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
- Editors:
- Sarah T. Roberts, Joel Tetreault, Vinodkumar Prabhakaran, Zeerak Waseem
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/W19-3503.pdf