In order to appeal to a growing market, game developers are offering a wide variety of activities. It is becoming necessary to understand which psychological need each activity caters for. The purpose of this paper is to demonstrate the development and evaluation of an instrument to assess which basic psychological needs are satisfied by different video games. This work is part of a growing effort in HCI to develop surveys able to capture subtle nuances of the player experience. This model, UPEQ, was developed by transforming a self-determination theory questionnaire into a video game specific survey. UPEQ consists of three subscale of Autonomy, Competence and Relatedness, which, through two studies focusing on development and validation of the model showed significant correlations with other self-reported measures of sense of transportation to the game as well as enjoyment of and engagement with the game. Regression with ingame behavior of players tracked by game engine also confirmed that each subscale of UPEQ independently predicts playtime, money spent on the game and playing as a group. CCS ConceptsCCS → Human-centered computing → Human computer interaction (HCI) → HCI design and evaluation methods → User models.
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.
Is it possible to detect toxicity in games just by observing in-game behavior? If so, what are the behavioral factors that will help machine learning to discover the unknown relationship between gameplay and toxic behavior? In this initial study, we examine whether it is possible to predict toxicity in the MOBA gameFor Honor by observing in-game behavior for players that have been labeled as toxic (i.e. players that have been sanctioned by Ubisoft community managers). We test our hypothesis of detecting toxicity through gameplay with a dataset of almost 1,800 sanctioned players, and comparing these sanctioned players with unsanctioned players. Sanctioned players are defined by their toxic action type (offensive behavior vs. unfair advantage) and degree of severity (warned vs. banned). Our findings, based on supervised learning with random forests, suggest that it is not only possible to behaviorally distinguish sanctioned from unsanctioned players based on selected features of gameplay; it is also possible to predict both the sanction severity (warned vs. banned) and the sanction type (offensive behavior vs. unfair advantage). In particular, all random forest models predict toxicity, its severity, and type, with an accuracy of at least 82%, on average, on unseen players. This research shows that observing in-game behavior can support the work of community managers in moderating and possibly containing the burden of toxic behavior.
In a wide range of social networks, people's behavior is inluenced by social contagion: we do what our network does. Networks often feature particularly inluential individuals, commonly called łinluencers'. ' Existing work suggests that in-game social networks in online games are similar to reallife social networks in many respects. However, we do not know whether there are in-game equivalents to inluencers. We therefore applied standard social network features used to identify inluencers to the online multiplayer shooter Tom Clancy's The Division. Results show that network featuredeined inluencers had indeed an outsized impact on playtime and social play of players joining their in-game network.
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