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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Cited by
Sorted by citations
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957
Sentiment Analysis and Opinion Mining
Liu, B. (2012) · Synthesis lectures on human language technologies · doi:10.2200/s00416ed1v01y201204hlt016 · Cited by 3,995
Self-citationSentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2017) · Springer eBooks · doi:10.1007/978-1-4899-7687-1_907 · Cited by 3,307
Self-citationSentiment Analysis and Subjectivity
Bing, L. (2010) · Cited by 1,724
Hidden factors and hidden topics
McAuley, J., & Leskovec, J. (2013) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2507157.2507163 · Cited by 1,565
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
A Survey of Opinion Mining and Sentiment Analysis
Liu, B., & Zhang, L. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3223-4_13 · Cited by 1,356
Self-citationA survey on opinion mining and sentiment analysis: Tasks, approaches and applications
Ravi, K., & Ravi, V. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.06.015 · Cited by 1,376
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Zhou, X., & Zafarani, R. (2021) · ACM Computing Surveys · doi:10.1145/3395046 · Cited by 1,184
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
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
Rashkin, H., Choi, E., Jang, J. Y., Volkova, S., & Choi, Y. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1317 · Cited by 965
Filieri, R., Alguezaui, S., & McLeay, F. (2015) · Tourism Management · doi:10.1016/j.tourman.2015.05.007 · Cited by 882
Survey on Aspect-Level Sentiment Analysis
Schouten, K., & Frăsincar, F. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2485209 · Cited by 856
Sentiment analysis using product review data
Fang, X., & Zhan, J. Z. (2015) · Journal Of Big Data · doi:10.1186/s40537-015-0015-2 · Cited by 857
Cross-domain sentiment classification via spectral feature alignment
Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., & Chen, Z. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772767 · Cited by 791
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
Self-citationSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
“…“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.”
BackgroundDetecting 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
Self-citationChen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154
Self-citationMagno, G., Rodrigues, T., & Almeida, V. A. F. (2010) · Cited by 649
Sentiment Analysis: Mining Opinions, Sentiments, and Emotions
Liu, B. (2015) · Cited by 624
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
What makes an online consumer review trustworthy?
Filieri, R. (2016) · Annals of Tourism Research · doi:10.1016/j.annals.2015.12.019 · Cited by 587
Salehan, M., & Kim, D. J. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.10.006 · Cited by 588
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 570
Self-citationDeception detection for news: Three types of fakes
Rubin, V. L., Chen, Y., & Conroy, N. K. (2015) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.2015.145052010083 · Cited by 558
A review of natural language processing techniques for opinion mining systems
Sun, S., Luo, C., & Chen, J. (2016) · Information Fusion · doi:10.1016/j.inffus.2016.10.004 · Cited by 576
Chen, Z., & Liu, B. (2018) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01581-6 · Cited by 551
Self-citationAR-miner: mining informative reviews for developers from mobile app marketplace
Chen, N., Lin, J., Hoi, S. C. H., Xiao, X., & Zhang, B. (2014) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2568225.2568263 · Cited by 541
McAuley, J., & Leskovec, J. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488466 · Cited by 527
Detecting opinion spams and fake news using text classification
Ahmed, H., Traoré, I., & Saad, S. (2017) · Security and Privacy · doi:10.1002/spy2.9 · Cited by 553
Survey of review spam detection using machine learning techniques
Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N., & Najada, H. A. (2015) · Journal Of Big Data · doi:10.1186/s40537-015-0029-9 · Cited by 530
Syntactic Stylometry for Deception Detection
Feng, S., Banerjee, R., & Choi, Y. (2012) · Cited by 499
Current State of Text Sentiment Analysis from Opinion to Emotion Mining
Yadollahi, A., Shahraki, A. G., & Zai͏̈ane, O. R. (2017) · ACM Computing Surveys · doi:10.1145/3057270 · Cited by 517
Opinion Mining and Sentiment Analysis
Liu, B. (2011) · Springer eBooks · doi:10.1007/978-3-642-19460-3_11 · Cited by 484
Self-citationSurvey on mining subjective data on the web
Tsytsarau, M., & Palpanas, T. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-011-0238-6 · Cited by 443
