Identifying Singleton Spammers via Spammer Group Detection

Kumar, D, Shaalan, Y, Zhang, X and Chan, J 2018, 'Identifying Singleton Spammers via Spammer Group Detection', in Dinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi (ed.) Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018) Part I, Melbourne, Australia, 3-6 June 2018, pp. 656-667.


Document type: Conference Paper
Collection: Conference Papers

Title Identifying Singleton Spammers via Spammer Group Detection
Author(s) Kumar, D
Shaalan, Y
Zhang, X
Chan, J
Year 2018
Conference name PAKDD 2018: Lecture Notes in Artificial Intelligence Volume 10937
Conference location Melbourne, Australia
Conference dates 3-6 June 2018
Proceedings title Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018) Part I
Editor(s) Dinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
Publisher Springer
Place of publication Cham, Switzerland
Start page 656
End page 667
Total pages 12
Abstract Opinion spam is a well-recognized threat to the credibility of online reviews. Existing approaches to detecting spam reviews or spammers examine review content, reviewer behavior and reviewer-product network, and often operate on the assumption that spammers write at least several if not many fake reviews. On the other hand, spammers setup multiple sockpuppet IDs and write one-time, singleton spam reviews to avoid detection. It is reported that for most review sites, a large portion, sometimes over 90%, of reviewers are singletons (identified by the reviewer ID). Singleton spammers are difficult to catch due to the scarcity of behavioral clues. In this paper, we argue that the key to detect singleton spammers (and their fake reviews) is to detect group spam attacks by inferring the hidden collusiveness among them. To address the challenge of lack of explicit behavioral signals for singleton reviewers, we propose to infer the hidden reviewer-product associations by completing the review-product matrix by leveraging the product and review metadata and text. Experiments on three real-life Yelp datasets established that our approach can effectively detect singleton spammers via group detection, which are often missed by existing approaches.
Subjects Pattern Recognition and Data Mining
Keyword(s) opinion spam
singleton spammers
sockpuppet IDs
Inductive matrix completion
DOI - identifier 10.1007/978-3-319-93034-3_52
Copyright notice © Springer International Publishing AG, part of Springer Nature 2018
ISBN 9783319930336
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