2021/10/06 by Andraz De Luisa, De Luisa, Andraž, Jan Hartman +13
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Digital Games and Media #FOS: Computer and information sciences #Machine Learning (cs.LG) #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.2110.02896
openalex publication_date 2021/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The video game industry has seen rapid growth over the last decade. Thousands of video games are released and played by millions of people every year, creating a large community of players. Steam is a leading gaming platform and social networking site, which allows its users to purchase and store games. A by-product of Steam is a large database of information about games, players, and gaming behavior. In this paper, we take recent video games released on Steam and aim to discover the relation between game popularity and a game's features that can be acquired through Steam. We approach this task by predicting the popularity of Steam games in the early stages after their release and we use a Bayesian approach to understand the influence of a game's price, size, supported languages, release date, and genres on its player count. We implement several models and discover that a genre-based hierarchical approach achieves the best performance. We further analyze the model and interpret its coefficients, which indicate that games released at the beginning of the month and games of certain genres correlate with game popularity.