Fake Reviewer Groups Detection from Digital Market
Online product reviews have become increasingly important in digital consumer markets where they play a crucial role in making purchasing decisions by most consumers. Unfortunately, spammers often take advantage of online reviews by writing fake reviews to promote/demote certain products. Most of the previous studies have focused on detecting fake reviews and individual fake reviewer-ids. However, to target a particular product, fake reviewers work collaboratively in groups and/or create multiple fake ids to write reviews and control the sentiments of the product. This work addresses the problem of detecting such fake reviewer groups.
We proposed a top-down framework for candidate fake reviewer groups’ detection based on the DeepWalk approach on reviewers’ graph data and a (modified) semisupervised clustering method, which can incorporate partial background knowledge. Our experimental results demonstrated that the proposed approach is able to identify the candidate spammer groups with reasonable accuracy. This approach can also be extended to detect groups of opinion spammers in social media (e.g. fake comments or fake postings) with temporal affinity, semantic characteristics, and sentiment analysis.