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

Modelling Early User-Game Interactions for Joint Estimation of Survival Time and Churn Probability

Data-driven approaches which aim to identify and predict player engagement are becoming increasingly popular in games industry contexts. This is due to the growing practice of tracking and storing large volumes of in-game telemetries coupled with a desire to tailor the gaming experience to the end-user's needs. These approaches are particularly useful not just for companies adopting Game-as-a-Service (GaaS) models (e.g. for re-engagement strategies) but also for those working under persistent content-delivery regimes (e.g. for better audience targeting). A major challenge for the latter is to build engagement models of the user which are data-efficient, holistic and can generalize across multiple game titles and genres with minimal adjustments. This work leverages a theoretical framework rooted in engagement and behavioural science research for building a model able to estimate engagement-related behaviours employing only a minimal set of game-agnostic metrics. Through a series of experiments we show how, by modelling early user-game interactions, this approach can make joint estimates of long-term survival time and churn probability across several single-player games in a range of genres. The model proposed is very suitable for industry applications since it relies on a minimal set of metrics and observations, scales well with the number of users and is explicitly designed to work across a diverse range of titles.Comment: Submitted to IEEE Conference on Games 201

Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy and prediction. Towards the latter, previous work has used match data from a variety of player ranks from hobbyist to professional players. However, professional players have been shown to behave differently than lower ranked players. Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters. Here we show that, although there is a slightly reduced accuracy, mixed-rank datasets can be used to predict the outcome of professional matches, with suitably optimized configurations

Exploration and Skill Acquisition in a Major Online Game

Using data from a major commercial online game, Destiny, we track the development of player skill across time. From over 20,000 player record we identify 3475 players who have played on 50 or more days. Our focus is on how variability in elements of play affect subsequent skill development. After validating the persistent influence of differences in initial performance between players, we test how practice spacing, social play, play mode variability and a direct measure of game-world exploration affect learning rate. These latter two factors do not affect learning rate. Players who space their practice more learn faster, in line with our expectations, whereas players who coordinate more with other players learn slower, which contradicts our initial hypothesis. We conclude that not all forms of practice variety expedite skill acquisition. Online game telemetry is a rich domain for exploring theories of optimal skill acquisition

Debate:Games-based collaboration as a driver for massive-scale mental health research

Games have become a key part of the daily lives of many children and young people, irrespective of geographical location, age, gender or culture. Games form a gateway to these audiences – as well as tertiary groups like parents – which does not exist anywhere else. Additionally, behavioural telemetry from games forms an untapped and sizeable potential for mental health and well-being research. Working with the games industry gives mental health research and associated interventions a pathway for conducting research and working with communities at very large scales.</p

How do Software Professionals Use Local Informal Meetups?

This report presents the findings of the world’s first study of informal technology meetups. Local meetings organised by and for technology professionals have grown rapidly in size, reach and scope in recent years. Despite this, however, little is known about how participating in such communities impacts local professionals