%0 Conference Proceedings
%T Detecting Opinion Spammer Groups Through Community Discovery and Sentiment Analysis
%+ North Carolina State University [Raleigh] (NC State)
%+ Qatar Computing Research Institute [Doha, Qatar] (QCRI)
%A Choo, Euijin
%A Yu, Ting
%A Chi, Min
%Z Part 4: Authentication and Information Integration
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 29th IFIP Annual Conference on Data and Applications Security and Privacy (DBSEC)
%C Fairfax, VA, United States
%Y Pierangela Samarati
%I Springer International Publishing
%3 Data and Applications Security and Privacy XXIX
%V LNCS-9149
%P 170-187
%8 2015-07-13
%D 2015
%R 10.1007/978-3-319-20810-7_11
%K Opinion spammer groups
%K Sentiment analysis
%K Community discovery
%Z Computer Science [cs]Conference papers
%X In this paper we investigate on detection of opinion spammer groups in review systems. Most existing approaches typically build pure content-based classifiers, using various features extracted from review contents; however, spammers can superficially alter their review contents to avoid detections. In our approach, we focus on user relationships built through interactions to identify spammers. Previously, we revealed the existence of implicit communities among users based upon their interaction patterns [3]. In this work we further explore the community structures to distinguish spam communities from non-spam ones with sentiment analysis on user interactions. Through extensive experiments over a dataset collected from Amazon, we found that the discovered strong positive communities are more likely to be opinion spammer groups. In fact, our results show that our approach is comparable to the existing state-of-art content-based classifier, meaning that our approach can identify spammer groups reliably even if spammers alter their contents.
%G English
%Z TC 11
%Z WG 11.3
%2 https://inria.hal.science/hal-01745834/document
%2 https://inria.hal.science/hal-01745834/file/340025_1_En_11_Chapter.pdf
%L hal-01745834
%U https://inria.hal.science/hal-01745834
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC11
%~ IFIP-WG11-3
%~ IFIP-DBSEC
%~ IFIP-LNCS-9149