HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media

Hsin-Yu Chen, Cheng-Te Li


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
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/author-url/2020.emnlp-main.200.pdf
Video:
 https://slideslive.com/38939269
Code
 HsinYu7330/HENIN