Inter-Player Data for the Prediction of Emotional Intensity in a Multiplayer Game

Brooke, Alexander ORCID: https://orcid.org/0009-0009-5907-9044, Crossley, Matthew ORCID: https://orcid.org/0000-0001-5965-8147, Lloyd, Huw ORCID: https://orcid.org/0000-0001-6537-4036 and Cunningham, Stuart ORCID: https://orcid.org/0000-0002-5348-7700 (2025) Inter-Player Data for the Prediction of Emotional Intensity in a Multiplayer Game. In: 2025 IEEE Conference on Games (CoG), pp. 1-8. Presented at IEEE Conference on Games (CoG), 26 August 2025 - 29 August 2025, Lisbon, Portugal.

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Abstract

This work assesses the feasibility of predicting emotional intensities for a given player in a testbed multiplayer game, using facial expression data collected from other players in the multiplayer group. Whilst there is significant literature on the utilisation of affect detection to build models of player experience, little research considers the additional data provided from other players in a multiplayer setting, despite the inherently shared experiences that they provide. A dataset describing 24 participants is collected, detailing ten levels of a testbed game, Colour Rush, with data collected describing facial expression activity and responses to the Discrete Emotions Questionnaire. The viability of modelling uncaptured player experiences is tested using artificial neural networks trained on facial expression data from target players, non-target players and a combination of both. Findings indicate that multiplayer data can be beneficial in the prediction of a target player's emotional responses, although this holds true only in a minority of cases, and for specific groups of players.