Detecting harassment in real-time as conversations develop

Wessel Stoop, Florian Kunneman, Antal van den Bosch, Ben Miller


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.
Anthology ID:
W19-3503
Volume:
Proceedings of the Third Workshop on Abusive Language Online
Month:
August
Year:
2019
Address:
Florence, Italy
Venue:
ALW
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19–24
Language:
URL:
https://aclanthology.org/W19-3503
DOI:
10.18653/v1/W19-3503
Bibkey:
Cite (ACL):
Wessel Stoop, Florian Kunneman, Antal van den Bosch, and Ben Miller. 2019. Detecting harassment in real-time as conversations develop. In Proceedings of the Third Workshop on Abusive Language Online, pages 19–24, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Detecting harassment in real-time as conversations develop (Stoop et al., ALW 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-3503.pdf