Detecting Spam Game Reviews on Steam with a Semi-Supervised Approach

Abstract

The potential value of online reviews has led to more and more spam reviews appearing on the web. These spam reviews are widely distributed, harmful, and difficult to identify manually. In this paper, we explore and implement generalised approaches for identifying online deceptive spam game reviews from Steam. We analyse spam game reviews and present and validate some techniques to detect them. In addition, we aim to identify the unique features of game reviews and to create a labelled game review dataset based on different features. We were able to create a labelled dataset that can be used to identify spam game reviews in future research. Our method resulted in 5,021 of the 33,450 unlabelled Steam reviews being labelled as spam reviews, or approximately 15%. This falls within the expected range of 10-20% and maps to the Yelp figures of 14-20% of reviews are spam. Show more