The Experts below are selected from a list of 1557 Experts worldwide ranked by ideXlab platform
Yubo Kou - One of the best experts on this subject based on the ideXlab platform.
-
toxic behaviors in team based competitive gaming the case of League of Legends
Annual Symposium on Computer-Human Interaction in Play, 2020Co-Authors: Yubo KouAbstract:Toxic behaviors in online gaming such as flaming and harassment have been gaining attention from the research community, yet little consensus has formed about what constitutes toxic behavior. Game developers usually maintain a classification system of toxic behaviors, which oftentimes fails to reflect the dynamic and developing forms of toxicity. In this paper, we consider toxic behavior as situated action, and seek to establish a taxonomy of toxic behaviors from a player perspective in League of Legends, currently one of the largest Esports games in the world. Our findings include five primary types of toxic behaviors, as well as five contextual factors that could lead to toxic behavior. In doing so, we provide a holistic, detailed account of toxic behavior in a team-based competitive gaming context, highlight the role of player perspective in explaining toxic behavior, and extend existing scholarly discussions on toxicity and moderation.
-
emotion regulation in esports gaming a qualitative study of League of Legends
Proceedings of the ACM on Human-Computer Interaction, 2020Co-Authors: Yubo Kou, Xinning GuiAbstract:Today eSports gaming is enjoying growing popularity in the world and much attention from various research areas, including CSCW. eSports gaming is a highly competitive environment commonly associated with negative emotions such as anxiety and stress. However, little attention has been paid to emotion regulation in eSports gaming. In this study, we empirically investigated how players experience emotion and regulate emotions in League of Legends, one of the largest eSports games today. We identify four emotive factors, as well as emotion regulation strategies that players deploy to manage the emotions of their selves, teammates, and opponents. We further report on how they use emotion regulation in emotional self-care and emotional leadership. Building upon this set of findings, we discuss how the competitive eSports gaming context conditions emotion regulation in League of Legends, foreground emotion regulation expertise in competitive gaming, and derive implications for designing emotion regulation technologies.
-
Managing Disruptive Behavior through Non-Hierarchical Governance: Crowdsourcing in League of Legends and Weibo
Proceedings of the ACM on Human-Computer Interaction, 2017Co-Authors: Yubo Kou, Xinning Gui, Shaozeng Zhang, Bonnie NardiAbstract:Disruptive behaviors such as flaming and vandalism have been part of the Internet since its beginning. Various models of hierarchical governance have been established and managed in different online venues, with both successes and failures. Recently, a new model of non-hierarchical governance has emerged using crowdsourcing technology to allow an online community to manage itself. How do people view and work with non-hierarchical governance? In this paper, we present an interview study with people from two sites: the video game League of Legends and Weibo, a microblogging site in China. We found that people were passionate about participation in crowdsourcing, but at the same time, struggled with the system, and acted beyond their designated role within the system. We derive implications for designing online non-hierarchical governance from our research.
-
ranking practices and distinction in League of Legends
Annual Symposium on Computer-Human Interaction in Play, 2016Co-Authors: Yubo Kou, Xinning Gui, Yong Ming KowAbstract:Player ranking is a common feature of competitive online games, but little research work has closely examined the ways it mediates player practices within this game genre. In this paper, we present a qualitative study of player practices around ranking in League of Legends (LoL), published by Riot Games and currently one of the most popular eSports games. We found that ranking is a cornerstone of LoL's competitive gaming practices, shaping the ways players distinguished and narrated their game experiences, thus engendering a culture of collaboration and competition through distinction.
-
CHI PLAY - Ranking Practices and Distinction in League of Legends
Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play, 2016Co-Authors: Yubo Kou, Xinning Gui, Yong Ming KowAbstract:Player ranking is a common feature of competitive online games, but little research work has closely examined the ways it mediates player practices within this game genre. In this paper, we present a qualitative study of player practices around ranking in League of Legends (LoL), published by Riot Games and currently one of the most popular eSports games. We found that ranking is a cornerstone of LoL's competitive gaming practices, shaping the ways players distinguished and narrated their game experiences, thus engendering a culture of collaboration and competition through distinction.
Xinning Gui - One of the best experts on this subject based on the ideXlab platform.
-
emotion regulation in esports gaming a qualitative study of League of Legends
Proceedings of the ACM on Human-Computer Interaction, 2020Co-Authors: Yubo Kou, Xinning GuiAbstract:Today eSports gaming is enjoying growing popularity in the world and much attention from various research areas, including CSCW. eSports gaming is a highly competitive environment commonly associated with negative emotions such as anxiety and stress. However, little attention has been paid to emotion regulation in eSports gaming. In this study, we empirically investigated how players experience emotion and regulate emotions in League of Legends, one of the largest eSports games today. We identify four emotive factors, as well as emotion regulation strategies that players deploy to manage the emotions of their selves, teammates, and opponents. We further report on how they use emotion regulation in emotional self-care and emotional leadership. Building upon this set of findings, we discuss how the competitive eSports gaming context conditions emotion regulation in League of Legends, foreground emotion regulation expertise in competitive gaming, and derive implications for designing emotion regulation technologies.
