Hardcore Gamer Profiling: Results from an Unsupervised Learning
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Available online at www.sciencedirect.com Available onlineScienceDirect at www.sciencedirect.com Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000 ScienceDirect ScienceDirect www.elsevier.com/locate/procedia Procedia Computer Science 126 (2018) 1289–1297 Procedia Computer Science 00 (2018) 000–000 www.elsevier.com/locate/procedia International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2018, 3-5 September 2018, Belgrade, Serbia International Conference on Knowledge Based and Intelligent Information and Engineering Hardcore GamerSystems, Profiling: KES2018, 3 Results-5 September from 2018, a Belgrade,n unsupervised Serbia learning approach to playing behavior on the Steam platform Hardcore Gamer Profiling: Results from an unsupervised learning Florianapproach Baumann *to, Dominik playing Emmert, behavior Hermann on the Baumgartl, Steam Ricardo platform Buettner Aalen University, Germany Florian Baumann*, Dominik Emmert, Hermann Baumgartl, Ricardo Buettner Aalen University, Germany Abstract Based on a very large dataset of over 100 million Steam platform users we present the first comprehensive analysis of hardcore Abstractgamer profiles by over 700,000 hardcore players (users playing more than 20 hours per week) covering more than 3,300 games. 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ThisB.V. Peeris an-review open accessunder responsibilityarticle under of the KES CC International BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International. 10.1016/j.procs.2018.08.078 10.1016/j.procs.2018.08.078 1877-0509 1290 Florian Baumann et al. / Procedia Computer Science 126 (2018) 1289–1297 2 Author name / Procedia Computer Science 00 (2018) 000–000 That is why our study focuses on the behavior of “hardcore gamers” in a lot more detail. This type of player is more dedicated to gaming in almost every way, for example due to their high level of involvement in games, quantified by time spent playing and the scale of their respective in-game achievements [5-7]. Hardcore gamers can be described as people who play as a lifestyle preference and invest substantial amounts of time and money on games [8, 9]. They constitute the pioneers of a particular game, despite being the smallest group of players among the total player-base, and they help to define the experience for their fellow players through their own actions and behavior [7]. By identifying and analyzing their playing patterns it is possible to see how games are perceived by these influential players [7]. Such information can help support improvements in game design and game development [4, 7]. It is also important for the game industry to know and better understand their most influential players in terms of marketing and sales-promotional activities. In this paper, we provide interesting results from an unsupervised learning approach to the analysis of the playing behavior of over 700,000 hardcore players, covering more than 3,300 games. Our analysis is based on a very large dataset, collected by O’Neil et al. [10]. The dataset was originally used to analyze the gaming behavior of over 100 million Steam users in general, and offers numerous possibilities for follow-up research [10]. For this reason, in our study we focus on a subset of players with the highest playtime in the dataset. In order to fit into the subset, Steam players classified as “hardcore gamers” where selected. According to Poels et al. [17] “hardcore gamers” play 19 hours per week on average. O’Neil et al. [10] also demonstrated that the 95th percentile of gamers has a total playtime of 1,233.9 hours, while the 99th percentile has 2,660.1 hours of total playtime. Based on these results, players with a minimum total playtime of 2,000 hours and a minimum two-week playtime of 40 hours across all games were selected as “hardcore gamers”. This subset represents the active Steam community and is characterized by the large amount of time the players spend playing computer games. Our results identify six behavioral subtypes of hardcore gamers as well as the games and genres they play. In the next section we examine related work in the field and outline our methodology, before presenting results in-depth and a discussion of them. We then conclude by highlighting limitations and future work. 2. Research Background While scholars are becoming more and more interested in hedonic information systems, the focus on computer games as a serious field of research was established within the last two decades. As a result there are comprehensive general overviews of gaming behavior [4, 16, 18, 19] and a lot of studies on individual games and their related communities [11, 13]. The authors of these studies limit their findings since data were regularly biased due to the single game focus. Authors of studies analyzing gaming behavior using questionnaires limit their findings due to self-selection and social desirability biases. For these reasons scholars acknowledge the desirability of capturing unbiased actual behavior data of a broad range of players using gaming platform data. Since the Steam platform (store.steampowered.com)