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

Julian Frommel, Valentin Sagl, Ansgar E. Depping, Colby Johanson, Matthew K. Miller, Regan L. Mandryk

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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% accuracy (F1) and 20.1% explained variance (R2) on unseen data, with features based on verbal communication demonstrating the highest potential. Our findings can inform the design of multiplayer games and game communities, and guide the development of systems for matchmaking and mitigating toxic behaviour in online games.

Original languageEnglish
Title of host publicationCHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery (ACM)
ISBN (Electronic)9781450367080
DOIs
Publication statusPublished - 21 Apr 2020
Event2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020 - Honolulu, United States
Duration: 25 Apr 202030 Apr 2020

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020
Country/TerritoryUnited States
CityHonolulu
Period25/04/2030/04/20

Keywords

  • affiliation
  • bonding
  • cooperative games
  • evaluation
  • machine learning
  • prediction
  • recognition
  • social interaction

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