# Hate Speech Detection on Vietnamese Social Media Text using the Bidirectional-LSTM Model

arXiv: https://arxiv.org/abs/1911.03648 · HTML: https://mlsanity.com/paper/1911.03648/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [VLM as Policy: Common-Law Content Moderation Framework for Short Video Platform](https://mlsanity.com/paper/2504.14904/) (2025)
- [SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence](https://mlsanity.com/paper/2105.15079/) (2021)
- [Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data](https://mlsanity.com/paper/2206.00524/) (2022)
- [Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art](https://mlsanity.com/paper/2308.15745/) (2023)
- [Role of Artificial Intelligence in Detection of Hateful Speech for Hinglish Data on Social Media](https://mlsanity.com/paper/2105.04913/) (2021)
- [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024)
- [Hate Speech Detection and Classification in Amharic Text with Deep Learning](https://mlsanity.com/paper/2408.03849/) (2024)

## Abstract (arXiv)

In this paper, we describe our system which participates in the shared task of Hate Speech Detection on Social Networks of VLSP 2019 evaluation campaign. We are provided with the pre-labeled dataset and an unlabeled dataset for social media comments or posts. Our mission is to pre-process and build machine learning models to classify comments/posts. In this report, we use Bidirectional Long Short-Term Memory to build the model that can predict labels for social media text according to Clean, Offensive, Hate. With this system, we achieve comparative results with 71.43% on the public standard test set of VLSP 2019.