Identifying spam in the iOS app store
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
Spotting fake reviewer groups in consumer reviews
Proceedings of the 21st international conference on World Wide Web
Exploiting shopping and reviewing behavior to re-score online evaluations
Proceedings of the 21st international conference companion on World Wide Web
Fake reviews: the malicious perspective
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Simultaneously detecting fake reviews and review spammers using factor graph model
Proceedings of the 5th Annual ACM Web Science Conference
Spotting opinion spammers using behavioral footprints
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Why people hate your app: making sense of user feedback in a mobile app store
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Iolaus: securing online content rating systems
Proceedings of the 22nd international conference on World Wide Web
Detecting collusive spammers in online review communities
Proceedings of the sixth workshop on Ph.D. students in information and knowledge management
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Online reviews provide valuable information about products and services to consumers. However, spammers are joining the community trying to mislead readers by writing fake reviews. Previous attempts for spammer detection used reviewers' behaviors, text similarity, linguistics features and rating patterns. Those studies are able to identify certain types of spammers, e.g., those who post many similar reviews about one target entity. However, in reality, there are other kinds of spammers who can manipulate their behaviors to act just like genuine reviewers, and thus cannot be detected by the available techniques. In this paper, we propose a novel concept of a heterogeneous review graph to capture the relationships among reviewers, reviews and stores that the reviewers have reviewed. We explore how interactions between nodes in this graph can reveal the cause of spam and propose an iterative model to identify suspicious reviewers. This is the first time such intricate relationships have been identified for review spam detection. We also develop an effective computation method to quantify the trustiness of reviewers, the honesty of reviews, and the reliability of stores. Different from existing approaches, we don't use review text information. Our model is thus complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after they evaluate our results.