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

arXiv: https://arxiv.org/abs/2010.04576 · HTML: https://mlsanity.com/paper/2010.04576/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification](https://mlsanity.com/paper/2106.01191/) (2021)
- [Session-based Cyberbullying Detection in Social Media: A Survey](https://mlsanity.com/paper/2207.10639/) (2022)
- [SCCD: A Session-based Dataset for Chinese Cyberbullying Detection](https://mlsanity.com/paper/2501.15042/) (2025)
- [CoGate-LSTM: Prototype-Guided Feature-Space Gating for Mitigating Gradient Dilution in Imbalanced Toxic Comment Classification](https://mlsanity.com/paper/2510.17018/) (2025)
- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)

## Abstract (arXiv)

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.