Moment-to-moment Engagement Prediction through the Eyes of the Observer: PUBG Streaming on Twitch

TL;DR

This study demonstrates that viewer engagement during gameplay can be accurately predicted using game telemetry and chat data, revealing moments of high engagement through machine learning models trained on Twitch streaming data.

Contribution

The paper introduces a novel approach to defining and predicting gameplay engagement from the perspective of viewers using neural networks trained on telemetry and chat logs.

Findings

Engagement models achieved up to 84% accuracy.

Models performed well across different streamers and play styles.

40 gameplay features sufficed for high-accuracy predictions.

Abstract

Is it possible to predict moment-to-moment gameplay engagement based solely on game telemetry? Can we reveal engaging moments of gameplay by observing the way the viewers of the game behave? To address these questions in this paper, we reframe the way gameplay engagement is defined and we view it, instead, through the eyes of a game's live audience. We build prediction models for viewers' engagement based on data collected from the popular battle royale game PlayerUnknown's Battlegrounds as obtained from the Twitch streaming service. In particular, we collect viewers' chat logs and in-game telemetry data from several hundred matches of five popular streamers (containing over 100,000 game events) and machine learn the mapping between gameplay and viewer chat frequency during play, using small neural network architectures. Our key findings showcase that engagement models trained solely on 40 gameplay features can reach accuracies of up to 80% on average and 84% at best. Our models are scalable and generalisable as they perform equally well within- and across-streamers, as well as across streamer play styles.