# Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data

arXiv: https://arxiv.org/abs/2206.00524 · HTML: https://mlsanity.com/paper/2206.00524/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 84 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Are Sixteen Heads Really Better than One?](https://mlsanity.com/paper/1905.10650/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media](https://mlsanity.com/paper/1910.12574/) (2019)
- [KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media](https://mlsanity.com/paper/2007.13184/) (2020)
- [Exploiting BERT for End-to-End Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1910.00883/) (2019)
- [VnCoreNLP: A Vietnamese Natural Language Processing Toolkit](https://mlsanity.com/paper/1801.01331/) (2018)
- [Offensive Language and Hate Speech Detection for Danish](https://mlsanity.com/paper/1908.04531/) (2019)
- [Detecting Hate Speech in Social Media](https://mlsanity.com/paper/1712.06427/) (2017)
- [A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese](https://mlsanity.com/paper/2010.01891/) (2020)
- [A Large-scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts](https://mlsanity.com/paper/2103.11528/) (2021)
- [HSD Shared Task in VLSP Campaign 2019:Hate Speech Detection for Social Good](https://mlsanity.com/paper/2007.06493/) (2020)
- [A Simple and Efficient Ensemble Classifier Combining Multiple Neural Network Models on Social Media Datasets in Vietnamese](https://mlsanity.com/paper/2009.13060/) (2020)
- [Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection](https://mlsanity.com/paper/2102.10084/) (2021)
- [Hate Speech Detection on Vietnamese Social Media Text using the Bidirectional-LSTM Model](https://mlsanity.com/paper/1911.03648/) (2019)
- [Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese](https://mlsanity.com/paper/2009.11005/) (2020)
- [VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination](https://mlsanity.com/paper/1910.05608/) (2019)
- [Comparison Between Traditional Machine Learning Models And Neural Network Models For Vietnamese Hate Speech Detection](https://mlsanity.com/paper/2002.00759/) (2020)
- [Empirical Study of Text Augmentation on Social Media Text in Vietnamese](https://mlsanity.com/paper/2009.12319/) (2020)

## Built on by (most cited first, 9 of 9)

- [ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing](https://mlsanity.com/paper/2310.11166/) (2023)
- [VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understanding](https://mlsanity.com/paper/2403.15882/) (2024)
- [A Graph-Based Context-Aware Model to Understand Online Conversations](https://mlsanity.com/paper/2211.09207/) (2022)
- [ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese](https://mlsanity.com/paper/2309.02902/) (2023)
- [Transformer-Based Contextualized Language Models Joint with Neural Networks for Natural Language Inference in Vietnamese](https://mlsanity.com/paper/2411.13407/) (2024)
- [Breaking the Silence Detecting and Mitigating Gendered Abuse in Hindi, Tamil, and Indian English Online Spaces](https://mlsanity.com/paper/2404.02013/) (2024)
- [ViTHSD: Exploiting Hatred by Targets for Hate Speech Detection on Vietnamese Social Media Texts](https://mlsanity.com/paper/2404.19252/) (2024)
- [A Big Data-empowered System for Real-time Detection of Regional Discriminatory Comments on Vietnamese Social Media](https://mlsanity.com/paper/2411.02587/) (2024)
- [NEU-ESC: A Comprehensive Vietnamese dataset for Educational Sentiment analysis and topic Classification toward multitask learning](https://mlsanity.com/paper/2506.23524/) (2025)

## Abstract (arXiv)

Society needs to develop a system to detect hate and offense to build a healthy and safe environment. However, current research in this field still faces four major shortcomings, including deficient pre-processing techniques, indifference to data imbalance issues, modest performance models, and lacking practical applications. This paper focused on developing an intelligent system capable of addressing these shortcomings. Firstly, we proposed an efficient pre-processing technique to clean comments collected from Vietnamese social media. Secondly, a novel hate speech detection (HSD) model, which is the combination of a pre-trained PhoBERT model and a Text-CNN model, was proposed for solving tasks in Vietnamese. Thirdly, EDA techniques are applied to deal with imbalanced data to improve the performance of classification models. Besides, various experiments were conducted as baselines to compare and investigate the proposed model's performance against state-of-the-art methods. The experiment results show that the proposed PhoBERT-CNN model outperforms SOTA methods and achieves an F1-score of 67,46% and 98,45% on two benchmark datasets, ViHSD and HSD-VLSP, respectively. Finally, we also built a streaming HSD application to demonstrate the practicality of our proposed system.