Opinion Spam Detection in Social Networks

As social media websites have emerged as popular platforms for sharing and spreading real-time information on the Internet, impostors see huge opportunities in taking advantage of such systems to spread distorted information. Online social networks and review websites have become targets of opinion spamming. More and more traditional review websites allow users to “friend” or “follow” each other so as to enhance overall user experience. This has brought significant advances of opinion spam detection using users’ social networks or heterogeneous networks of various entities in a broader sense. In this chapter, different techniques are introduced, such as belief propagation and collective positive-unlabeled learning, that leverage the intricate relations between different entities in the network. We discuss these methods via the application of spam detection on review-hosting websites and popular social media platforms.

[1]  Arjun Mukherjee,et al.  Spotting fake reviewer groups in consumer reviews , 2012, WWW.

[2]  Huan Liu,et al.  Social Spammer Detection in Microblogging , 2013, IJCAI.

[3]  Krishna P. Gummadi,et al.  Understanding and combating link farming in the twitter social network , 2012, WWW.

[4]  Claire Cardie,et al.  Towards a General Rule for Identifying Deceptive Opinion Spam , 2014, ACL.

[5]  Philip S. Yu,et al.  Meta path-based collective classification in heterogeneous information networks , 2012, CIKM.

[6]  Olga Veksler,et al.  Markov random fields with efficient approximations , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[7]  Arjun Mukherjee,et al.  Exploiting Burstiness in Reviews for Review Spammer Detection , 2021, ICWSM.

[8]  Michael Sirivianos,et al.  Aiding the Detection of Fake Accounts in Large Scale Social Online Services , 2012, NSDI.

[9]  Guofei Gu,et al.  Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter , 2012, WWW.

[10]  Michael I. Jordan,et al.  Loopy Belief Propagation for Approximate Inference: An Empirical Study , 1999, UAI.

[11]  Ponnurangam Kumaraguru,et al.  Followers or Phantoms? An Anatomy of Purchased Twitter Followers , 2014, ArXiv.

[12]  Leman Akoglu,et al.  Collective Opinion Spam Detection: Bridging Review Networks and Metadata , 2015, KDD.

[13]  Bing Liu,et al.  Opinion spam and analysis , 2008, WSDM '08.

[14]  Gianluca Stringhini,et al.  Detecting spammers on social networks , 2010, ACSAC '10.

[15]  Wojciech Pieczynski,et al.  Pairwise Markov random fields and its application in textured images segmentation , 2000, 4th IEEE Southwest Symposium on Image Analysis and Interpretation.

[16]  Ee-Peng Lim,et al.  Detecting product review spammers using rating behaviors , 2010, CIKM.

[17]  Arjun Mukherjee,et al.  Detecting Campaign Promoters on Twitter Using Markov Random Fields , 2014, 2014 IEEE International Conference on Data Mining.

[18]  Paolo Rosso,et al.  Using PU-Learning to Detect Deceptive Opinion Spam , 2013, WASSA@NAACL-HLT.

[19]  Philip S. Yu,et al.  Building text classifiers using positive and unlabeled examples , 2003, Third IEEE International Conference on Data Mining.

[20]  Arjun Mukherjee,et al.  Spotting Fake Reviews using Positive-Unlabeled Learning , 2014, Computación y Sistemas.

[21]  Lise Getoor,et al.  Collective Classification in Network Data , 2008, AI Mag..

[22]  D. W. Scott,et al.  Variable Kernel Density Estimation , 1992 .

[23]  Ee-Peng Lim,et al.  Finding unusual review patterns using unexpected rules , 2010, CIKM.

[24]  Judea Pearl,et al.  Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach , 1982, AAAI.

[25]  William T. Freeman,et al.  Understanding belief propagation and its generalizations , 2003 .

[26]  Claire Cardie,et al.  Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.

[27]  Marc Lemercier,et al.  SPOT 1.0: Scoring Suspicious Profiles on Twitter , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[28]  Christos Faloutsos,et al.  Opinion Fraud Detection in Online Reviews by Network Effects , 2013, ICWSM.

[29]  Yi Yang,et al.  Learning to Identify Review Spam , 2011, IJCAI.

[30]  Arjun Mukherjee,et al.  Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns , 2015, ICWSM.

[31]  Charu C. Aggarwal,et al.  An Introduction to Social Network Data Analytics , 2011, Social Network Data Analytics.

[32]  Philip S. Yu,et al.  Review Graph Based Online Store Review Spammer Detection , 2011, 2011 IEEE 11th International Conference on Data Mining.

[33]  Philip S. Yu,et al.  Review spam detection via temporal pattern discovery , 2012, KDD.

[34]  Kang Liu,et al.  Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu , 2015, CL.

[35]  Emiliano De Cristofaro,et al.  Paying for Likes?: Understanding Facebook Like Fraud Using Honeypots , 2014, Internet Measurement Conference.

[36]  Bing Liu,et al.  Spotting Fake Reviews via Collective Positive-Unlabeled Learning , 2014, 2014 IEEE International Conference on Data Mining.