Are Neighbors Alike? A Semisupervised Probabilistic Collaborative Learning Model for Online Review Spammers Detection

Abstract

Online platforms continually face the menace of review spammers who manipulate ratings and comments for personal gain, eroding trust in online reviews. Despite various methods proposed to tackle this issue, challenges, like collusion between spammers, scarce labels, and skewed data distribution, persist. This paper introduces a practical solution to these problems by integrating individual behavioral features with a reviewer network, and it proposes a novel semisupervised collaborative learning model. Imagine our approach as a detective tool for online platforms. It works by investigating who is reviewing what and how they are linked within the reviewer network, which is like uncovering hidden relationships among reviewers to catch spammers. Through this method, our model significantly boosts the accuracy and transparency in identifying spammers. We tested this approach on real-world review data from different types of online platforms, and it outperformed other methods. Importantly, the model is resilient, even when you have limited data or when some labels might not be perfect. By using our approach, online platforms can spot fake reviews and spammers in a more cost-effective way, ensuring that the reviews are more reliable and that it is easier for consumers to make informed decisions. It is a practical step forward in the fight against fake reviews online.