Interpretable and effective opinion spam detection via temporal patterns mining across websites

Yuan Yuan, Sihong Xie, Chun Ta Lu, Jie Tang, Philip S. Yu

科研成果: 书/报告/会议事项章节会议稿件同行评审

9 引用 (Scopus)

摘要

Millions of ratings and reviews on online review websites are influential over business revenues and customer experiences. However, spammers are posting fake reviews in order to gain financial benefits, at the cost of harming honest businesses and customers. Such fake reviews can be illegal and it is important to detect spamming attacks to eliminate unjust ratings and reviews. However, most of the current approaches can be incompetent as they can only utilize data from individual websites independently, or fail to detect more subtle attacks even they can fuse data from multiple sources. Further, the revealed evidence fails to explain the more complicated real world spamming attacks, hindering the detection processes that usually have human experts in the loop. We close this gap by introducing a novel framework that can jointly detect and explain the potential attacks. The framework mines both macroscopic level temporal sentimental patterns and microscopic level features from multiple review websites. We construct multiple sentimental time series to detect atomic dynamics, based on which we mine various cross-site sentimental temporal patterns that can explain various attacking scenarios. To further identify individual spams within the attacks with more evidence, we study and identify effective microscopic textual and behavioral features that are indicative of spams. We demonstrate via human annotations, that the simple and effective framework can spot a sizable collection of spams that have bypassed one of the current commercial anti-spam systems.

源语言英语
主期刊名Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
编辑Ronay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
出版商Institute of Electrical and Electronics Engineers Inc.
96-105
页数10
ISBN(电子版)9781467390040
DOI
出版状态已出版 - 2016
已对外发布
活动4th IEEE International Conference on Big Data, Big Data 2016 - Washington, 美国
期限: 5 12月 20168 12月 2016

出版系列

姓名Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

会议

会议4th IEEE International Conference on Big Data, Big Data 2016
国家/地区美国
Washington
时期5/12/168/12/16

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