A Low-code Approach to Identify Toxicity in MOBA Games

Resumo


In this work, we explore the use of KNIME to identify toxic behavior in the MOBA game DOTA 2. Using a dataset composed of 10530 messages taken from 1903 matches, we tested the use of KNIME to identify toxic messages obtaining an accuracy of 92% and 85% for toxic and non-toxic messages, respectively. The DOTA 2 game chat log was used to present a low-code approach to a supervised learning model for message classification. In addition to providing insight into the toxic behavior of MOBA players, our work supports the idea that low-code development can reach levels as good as traditional development. On the other hand, our study can also serve as a basis for more elaborate implementations that allow us to observe other aspects of toxic behavior from its detection, encouraging the construction of prevention and neutralization tools.

Palavras-chave: Toxicity in Games, Low-code, Natural Language Processing, DOTA 2

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Publicado
18/10/2021
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VAZ, Alexandre; BATISTA, Alexandre; SILVA, Farmy; COSTA, Lincoln Magalhães; XEXÉO, Geraldo. A Low-code Approach to Identify Toxicity in MOBA Games. In: TRILHA DE COMPUTAÇÃO – ARTIGOS CURTOS - SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 20. , 2021, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 305-308. DOI: https://doi.org/10.5753/sbgames_estendido.2021.19657.