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

arXiv: https://arxiv.org/abs/2003.03438 · HTML: https://mlsanity.com/paper/2003.03438/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 1 of 1)

- ["bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games](https://mlsanity.com/paper/2604.10175/) (2026)

## Abstract (arXiv)

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.