25/04/2020

Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games

Julian Frommel, Valentin Sagl, Ansgar Depping, Colby Johanson, Matthew Miller, Regan Mandryk

Keywords: affiliation, social interaction, evaluation, prediction, recognition, cooperative games, machine learning, bonding

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Abstract: Online social interactions in multiplayer games can be supportive and positive or toxic and harmful; however, few methods can easily assess interpersonal interaction quality in games. We use behavioural traces to predict affiliation between dyadic strangers, facilitated through their social interactions in an online gaming setting. We collected audio, video, in-game, and self-report data from 23 dyads, extracted 75 features, trained Random Forest and Support Vector Machine models, and evaluated their performance predicting binary (high/low) as well as continuous affiliation toward a partner. The models can predict both binary and continuous affiliation with up to 79.1

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