StABLE: Analyzing Player Movement Similarity Using Text Mining
Luana Fragoso, Kevin Gordon Stanley · 2021 IEEE Conference on Games (CoG) · 2021
Digital games are increasingly delivered as services. Understanding how and why players interact with games on an ongoing basis is important for maintenance. Logs of player activity offer a potentially rich window into how and why players interact with games, but can be difficult to render into actionable insights because of their size and complexity. In particular, understanding the sequential behaviour in-game logs can be difficult. In this paper, we present the String Analysis of Behaviour Log Elements (StABLE) method, which renders location and activity data from a game log file into a sequence of symbols which can be analyzed using techniques from text mining. We show that by intelligently designing sequences of features, it is possible to cluster players into groups corresponding to experience or motivation by analyzing a dataset containing Minecraft game logs. The findings demonstrate the validity of the proposed method, and illustrate its potential utility in mining readily available data to better understand player behaviour.