A unified framework for detecting author spamicity by modeling review deviation. (1st December 2018) - Liu, Yuanchao; Pang, Bo
Highlights: Dealing with review spammer detection problem. We propose an unified framework for detecting author spamicity. A set of abnormity signals were proposed from deviation angle. An aspect-based deviation was designed to model latent content deviation. Experimental results suggest that our approach is appropriate for this task. Abstract: The success of e-commerce firms is highly dependent on the increasing number of customer reviews. However, to gain profit or fame, people may try to challenge the system by writing deceptive reviews that unjustly promote and/or demote target products or services. In this paper, a unified unsupervised framework is proposed to address the problem of opinion spamming. The rationale is that although not all outlier reviews are spam, spammers usually exhibit abnormities and deviations from normal users on certain dimensions concerning the same or even many products, thereby increasing their corresponding degrees of spamming (called "spamicity" in this paper). We introduce a set of abnormity signals from a review deviation angle and also present in detail an aspect-based review deviation dimension to model latent content deviation. Afterwards, a joint review deviation divergence is computed and ranked for detecting final opinion reviewer spamicity. Results of experiments conducted on a real-life Amazon review dataset demonstrate the effectiveness of the proposed approach.