Reliable fake review detection via modeling temporal and behavioral patterns

Abstract

Fake reviews have become a pervasive problem in online review systems, wherein fraudulent users manipulate the perception of an object (e.g., a restaurant) by fabricating fake reviews. Extensive work has been devoted to identifying fake reviews via modeling different factors separately, such as user features, object characteristics, and user-object bipartite relations. However, this problem remains challenging due to the fact that more advanced camouflage strategies are utilized by malicious users. In real-world scenarios, spammers may pretend to be normal users by giving fake reviews with the similar score distribution as normal users. To address these issues, we propose to explore the temporal patterns of users' review behavior, because spammers prefer to promote or demote the target businesses in a short period of time. In this work, we present a unified framework Reliable Fake Review Detection (RFRD) that explicitly models temporal patterns of users' review behavior into a probabilistic generative model. Moreover, the RFRD framework models users' underlying review credibility and objects' highly-skewed review distributions. We conduct experiments on two Yelp datasets, demonstrating the effectiveness of the proposed RFRD framework.