Spotting opinion spammers using behavioral footprints
Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487580 · Cited by 426
Self-citationThe influence of online reviews to online hotel booking intentions
Zhao, X., Wang, L., Guo, X., & Law, R. (2015) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-12-2013-0542 · Cited by 433
Aspect-based sentiment analysis of movie reviews on discussion boards
Thet, T. T., Na, J., & Khoo, C. S. G. (2010) · Journal of Information Science · doi:10.1177/0165551510388123 · Cited by 406
Recommender systems based on user reviews: the state of the art
Chen, L., Chen, G., & Wang, F. (2015) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-015-9155-5 · Cited by 432
Review 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 406
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
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 402
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
Self-citationLai, V., & Tan, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287590 · Cited by 377
Towards 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 383
Fu, B., Lin, J., Li, L., Faloutsos, C., Hong, J., & Sadeh, N. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2488202 · Cited by 377
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 401
Phrase dependency parsing for opinion mining
Wu, Y., Zhang, Q., Huang, X., & Wu, L. (2009) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1699648.1699700 · Cited by 365
Liu, B. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108639286 · Cited by 422
Self-citationA systematic literature review: Opinion mining studies from mobile app store user reviews
Genc-Nayebi, N., & Abran, A. (2016) · Journal of Systems and Software · doi:10.1016/j.jss.2016.11.027 · Cited by 358
(2013) · Cited by 353
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 356
Self-citationFinding unusual review patterns using unexpected rules
Jindal, N., Liu, B., & Lim, E. (2010) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1871437.1871669 · Cited by 345
Self-citationEstimating aggregate consumer preferences from online product reviews
Decker, R., & Trusov, M. (2010) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2010.09.001 · Cited by 342
Detection of Harassment on Web 2.0
Yin, D., Xue, Z., Hong, L., Davison, B. D., & Edwards, L. (2009) · Cited by 330
Modeling and Predicting the Helpfulness of Online Reviews
Liu, Y., Huang, X., An, A., & Yu, X. (2008) · Proceedings · doi:10.1109/icdm.2008.94 · Cited by 324
Schmiedel, T., Müller, O., & vom Brocke, J. (2019) · Organizational Research Methods · doi:10.1177/1094428118773858 · Cited by 319
Zhang, D., Zhou, L., Kehoe, J. L., & Kilic, I. D. Y. (2016) · Journal of Management Information Systems · doi:10.1080/07421222.2016.1205907 · Cited by 328
Creating and detecting fake reviews of online products
Salminen, J. O., Kandpal, C. S., Kamel, A. M., Jung, S., & Jansen, B. J. J. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102771 · Cited by 361
FRAUDAR: 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
Estimating the prevalence of deception in online review communities
Ott, M., Cardie, C., & Hancock, J. T. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187864 · Cited by 304
Detection of review spam: A survey
Heydari, A., Tavakoli, M. A., Salim, N. B., & Heydari, Z. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.12.029 · Cited by 307
A framework for fake review detection in online consumer electronics retailers
Barbado, R., Araque, Ó., & Iglesias, C. Á. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.002 · Cited by 300
False Information on Web and Social Media: A Survey
Kumar, S., & Shah, N. (2018) · arXiv · doi:10.48550/arxiv.1804.08559 · Cited by 289
Quality evaluation of product reviews using an information quality framework
Chen, C. C., & Tseng, Y.-D. (2010) · Decision Support Systems · doi:10.1016/j.dss.2010.08.023 · Cited by 281
Sentiment Classification Using a Single-Layered BiLSTM Model
Hameed, Z., & García-Zapirain, B. (2020) · IEEE Access · doi:10.1109/access.2020.2988550 · Cited by 296
Spam Review Detection with Graph Convolutional Networks
Li, A., Qin, Z., Liu, R., Yang, Y., & Li, D. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357820 · Cited by 273
Sentiment Analysis: A Multi-Faceted Problem
Liu, B. (2010) · IEEE Intelligent Systems · Cited by 254
Self-citationData Analytics for the Identification of Fake Reviews Using Supervised Learning
Alsubari, S. N. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.019625 · Cited by 264
Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Rubio, R. S., & Mora, H. (2019) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.08.003 · Cited by 265
