data using HMM and similarity measures
Mewada, A., & Dewang, R. K. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.12.1126 · Cited by 4
RetractedLe, C., Zheng, R., Lu, T., & Chen, Y. (2024) · Entropy · doi:10.3390/e26121005 · Cited by 3
Empowering Trust in Digital Platforms: Sentiment Analysis and Emotion AI for Fake Reviews Detection
Khan, C., Adnan, A., & Anwar, S. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3638615 · Cited by 3
연종흠, 심준호, & 이상구 (2013) · Han-guk jeonja georae hakoeji · Cited by 3
Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach
Melleng, A., Loughrey, A.-J., & Deepak, P. (2021) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-072-4_109 · Cited by 3
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Cao, H., Li, H., He, Y., Xu, Y., Yang, F., & Wang, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15931-2_41 · Cited by 3
Kumar, K., George, K. S., Bhatt, D., & Paul, O. P. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.52743 · Cited by 4
Data Fusion for Better Fake Reviews Detection
Melleng, A., Loughrey, A.-J., & Deepak, P. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_079 · Cited by 3
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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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