What We Talk About When We Talk About Games: Bottom-Up Game Studies Using Natural Language Processing

In this paper, we endorse and advance an emerging bottomup approach to game studies that utilizes techniques from natural language processing. Our contribution is threefold: we present the first complete review of the growing body of work through which this methodology has been innovated; we present a latent semantic analysis model that constitutes the first application of this fundamental bottom-up technique to the domain of digital games; and finally, unlike earlier projects that have only written about their models, we introduce and evaluate a tool that serves as an interface to ours. This tool is GameNet, in which nearly 12,000 games are linked to the games to which they are most related. From an expert evaluation, we demonstrate that, beyond being an interface to our model, GameNet may be used more generally as a research tool for game scholars. Specifically, we find that it is especially useful for the scholar who wishes to explore a relatively unfamiliar area of games, but that it may also be used to discover unforeseen cases related to topics that have already been thoroughly researched.