Tweeting your Destiny: Profiling Users in the Twitter Landscape around an Online Game

Social media has become a major communication channel for communities centered around video games. Consequently, social media offers a rich data source to study online communities and the discussions evolving around games. Towards this end, we explore a large-scale dataset consisting of over 1 million tweets related to the online multiplayer shooter Destiny and spanning a time period of about 14 months using unsupervised clustering and topic modelling. Furthermore, we correlate Twitter activity of over 3,000 players with their playtime. Our results contribute to the understanding of online player communities by identifying distinct player groups with respect to their Twitter characteristics, describing subgroups within the Destiny community, and uncovering broad topics of community interest.Comment: Accepted at IEEE Conference on Games 201

Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The Division

Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division. On the other end, we ask them to report their levels of competence, autonomy, relatedness and presence using the Ubisoft Perceived Experience Questionnaire. After processing the survey responses in an ordinal fashion we employ preference learning methods based on support vector machines to infer the mapping between gameplay and the reported four motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the best obtained models reach accuracies of near certainty, from 92% up to 94% on unseen players.Comment: Version accepted for IEEE Conference on Games, 201

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

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.Comment: Version accepted for the Conference on the Foundations of Digital Games 2020 - Malt

Turning Users' In-Game Behaviours into Actionable Adaptive Gamification Strategies using the PEAS Framework

Adaptive gamification answers the need to customize engagement strategies because users are motivated by different game elements and mechanics. To better understand these individual preferences, user modelling is vital. However, gameful designers must make many decisions on matching profiling data to actual adaptation strategies, which makes modelling particularly challenging. The lack of a standardized and guided process for adaptive gamification hinders replicability, comparability, and complicates making adaptation dynamic. In this study, we analyzed a persuasive gameful application (Play\&Go) to show how in-game behaviours can be translated into adaptation strategies. We used an existing adaptation framework (PEAS) grounded in the games and gamification literature. Our work demonstrates the suitability of the PEAS model as a shared, standardized method for adaptive gamification and shows how it can guide the process of transforming user behaviours into actionable adaptation strategies

Turning Users\u27 In-Game Behaviours into Actionable Adaptive Gamification Strategies using the PEAS Framework

Adaptive gamification answers the need to customize engagement strategies because users are motivated by different game elements and mechanics. To better understand these individual preferences, user modelling is vital. However, gameful designers must make many decisions on matching profiling data to actual adaptation strategies, which makes modelling particularly challenging. The lack of a standardized and guided process for adaptive gamification hinders replicability, comparability, and complicates making adaptation dynamic. In this study, we analyzed a persuasive gameful application (Play\&Go) to show how in-game behaviours can be translated into adaptation strategies. We used an existing adaptation framework (PEAS) grounded in the games and gamification literature. Our work demonstrates the suitability of the PEAS model as a shared, standardized method for adaptive gamification and shows how it can guide the process of transforming user behaviours into actionable adaptation strategies