-
Managing Disruptive Behavior through Non-Hierarchical Governance: Crowdsourcing in League of Legends and Weibo
Proceedings of the ACM on Human-Computer Interaction, 2017Co-Authors: Yubo Kou, Xinning Gui, Shaozeng Zhang, Bonnie NardiAbstract:Disruptive behaviors such as flaming and vandalism have been part of the Internet since its beginning. Various models of hierarchical governance have been established and managed in different online venues, with both successes and failures. Recently, a new model of non-hierarchical governance has emerged using crowdsourcing technology to allow an online community to manage itself. How do people view and work with non-hierarchical governance? In this paper, we present an interview study with people from two sites: the video game League of Legends and Weibo, a microblogging site in China. We found that people were passionate about participation in crowdsourcing, but at the same time, struggled with the system, and acted beyond their designated role within the system. We derive implications for designing online non-hierarchical governance from our research.
-
ranking practices and distinction in League of Legends
Annual Symposium on Computer-Human Interaction in Play, 2016Co-Authors: Yubo Kou, Xinning Gui, Yong Ming KowAbstract:Player ranking is a common feature of competitive online games, but little research work has closely examined the ways it mediates player practices within this game genre. In this paper, we present a qualitative study of player practices around ranking in League of Legends (LoL), published by Riot Games and currently one of the most popular eSports games. We found that ranking is a cornerstone of LoL's competitive gaming practices, shaping the ways players distinguished and narrated their game experiences, thus engendering a culture of collaboration and competition through distinction.
-
CHI PLAY - Ranking Practices and Distinction in League of Legends
Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play, 2016Co-Authors: Yubo Kou, Xinning Gui, Yong Ming KowAbstract:Player ranking is a common feature of competitive online games, but little research work has closely examined the ways it mediates player practices within this game genre. In this paper, we present a qualitative study of player practices around ranking in League of Legends (LoL), published by Riot Games and currently one of the most popular eSports games. We found that ranking is a cornerstone of LoL's competitive gaming practices, shaping the ways players distinguished and narrated their game experiences, thus engendering a culture of collaboration and competition through distinction.
-
playing with strangers understanding temporary teams in League of Legends
Annual Symposium on Computer-Human Interaction in Play, 2014Co-Authors: Yubo Kou, Xinning GuiAbstract:Game researchers have extensively studied how players form long-term social organizations such as guilds and clans to accomplish complex tasks such as raiding in online games. Few studies have paid attention to how temporary teams (or pickup groups) composed of strangers fulfill complex tasks. Riot Games' League of Legends, a team-based competitive online game, is played by two temporary teams. Players must collaborate with strangers in a relatively short time (about 30-50 minutes). How do players interact and collaborate with their teammates in temporary teams? To answer this question, we conducted an ethnographic study within the League of Legends community. We conducted 30 semi-structured interviews with experienced players. We found that rich social interaction exists within temporary teams. Players want to collaborate with strangers through communication and coordination. They discipline their own ways of interaction to facilitate collaboration. They try to exert influence over their teammates. We further discuss design implications for facilitating collaboration among strangers.
Erica Pasquini - One of the best experts on this subject based on the ideXlab platform.
-
Using API Data to Understand Learning in League of Legends: A Mixed Methods Study.
Educational Media International, 2019Co-Authors: Hannah R. Gerber, Kevin Sweeney, Erica PasquiniAbstract:This mixed-methods research was conducted to understand the impact of learning and player growth in a League of Legends summer camp. Eighteen adolescents engaged in a three-day sleep-over s...
Leandro Balby Marinho - One of the best experts on this subject based on the ideXlab platform.
-
profiling successful team behaviors in League of Legends
Brazilian Symposium on Multimedia and the Web, 2017Co-Authors: Fernando Felix Do Nascimento, Allan Sales Da Costa Melo, Igor Barbosa Da Costa, Leandro Balby MarinhoAbstract:Despite the increasing popularity of electronic sports (eSports), there is still a scarcity of academic works exploring the playing behavior of teams. Understanding the features that help to discriminate between successful and unsuccessful teams would help teams improving their strategies, such as determine performance metrics to reach. In this paper, we identify and characterize team behavior patterns based on historical matches from the very popular eSpor League of Legends web API. By applying machine learning and statistical analysis, we clustered teams' performance and investigate for each cluster how and to what extent these features have an influence on teams' success and failure. Some clusters are more likely to have winning teams than others, the results of our study helped to discover the characteristics that are associated with this predisposition and allowed us to model performance metrics of successful and unsuccessful team profiles. At all, we found 7 profiles in which were categorized into four levels in terms of winning team proportion: very low, moderate, high and very high.
