With this, we hope to help game developers and communities to promote integration dynamics. Furthermore, we raise and discuss different elements and mechanics in games that impact the well-being of the community through the individualization of the player, elimination of solidarities, and affirmation of indifference towards toxic behavior.
The results show that most players suffered offenses and offended other people in the game, in addition to recognizing MOBA as the genre that generates the most impatience among them. For this, four of the main games of the MOBA genre were analyzed with the support of the MDA Framework and a questionnaire with players. In this article, we investigate how game design elements influence toxic behavior, using toxic disinhibition factors as a basis for the analysis. However, it is possible that other factors influence the toxicity. Many studies address this area, focusing on players and, principally, on the content and its negative impact on the community. Reports of hate speech and anti-gaming behavior are common in many online multiplayer games. Toxic behavior is one of the problems most associated with the gaming community. Isso gera um efeito de invisibilidade através da camuflagem da estrutura de grupo, e dificulta a formação de solidariedades entre os jogadores. O resultado dissoé um ambiente de dissimulação, onde os jogadores podem ser facilmente enganados por outros e suas expectativas sobre as performances dos companheiros não são atendidas. 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 particular, all random forest models predict toxicity, its severity, and type, with an accuracy of at least 82%, on average, on unseen players. banned) and the sanction type (offensive behavior vs. 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. unfair advantage) and degree of severity (warned vs. Sanctioned players are defined by their toxic action type (offensive behavior vs. 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. players that have been sanctioned by Ubisoft community managers). 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.