Unveiling the value of user reviews on Steam: a predictive modeling of user engagement approach using Machine Learning

In an era where user-generated content is both ubiquitous and influential, accurately evaluating videogame reviews’ relevance becomes critical. The vast digital domain of videogames brims with user feedback, presenting the challenge of distinguishing genuinely helpful reviews. Our study, analyzing over a million videogame reviews from the Steam platform, employs cutting-edge machine learning techniques to ascertain review helpfulness. We applied both regression and binary classification models, revealing the latter’s enhanced predictive prowess. Interestingly, our findings contradict the anticipated benefit of incorporating features from pre-trained NLP models into enhancing prediction accuracy. This paper not only highlights methods for assessing review helpfulness effectively but also promotes the application of computational techniques for the insightful analysis of user-generated content. Furthermore, it provides valuable perspectives on the elements influencing user engagement and the intrinsic value of feedback within the context of videogame consumption, marking a significant contribution to understanding digital user interaction dynamics.