Encyclopedia of Social Network Analysis and Mining

2018 Edition
| Editors: Reda Alhajj, Jon Rokne

Identifying Spam in Reviews

  • Zhiang WuEmail author
  • Lu Zhang
  • Youquan Wang
  • Jie Cao
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-7131-2_110200




A set of attributes indicating the spamming behavior

Review Spammer

A malicious user who write fraudulent reviews

Spam Detection

Identify spam reviews, users, or groups

Spam Review

A deceptive review to manipulate the opinon about the product

Water Army

A group of ghostwriters paid to post fake reviews


The fake reviews target at promoting or demoting the sale of products in e-commerce sites, and attracting attention or triggering curiosity in social networking sites, by creating and spreading purposeful comments. Hence, the goal of spam detection is to identify spam objects, including review/opinion spam, spam users, and spammer groups, from reviews.


Online reviews are actually a kind of user-generated content (UGC) and hence provide a voice for customers to praise or criticize products,...

This is a preview of subscription content, log in to check access.



This work was partially supported by the National Key Research and Development Program of China (2017YFD0401001), the National Natural Science Foundation of China (71571093, 91646204, 71372188), National Center for International Joint Research on E-Business Information Processing (2013B01035), Industry Projects in Jiangsu S&T Pillar Program (BE2014141), and Key/Surface Projects of Natural Science Research in Jiangsu Provincial Colleges and Universities (14KJA520001, 15KJB520012 and 15KJB520011).


