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

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.