Detecting harassment in real-time as conversations develop

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

Onderzoeksoutput: Hoofdstuk in boek/boekdeelBijdrage aan conferentie proceedingsWetenschappelijkpeer review

Samenvatting

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-2Engels
TitelProceedings of the Third Workshop on Abusive Language Online
Plaats van productieFlorence, Italy
UitgeverijAssociation for Computational Linguistics (ACL)
Pagina's19-24
DOI's
StatusGepubliceerd - 01 aug 2019

Vingerafdruk Duik in de onderzoeksthema's van 'Detecting harassment in real-time as conversations develop'. Samen vormen ze een unieke vingerafdruk.

  • Citeer dit

    Stoop, W., Kunneman, F., van den Bosch, A., & Miller, B. (2019). Detecting harassment in real-time as conversations develop. In Proceedings of the Third Workshop on Abusive Language Online (blz. 19-24). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W19-3503