SDTE: A Secure Blockchain-Based Data Trading Ecosystem
Dai, W., Dai, C., Choo, K. R., Cui, C., Zou, D., & Jin, H. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2928256 · Cited by 246
Mining Social Media: A Brief Introduction
Gundecha, P., & Liu, H. (2012) · doi:10.1287/educ.1120.0105 · Cited by 247
Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain
Yu, X., Liu, Y., Huang, X., & An, A. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.269 · Cited by 240
Neural networks for deceptive opinion spam detection: An empirical study
Ren, Y., & Ji, D. (2017) · Information Sciences · doi:10.1016/j.ins.2017.01.015 · Cited by 248
Mukherjee, A., Liu, B., Wang, J., Glance, N., & Jindal, N. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963192.1963240 · Cited by 235
Self-citationEvaluation Measures for Ordinal Regression
Baccianella, S., Esuli, A., & Sebastiani, F. (2009) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2009.230 · Cited by 234
Opinion leader detection: A methodological review
Bamakan, S. M. H., Nurgaliev, I., & Qu, Q. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.07.069 · Cited by 254
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 228
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
The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry
Lappas, T., Sabnis, G., & Valkanas, G. (2016) · Information Systems Research · doi:10.1287/isre.2016.0674 · Cited by 225
Fake Reviews Detection: A Survey
Mohawesh, R., Xu, S., Tran, S. N., Ollington, R., Springer, M., Jararweh, Y., & Maqsood, S. (2021) · IEEE Access · doi:10.1109/access.2021.3075573 · Cited by 238
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
Fake News: A Survey of Research, Detection Methods, and Opportunities.Preprint
Zhou, X., & Zafarani, R. (2018) · arXiv (Cornell University) · Cited by 214
Identify Online Store Review Spammers via Social Review Graph
Wang, G., Xie, S., Liu, B., & Yu, P. S. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2337542.2337546 · Cited by 200
Self-citationChen, H., & Zimbra, D. K. (2010) · IEEE Intelligent Systems · doi:10.1109/mis.2010.75 · Cited by 195
Building emotional dictionary for sentiment analysis of online news
Rao, Y., Lei, J., Wenyin, L., Li, Q., & Chen, M. (2013) · World Wide Web · doi:10.1007/s11280-013-0221-9 · Cited by 196
Zhao, Y., Da, J., & Yan, J. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102390 · Cited by 207
Fake News: Fundamental Theories, Detection Strategies and Challenges
Zhou, X., Zafarani, R., Shu, K., & Liu, H. (2019) · Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining · doi:10.1145/3289600.3291382 · Cited by 256
Sentiment analysis using deep learning approaches: an overview
Habimana, O., Li, Y., Li, R., Gu, X., & Yu, G. (2019) · Science China Information Sciences · doi:10.1007/s11432-018-9941-6 · Cited by 198
Battling the internet water army
Chen, C., Wu, K., Srinivasan, V., & Zhang, X. (2013) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2492517.2492637 · Cited by 188
Recommender systems — beyond matrix completion
Jannach, D., Resnick, P., Tuzhilin, A., & Zanker, M. (2016) · Communications of the ACM · doi:10.1145/2891406 · Cited by 180
Detecting positive and negative deceptive opinions using PU-learning
Fusilier, D. H., Montes-y-Gómez, M., Rosso, P., & Guzmán-Cabrera, R. (2014) · Information Processing & Management · doi:10.1016/j.ipm.2014.11.001 · Cited by 176
Li, P., & König, C. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772759 · Cited by 173
A Novel Transit Rider Satisfaction Metric: Rider Sentiments Measured from Online Social Media Data
Collins, C., Hasan, S., & Ukkusuri, S. V. (2013) · Journal of Public Transportation · doi:10.5038/2375-0901.16.2.2 · Cited by 172
Detecting Review Manipulation on Online Platforms with Hierarchical Supervised Learning
Kumar, N., Venugopal, D., Qiu, L., & Kumar, S. (2018) · Journal of Management Information Systems · doi:10.1080/07421222.2018.1440758 · Cited by 177
Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data
Lau, R. Y. K., Zhang, W., & Xu, W. X. (2017) · Production and Operations Management · doi:10.1111/poms.12737 · Cited by 176
Analyzing changes in hotel customers’ expectations by trip mode
Liu, S., Law, R., Rong, J., Li, G., & Hall, J. E. (2012) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2012.11.011 · Cited by 169
hPSD: A Hybrid PU-Learning-Based Spammer Detection Model for Product Reviews
Wu, Z., Cao, J., Wang, Y., Wang, Y., Zhang, L., & Wu, J. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2877161 · Cited by 166
Recommender Systems as Mechanisms for Social Learning*
Che, Y., & Hörner, J. (2017) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx044 · Cited by 166
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
