# Abusive Language Detection with Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1904.04073 · HTML: https://mlsanity.com/paper/1904.04073/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 19 of 19)

- [Emotion Based Hate Speech Detection using Multimodal Learning](https://mlsanity.com/paper/2202.06218/) (2022)
- [Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity](https://mlsanity.com/paper/2104.08829/) (2021)
- [On the rise of fear speech in online social media](https://mlsanity.com/paper/2303.10311/) (2023)
- [Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability](https://mlsanity.com/paper/2103.17191/) (2021)
- [Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model](https://mlsanity.com/paper/2104.03189/) (2021)
- [Twitter User Representation Using Weakly Supervised Graph Embedding](https://mlsanity.com/paper/2108.08988/) (2021)
- [Contextual Hate Speech Detection in Code Mixed Text using Transformer Based Approaches](https://mlsanity.com/paper/2110.09338/) (2021)
- [Examining Temporal Bias in Abusive Language Detection](https://mlsanity.com/paper/2309.14146/) (2023)
- [Leveraging Transformers for Hate Speech Detection in Conversational Code-Mixed Tweets](https://mlsanity.com/paper/2112.09986/) (2021)
- [PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Prompts](https://mlsanity.com/paper/2309.07727/) (2023)
- [SOLD: Sinhala Offensive Language Dataset](https://mlsanity.com/paper/2212.00851/) (2022)
- [Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content](https://mlsanity.com/paper/2301.10871/) (2023)
- [Trustworthy Hate Speech Detection Through Visual Augmentation](https://mlsanity.com/paper/2409.13557/) (2024)
- [Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach](https://mlsanity.com/paper/2409.20204/) (2024)
- [Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning](https://mlsanity.com/paper/2103.11806/) (2021)
- [A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection](https://mlsanity.com/paper/2404.01822/) (2024)
- [Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks](https://mlsanity.com/paper/2203.02123/) (2022)
- [Multiclass Hate Speech Detection with RoBERTa-OTA: Integrating Transformer Attention and Graph Convolutional Networks](https://mlsanity.com/paper/2603.04414/) (2026)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)

## Abstract (arXiv)

Abuse on the Internet represents a significant societal problem of our time. Previous research on automated abusive language detection in Twitter has shown that community-based profiling of users is a promising technique for this task. However, existing approaches only capture shallow properties of online communities by modeling follower-following relationships. In contrast, working with graph convolutional networks (GCNs), we present the first approach that captures not only the structure of online communities but also the linguistic behavior of the users within them. We show that such a heterogeneous graph-structured modeling of communities significantly advances the current state of the art in abusive language detection.