# Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks

arXiv: https://arxiv.org/abs/2203.02123 · HTML: https://mlsanity.com/paper/2203.02123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 72 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media](https://mlsanity.com/paper/2007.13184/) (2020)
- [Characterizing and Detecting Hateful Users on Twitter](https://mlsanity.com/paper/1803.08977/) (2018)
- [One-step and Two-step Classification for Abusive Language Detection on Twitter](https://mlsanity.com/paper/1706.01206/) (2017)
- [Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages](https://mlsanity.com/paper/2108.05927/) (2021)
- [Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key](https://mlsanity.com/paper/2104.10788/) (2021)
- [Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection](https://mlsanity.com/paper/1804.03124/) (2018)
- [Abusive Language Detection with Graph Convolutional Networks](https://mlsanity.com/paper/1904.04073/) (2019)
- [Detecting the Hate Code on Social Media](https://mlsanity.com/paper/1703.05443/) (2017)
- [Joint Modelling of Emotion and Abusive Language Detection](https://mlsanity.com/paper/2005.14028/) (2020)
- [Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection](https://mlsanity.com/paper/2004.13432/) (2020)
- [Interlayer link prediction in multiplex social networks: an iterative degree penalty algorithm](https://mlsanity.com/paper/2002.02140/) (2020)
- [Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering](https://mlsanity.com/paper/1710.03430/) (2017)
- [Interlayer Link Prediction in Multiplex Social Networks Based on Multiple Types of Consistency between Embedding Vectors](https://mlsanity.com/paper/2008.05212/) (2020)

## Abstract (arXiv)

The pervasiveness of offensive language on the social network has caused adverse effects on society, such as abusive behavior online. It is urgent to detect offensive language and curb its spread. Existing research shows that methods with community structure features effectively improve the performance of offensive language detection. However, the existing models deal with community structure independently, which seriously affects the effectiveness of detection models. In this paper, we propose an end-to-end method based on community structure and text features for offensive language detection (CT-OLD). Specifically, the community structure features are directly captured by the graph attention network layer, and the text embeddings are taken from the last hidden layer of BERT. Attention mechanisms and position encoding are used to fuse these features. Meanwhile, we add user opinion to the community structure for representing user features. The user opinion is represented by user historical behavior information, which outperforms that represented by text information. Besides the above point, the distribution of users and tweets is unbalanced in the popular datasets, which limits the generalization ability of the model. To address this issue, we construct and release a dataset with reasonable user distribution. Our method outperforms baselines with the F1 score of 89.94%. The results show that the end-to-end model effectively learns the potential information of community structure and text, and user historical behavior information is more suitable for user opinion representation.