The effect of multiplayer game modes on inter-player data for player experience modelling

Abstract

Research into social compliance, emotional contagion and behavioural synchronicity shows promise for various avenues of work concerning human-computer interaction, and a wider understanding of emotion. Despite their relevance, few studies have applied findings from these domains to player experience modelling in a multiplayer game, in itself having applications in entertainment, education and healthcare. Further to this, of the little work making use of inter-player data to model aspects of player experience, none considers the differences that may be found across common multiplayer game modes. This work therefore makes use of data collected across players in a series of common multiplayer game modes, considering the utility of inter-player data for predictive modelling using artificial neural networks in each. Results suggest that approaches modelling measures of players' experiences in terms of discrete emotion intensities are best made using their own facial expressions in nearly all circumstances, but past this, facial expression data from team based and competitive game modes shows the greatest promise. Considering the additional data separations available to team-based gameplay, we find that data collected from players on an opposing team shows greater utility for prediction of target player experience than data collected from a player on the same team. Regarding this, we make suggestions for the most applicable avenues for future research into the utilisation of inter-player data for emotional modelling.