Abstract
Research on game genres and player types has been one of the pillars of game studies, providing a theoretical foundation for effective game design and development practices. Therein, various player types are proposed when the players are classified based on their preferences or play styles. However, players' social and behavioral traits, e.g., their personality, that can, to a certain extent, influence their ways of play and preferences of genres are limitedly studied. In this short paper, we investigate the different personalities of the players who play different game genres using the review data from the Steam online platform. For such a purpose, we collected 1.9 million player reviews for the 40 top-ranking games of the four most popular game genres. Using a pre-trained neural network classifier, we summarize the collective personality of the players for each selected game genre based on the Big Five personality model. The early results show that the collective player personality of different game genres differs and is worth considering in game design. This study aims to explore and initiate discussions on the latent connection between player personality and their preferences and behaviors and to draw attention to the effective adoption of data mining techniques for the domain of game studies. Furthermore, such studies shall contribute to considering enhanced user experience in game design and development.