  1. Chen C, Wu K, Srinivasan V, Zhang X (2013) Battling the internet water army: detection of hidden paid posters. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining. ACM, Niagara, ON, Canada. pp 116–120Google Scholar
  2. Cheng Z, Gao B, Sun C, Jiang Y, Liu TY (2011) Let web spammers expose themselves. In: Proceedings of the fourth ACM international conference on web search and data mining. ACM, Kowloon, Hong Kong. pp 525–534Google Scholar
  3. Costa LF, Rodrigues FA, Travieso G, Villas Boas PR (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56(1):167–242CrossRefGoogle Scholar
  4. Fakhraei S, Foulds J, Shashanka M, Getoor L (2015) Collective spammer detection in evolving multirelational social networks. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Sydney, NSW, Australia. pp 1769–1778Google Scholar
  5. Fayazi A, Lee K, Caverlee J, Squicciarini A (2015) Uncovering crowdsourced manipulation of online reviews. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, Santiago, Chile. pp 233–242Google Scholar
  6. Fei G, Mukherjee A, Liu B, Hsu M, Castellanos M, Ghosh R (2013) Exploiting burstiness in reviews for review spammer detection. ICWSM 13:175–184Google Scholar
  7. Forman C, Ghose A, Wiesenfeld B (2008) Examining the relationship between reviews and sales: the role of reviewer identity disclosure in electronic markets. Inf Syst Res 19(3):291–313CrossRefGoogle Scholar
  8. Grier C, Thomas K, Paxson V, Zhang M (2010) Spam: the understanding on 140 characters or less. In: Proceedings of the 17th ACM conference on computer and communications security. ACM, Chicago, IL, USA. pp 27–37Google Scholar
  9. Gyongyi Z, Garcia-Molina H (2005) Web spam taxonomy. In: First international workshop on adver-sarial information retrieval on the web (AIRWeb 2005), ACM, Chiba, JapanGoogle Scholar
  10. Heydari A, ali Tavakoli M, Salim N, Heydari Z (2015) Detection of review spam: a survey. Expert Syst Appl 42(7):3634–3642CrossRefGoogle Scholar
  11. Heymann P, Koutrika G, Garcia-Molina H (2007) Fighting spam on social web sites: a survey of approaches and future challenges. IEEE Internet Comput 11(6):36–45CrossRefGoogle Scholar
  12. Jagatic TN, Johnson NA, Jakobsson M, Menczer F (2007) Social phishing. Commun ACM 50(10):94–100CrossRefGoogle Scholar
  13. Jindal N, Liu B (2007a) Analyzing and detecting review spam. In: Seventh IEEE international conference on data mining. IEEE, Omaha, NE, USA. pp 547–552Google Scholar
  14. Jindal N, Liu B (2007b) Review spam detection. In: Proceedings of the 16th international conference on World Wide Web. ACM, Banff, AB, Canada. pp 1189–1190Google Scholar
  15. Jindal N, Liu B (2008) Opinion spam and analysis. In: Proceedings of the 2008 international conference on web search and data mining. ACM, Palo Alto, CA, USA. pp 219–230Google Scholar
  16. Kc S, Mukherjee A (2016) On the temporal dynamics of opinion spamming: case studies on yelp. In: Proceedings of the international World Wide Web conferences, ACM, Montreal, Canada. pp 369–379Google Scholar
  17. Lam S, Riedl J (2004) Shilling recommender systems for fun and profit. In: WWW’04, ACM, New York, NY, USA. pp 393–402Google Scholar
  18. Lee JS, Zhu D (2012) Shilling attack detection-a new approach for a trustworthy recommender system. INFORMS J Comput 24(1):117–131zbMATHCrossRefGoogle Scholar
  19. Lee K, Caverlee J, Webb S (2010) Uncovering social spammers: social honeypots + machine learning. In: Proceeding SIGIR’10 Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, ACM, Geneva, Switzerland. pp 435–442Google Scholar
  20. Li F, Huang M, Yang Y, Zhu X (2011) Learning to identify review spam. In: IJCAI Proceedings-international joint conference on artificial intelligence, vol 22, IJCAI/AAAI, Barcelona, Catalonia, Spain. p 2488Google Scholar
  21. Lim EP, Nguyen VA, Jindal N, Liu B, Lauw HW (2010) Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM international conference on information and knowledge management. ACM, pp 939–948Google Scholar
  22. Lin Y, Zhu T, Wang X, Zhang J, Zhou A (2014a) Towards online review spam detection. In: Proceedings of the 23rd international conference on World Wide Web. ACM, Seoul, Korea. pp 341–342Google Scholar
  23. Lin Y, Zhu T, Wu H, Zhang J, Wang X, Zhou A (2014b) Towards online anti-opinion spam: Spotting fake reviews from the review sequence. In: Advances in social networks analysis and mining (ASONAM), 2014 IEEE/ACM international conference on, IEEE, Beijing, China. pp 261–264Google Scholar
  24. Liu H, Zhang Y, Lin H, Wu J, Wu Z, Zhang X (2013) How many zombies around you? In: Data mining (ICDM), 2013 I.E. 13th international conference on, IEEE, Dallas, TX, USA, pp 1133–1138Google Scholar
  25. Mehta B, Nejdl W (2009) Unsupervised strategies for shilling detection and robust collaborative filtering. User Model User-Adap Inter 19(1–2):65–97CrossRefGoogle Scholar
  26. Mukherjee A, Liu B, Glance N (2012) Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st international conference on World Wide Web. ACM, Lyon, France. pp 191–200Google Scholar
  27. Mukherjee A, Kumar A, Liu B, Wang J, Hsu M, Castellanos M, Ghosh R (2013a) Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Chicago, IL, USA. pp 632–640Google Scholar
  28. Mukherjee A, Venkataraman V, Liu B, Glance NS (2013b) What yelp fake review filter might be doing? In: ICWSM AAAI, Cambridge, MA, USAGoogle Scholar
  29. Ott M, Choi Y, Cardie C, Hancock JT (2011) Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol 1. Association for Computational Linguistics, Association for Computational Linguistics 就是Pubblisher??, Portland, OR, USA. pp 309–319Google Scholar
  30. Ott M, Cardie C, Hancock J (2012) Estimating the prevalence of deception in online review communities. In: Proceedings of the 21st international conference on World Wide Web. ACM, Lyon, France. pp 201–210Google Scholar
  31. Pitsillidis A, Levchenko K, Kreibich C, Kanich C, Voelker GM, Paxson V, Weaver N, Savage S (2010) Botnet judo: fighting spam with itself. In: NDSS The Internet Society, San Diego, CA, USAGoogle Scholar
  32. Rayana S, Akoglu L (2015) Collective opinion spam detection: Bridging review networks and metadata. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Sydney, NSW, Australia. pp 985–994Google Scholar
  33. Sahami M, Dumais S, Heckerman D, Horvitz E (1998) A bayesian approach to filtering junk e-mail. In: Learning for text categorization: Papers from the 1998 workshop, vol 62, pp 98–105Google Scholar
  34. Spirin N, Han J (2012) Survey on web spam detection: principles and algorithms. ACM SIGKDD Explor Newslett 13(2):50–64CrossRefGoogle Scholar
  35. Wang G, Xie S, Liu B, Yu PS (2011) Review graph based online store review spammer detection. In: Data mining (ICDM), 2011 I.E. 11th international conference on, IEEE, Vancouver, BC, Canada. pp 1242–1247Google Scholar
  36. Wang Y, Wu Z, Bu Z, Cao J, Yang D, Gorman G, He W (2016) Discovering shilling groups in a real e-commerce platform. Online Inf Rev 40(1):62–78CrossRefGoogle Scholar
  37. Wu X, Feng Z, Fan W, Gao J, Yu Y (2013) Detecting marionette microblog users for improved information credibility. In: Machine learning and knowledge discovery in databases. Springer, Berlin, pp 483–498CrossRefGoogle Scholar
  38. Wu Z, Wang Y, Wang Y, Wu J, Cao J, Zhang L (2015) Spammers detection from product reviews: a hybrid model. In: 2015 I.E. international conference on data mining, ICDM 2015, Atlantic City, 14–17 Nov 2015, pp 1039–1044Google Scholar
  39. Yu R, He X, Liu Y (2014) GLAD: group anomaly detection in social media analysis. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, NY, USA. pp 372–381Google Scholar
  40. Zhou B, Pei J (2009) Link spam target detection using page farms. ACM Trans Knowl Discov Data (TKDD) 3(3):13Google Scholar

Copyright information

© Springer Science+Business Media LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Jiangsu Provincial Key Laboratory of E-BusinessNanjing University of Finance and EconomicsNanjingChina

Section editors and affiliations

  • Guandong Xu
    • 1
  • Peng Cui
    • 2
  1. 1.University of Technology SydneySydneyAustralia
  2. 2.Tsinghua UniversityBeijingChina