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Predictive Psychological Player Profiling

Video games have become the largest portion of the entertainment industry and everyday life of millions of players around the world. Considering games as cultural artifacts, it seems imperative to study both games and players to understand underlying psychological and behavioral implications of interacting with this medium, especially since video games are rich domains for occurrence of rich affective experiences annotated by and measurable via in-game behavior. This thesis is a presentation of a series of studies that attempt to model player perception and behavior as well as their psychosocial attributes in order to make sense of interrelations of these factors and implications the findings have for game designers and researchers. In separate studies including survey and in-game telemetry data of millions of players, we delve into reliable measures of player psychological need satisfaction, motivation and generational cohort and cross reference them with in-game behavioral patterns by presenting systemic frameworks for classification and regression. We introduce a measurement of perceived need satisfaction and discuss generational effects in playtime and motivation, present a robust prediction model for ordinally processed motivations and review classification techniques when it comes to playstyles derived from player choices. Additionally, social aspects of play, such as social influence and contagion as well as disruptive behavior, is discussed along with advanced statistical models to detect and explain them. / <p>Note: The papers are not included in the fulltext online</p><p>Vid tidpunkten för disputationen var följande delarbete opublicerat: delarbete I (manuskript).</p><p>At the time of the doctoral defence the following paper was unpublished: paper I (manuscript).</p>

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-41436
Date January 2021
CreatorsAzadvar, Ahmad
PublisherMalmö universitet, Institutionen för datavetenskap och medieteknik (DVMT), Ubisoft Sweden, User Research, Malmö
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeLicentiate thesis, comprehensive summary, info:eu-repo/semantics/masterThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationStudies in Computer Science

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