Towards Factor-oriented Understanding of Video Game Genres using Exploratory Factor Analysis on Steam Game Tags

Abstract

Context. Genre classification, as a tool of information managing, facilitates greatly the users' needs in identifying and retrieving information items of interest within mutually exclusive divisions of collection. Different from that of other products, current video game genre classifications suffer from providing such exclusive divisions. In the domain of game studies, game genre has long been an essential topic, when consensus has not yet been reached. On the other hand, the user-generated tags have been widely adopted enabling end users to annotate and interact with freely. It provides a unique set of crowd-sourced metadata that facilitates the description and understanding of the target products. Such feature is also adopted for video game distribution platforms. Objective. Thus, the goal of this work is to investigate the factor-oriented understanding of video game genre classification based on the analysis of large volume of game tag data from Steam platform. It shall contribute largely to the research on game genres within the game studies domain. Method. For such purpose, exploratory factor analysis is used herein towards the exploration of latent grouping of game tags seeking the underlying patterns. Results. As a result, 29 factors are extracted based on the over 20,000 video game data with 77 gameplay-based tags. It shows that the majority of video games on Steam can be rather seen as a combination of several factors to clear-cut classification.