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.
Referências
J. Blackburn and H. Kwak, “Stfu noob! predicting crowd sourced decisions on toxic behavior in online games”, in Proceedings of the 23rd international conference on World wide web, 2014, pp. 877–8
H. Kwak and J. Blackburn, “Linguistic analysis of toxic behavior in an online video game", in International Conference on Social Informatics, Springer, 2014, pp. 209–217
M. Märtens, S. Shen, A. Iosup, and F. Kuipers, “Toxicity detection in multiplayer online games", in Proceedings of the 2015 International Workshop on Network and Systems Support for Games, IEEE Press, 2015.
Y. Kou, “Toxic behaviors in team-based competitive gaming: The case of league of legends”, in Proceedings of the Annual Symposium on Computer-Human Interaction in Play, 2020, pp. 81–89.
N. A. Beres, J. Frommel, E. Reid, R. L. Mandryk, and M. Klarkowski, “Don’t you know that you’re toxic: Normalization of toxicity in online gaming", in Proceedings of the 2021 CHI Conference on Human Factorsin Computing Systems, 2021, pp. 1–15.
A. S. Maiya, “ktrain: A low-code library for augmented machine learning”, arXiv preprint arXiv:2004.10703, 2020.
G. Daniel, J. Cabot, L. Deruelle, and M. Derras, “Xatkit: A multimodal low-code chatbot development framework”, IEEE Access, vol. 8, pp.15332–15346, 2020.
Opendota api (v18.0.0). [Online]. Available: https://docs.opendota.com/
Dota 2 matches dataset: Base de dados. [Online]. Available: https://www.kaggle.com/jraramirez/dota-2-matches-dataset
kaggle, comunidade on-line de cientistas de dados. [Online]. Available: https://www.kaggle.com/
X-partitioner, 2020. [Online]. Available: [link].
K.T. Classifier, Text classifier, 2020. [Online]. Available: https://www.knime.com/book/text-classifier
K. aggregator, X-aggregator, 2020. [Online]. Available: [link].
W. Tang, J. Hu, H. Zhang, P. Wu, and H. He, “Kappa coefficient: apopular measure of rater agreement”, Shanghai archives of psychiatry, vol. 27, no. 1, pp. 62–67, Feb 2015.
M. L. McHugh, “Interrater reliability: the kappa statistic”, Biochemiamedica, vol. 22, no. 3, pp. 276–282, 2012, 23092060 [pmid].
O. N. P. Cardoso, “Recuperação de informação”, INFOCOMP Journal of Computer Science, vol. 2, no. 1, pp. 33–38, 2000.
P. F. Matos, L. Lombardi, R. Ciferri, T. Pardo, C. Ciferri, and M. Vieira, “Relatório técnico “métricas de avaliação”, Universidade Federal de São Carlos, Tech. Rep., 2009.
