Abstract
In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions. Despite their empirical success, we argue that a critical missing piece is the model explainability, i.e., why a particular piece of media session is detected as cyberbullying. In this paper, therefore, we propose a novel deep model, HEterogeneous Neural Interaction Networks (HENIN), for explainable cyberbullying detection. HENIN contains the following components: a comment encoder, a post-comment co-attention sub-network, and session-session and post-post interaction extractors. Extensive experiments conducted on real datasets exhibit not only the promising performance of HENIN, but also highlight evidential comments so that one can understand why a media session is identified as cyberbullying.- Anthology ID:
- 2020.emnlp-main.200
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2543–2552
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.200
- DOI:
- 10.18653/v1/2020.emnlp-main.200
- Cite (ACL):
- Hsin-Yu Chen and Cheng-Te Li. 2020. HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2543–2552, Online. Association for Computational Linguistics.
- Cite (Informal):
- HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media (Chen & Li, EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.200.pdf
- Code
- HsinYu7330/HENIN