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
Venues:
COLING | TRAC | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
42–51
Language:
URL:
https://aclanthology.org/W18-4405
DOI:
Bibkey:
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., 2018)
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PDF:
https://aclanthology.org/W18-4405.pdf