Temporal Opinion Spam Detection by Multivariate Indicative Signals

Abstract

Online consumer reviews reflect the testimonials of real people, unlike e.g., ads. As such, they have critical impact on potential consumers, and indirectly on businesses. Problematically, such financial incentives have created a market for spammers to fabricate reviews to unjustly promote or demote businesses, activities known as opinion spam (Jindal and Liu 2008). Most existing work on this problem have formulations based on static review data, with respective techniques operating in an offline fashion. Spam campaigns, however, are intended to make most impact during their course. Abnormal events triggered by spammers’ activities could be masked in the load of future events, which static analysis would fail to identify. In this work, we approach the opinion spam problem with a temporal formulation. Specifically, we monitor a list of carefully selected indicative signals of opinion spam over time and design efficient techniques to both detect and characterize abnormal events in real-time. Experiments on two different datasets show that our approach is fast, effective, and practical to be deployed in real-world systems.