-
WebMedia - Profiling Successful Team Behaviors in League of Legends
Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web, 2017Co-Authors: Fernando Felix Do Nascimento Junior, Allan Sales Da Costa Melo, Igor Barbosa Da Costa, Leandro Balby MarinhoAbstract:Despite the increasing popularity of electronic sports (eSports), there is still a scarcity of academic works exploring the playing behavior of teams. Understanding the features that help to discriminate between successful and unsuccessful teams would help teams improving their strategies, such as determine performance metrics to reach. In this paper, we identify and characterize team behavior patterns based on historical matches from the very popular eSpor League of Legends web API. By applying machine learning and statistical analysis, we clustered teams' performance and investigate for each cluster how and to what extent these features have an influence on teams' success and failure. Some clusters are more likely to have winning teams than others, the results of our study helped to discover the characteristics that are associated with this predisposition and allowed us to model performance metrics of successful and unsuccessful team profiles. At all, we found 7 profiles in which were categorized into four levels in terms of winning team proportion: very low, moderate, high and very high.
Miguel-angel Sicilia - One of the best experts on this subject based on the ideXlab platform.
-
Team efficiency and network structure: The case of professional League of Legends
Social Networks, 2019Co-Authors: Marçal Mora-cantallops, Miguel-angel SiciliaAbstract:Abstract Teams can be defined by their interactions and successful performance rests on their members’ behaviour. Although this topic has been studied both in sports and management, research on computer mediated team interactions, communication, cooperative work and efficiency in online competitive environments is scarce. In this article, networks will be used as a novel approach to understand how League of Legends professional players assist each other during a competitive match and to link their computer mediated behaviour and social interactions to their team's performance. Starting from a dataset consisting of 453.386 kill assists, the network structure and efficiency is assessed over 7.582 matches in total. After controlling for potential mixed-effects, such as the quality of the involved teams or their geography, this study reinforces previous research showing that team efficiency in the League of Legends professional scene is positively affected by the intensity of their interaction while centralization of resources is detrimental. Networks with high intensity and low inner centralization are, therefore, related to a higher performance as a team not only in traditional sports but also in computer mediated contexts.
-
Player-centric networks in League of Legends
Social Networks, 2018Co-Authors: Marçal Mora-cantallops, Miguel-angel SiciliaAbstract:Abstract Online competitive gaming has become one of the largest collective human activities globally and understanding motivations and social interaction is still not fully achieved. The aim of this study is to develop a basis for a systematic classification of player-centric networks in competitive online games based on structural network criteria. Using data extracted from League of Legends players, their matches and machine learning techniques, a classification of personal player networks in League of Legends is proposed. Results show the resulting egonets can be potentially grouped in four clusters related to their egos playing habits, ranging from solo to team players.
-
motivations to read and learn in videogame lore the case of League of Legends
Technological Ecosystems for Enhancing Multiculturality, 2016Co-Authors: Marcal Mora Cantallops, Miguel-angel SiciliaAbstract:League of Legends is a multiplayer online battle arena game that follows a freemium model, but where the in-game transactions do little to impact a player's performance or ability. Although its characters, called Champions, can be purchased with actual (or ingame) money, there is a weekly rotation of ten free champions where players can test new champions before buying them. When new content is launched or a Champion is changed or reworked, players react to that by either using that Champion more or less (depending on whether they perceive it as positive or negative). Additionally, from time to time, Special Events are launched. The Bilgewater event, "Burning Tides", is one of such events, the only one that happened in season five, that aimed to merge lore with gameplay and represented Riot most ambitious story-telling experiment to date. Moreover, the seven Champions that had its background tied to Bilgewater were added to the free rotation on top of the other ten during the event, so there was no economic limiting factor that could hinder players from using them. This paper aims to understand how an event such as Bilgewater impact players in two dimensions; first, whether special events have an impact in Champion Usage and second, whether lore events can have an impact in player's motivation to read and learn in League of Legends. Results show that the effect in Champion Usage is unclear, with other factors being more relevant than Special Events, but that lore events have the potential to drive motivation to read and learn about the Champions and their story, events or optimal builds in the game, with a clear peak of interest in them.
-
TEEM - Motivations to read and learn in videogame lore: the case of League of Legends
Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality, 2016Co-Authors: Marcal Mora Cantallops, Miguel-angel SiciliaAbstract:League of Legends is a multiplayer online battle arena game that follows a freemium model, but where the in-game transactions do little to impact a player's performance or ability. Although its characters, called Champions, can be purchased with actual (or ingame) money, there is a weekly rotation of ten free champions where players can test new champions before buying them. When new content is launched or a Champion is changed or reworked, players react to that by either using that Champion more or less (depending on whether they perceive it as positive or negative). Additionally, from time to time, Special Events are launched. The Bilgewater event, "Burning Tides", is one of such events, the only one that happened in season five, that aimed to merge lore with gameplay and represented Riot most ambitious story-telling experiment to date. Moreover, the seven Champions that had its background tied to Bilgewater were added to the free rotation on top of the other ten during the event, so there was no economic limiting factor that could hinder players from using them. This paper aims to understand how an event such as Bilgewater impact players in two dimensions; first, whether special events have an impact in Champion Usage and second, whether lore events can have an impact in player's motivation to read and learn in League of Legends. Results show that the effect in Champion Usage is unclear, with other factors being more relevant than Special Events, but that lore events have the potential to drive motivation to read and learn about the Champions and their story, events or optimal builds in the game, with a clear peak of interest in them.