Cyberbullying Intervention Based on Convolutional Neural Networks
Qianjia Huang, Diana Inkpen, Jianhong Zhang, David Van Bruwaene
Abstract
This paper describes the process of building a cyberbullying intervention interface driven by a machine-learning based text-classification service. We make two main contributions. First, we show that cyberbullying can be identified in real-time before it takes place, with available machine learning and natural language processing tools. Second, we present a mechanism that provides individuals with early feedback about how other people would feel about wording choices in their messages before they are sent out. This interface not only gives a chance for the user to revise the text, but also provides a system-level flagging/intervention in a situation related to cyberbullying.- Anthology ID:
- W18-4405
- Volume:
- Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Venue:
- TRAC
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 42–51
- Language:
- URL:
- https://aclanthology.org/W18-4405
- DOI:
- Cite (ACL):
- Qianjia Huang, Diana Inkpen, Jianhong Zhang, and David Van Bruwaene. 2018. Cyberbullying Intervention Based on Convolutional Neural Networks. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pages 42–51, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Cyberbullying Intervention Based on Convolutional Neural Networks (Huang et al., TRAC 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-4405.pdf