Identifying Singleton Spammers via Spammer Group Detection

Opinion spam is a well-recognized threat to the credibility of online reviews. Existing approaches to detecting spam reviews or spammers examine review content, reviewer behavior and reviewer-product network, and often operate on the assumption that spammers write at least several if not many fake reviews. On the other hand, spammers setup multiple sockpuppet IDs and write one-time, singleton spam reviews to avoid detection. It is reported that for most review sites, a large portion, sometimes over 90%, of reviewers are singletons (identified by the reviewer ID). Singleton spammers are difficult to catch due to the scarcity of behavioral clues. In this paper, we argue that the key to detect singleton spammers (and their fake reviews) is to detect group spam attacks by inferring the hidden collusiveness among them. To address the challenge of lack of explicit behavioral signals for singleton reviewers, we propose to infer the hidden reviewer-product associations by completing the review-product matrix by leveraging the product and review metadata and text. Experiments on three real-life Yelp datasets established that our approach can effectively detect singleton spammers via group detection, which are often missed by existing approaches.