Detecting harassment in real-time as conversations develop

Wessel Stoop; Florian Kunneman; A. van den Bosch; Benjamin Miller

doi:10.18653/v1/W19-3503

Detecting harassment in real-time as conversations develop

Wessel Stoop, Florian Kunneman, A. van den Bosch, Benjamin Miller

Meertens Instituut

Onderzoeksoutput: Hoofdstuk in boek/boekdeel › Bijdrage aan conferentie proceedings › Wetenschappelijk › peer review

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We developed a machine-learning-based method to detect video game players that harass teammates or opponents in chat earlier in the conversation. This real-time technology would allow gaming companies to intervene during games, such as issue warnings or muting or banning a player. In a proof-of-concept experiment on League of Legends data we compute and visualize evaluation metrics for a machine learning classifier as conversations unfold, and observe that the optimal precision and recall of detecting toxic players at each moment in the conversation depends on the confidence threshold of the classifier: the threshold should start low, and increase as the conversation unfolds. How fast this sliding threshold should increase depends on the training set size.

Originele taal-2	Engels
Titel	Proceedings of the Third Workshop on Abusive Language Online
Plaats van productie	Florence, Italy
Uitgeverij	Association for Computational Linguistics (ACL)
Pagina's	19-24
DOI's	https://doi.org/10.18653/v1/W19-3503
Status	Gepubliceerd - 01 aug. 2019

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10.18653/v1/W19-3503Licentie: CC BY

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title = "Detecting harassment in real-time as conversations develop",

abstract = "We developed a machine-learning-based method to detect video game players that harass teammates or opponents in chat earlier in the conversation. This real-time technology would allow gaming companies to intervene during games, such as issue warnings or muting or banning a player. In a proof-of-concept experiment on League of Legends data we compute and visualize evaluation metrics for a machine learning classifier as conversations unfold, and observe that the optimal precision and recall of detecting toxic players at each moment in the conversation depends on the confidence threshold of the classifier: the threshold should start low, and increase as the conversation unfolds. How fast this sliding threshold should increase depends on the training set size.",

author = "Wessel Stoop and Florian Kunneman and {van den Bosch}, A. and Benjamin Miller",

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doi = "10.18653/v1/W19-3503",

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Detecting harassment in real-time as conversations develop. / Stoop, Wessel; Kunneman, Florian; van den Bosch, A. et al.
Proceedings of the Third Workshop on Abusive Language Online. Florence, Italy: Association for Computational Linguistics (ACL), 2019. blz. 19-24.

Onderzoeksoutput: Hoofdstuk in boek/boekdeel › Bijdrage aan conferentie proceedings › Wetenschappelijk › peer review

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Detecting harassment in real-time as conversations develop

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