ViTHSD: Exploiting Hatred by Targets for Hate Speech Detection on Vietnamese Social Media Texts

TL;DR

This paper introduces ViTHSD, a new Vietnamese hate speech dataset with target labels, and develops a baseline model combining neural networks and BERT to detect targeted hate speech in social media texts, aiming to prevent harmful online content.

Contribution

It provides the first targeted hate speech dataset for Vietnamese social media and proposes an integrated detection system for real-time moderation.

Findings

The dataset contains 10,000 labeled comments with moderate inter-annotator agreement.

The baseline model effectively combines Bi-GRU-LSTM-CNN with BERT for hate speech detection.

The proposed system can be integrated into online streaming to reduce harmful content.

Abstract

The growth of social networks makes toxic content spread rapidly. Hate speech detection is a task to help decrease the number of harmful comments. With the diversity in the hate speech created by users, it is necessary to interpret the hate speech besides detecting it. Hence, we propose a methodology to construct a system for targeted hate speech detection from online streaming texts from social media. We first introduce the ViTHSD - a targeted hate speech detection dataset for Vietnamese Social Media Texts. The dataset contains 10K comments, each comment is labeled to specific targets with three levels: clean, offensive, and hate. There are 5 targets in the dataset, and each target is labeled with the corresponding level manually by humans with strict annotation guidelines. The inter-annotator agreement obtained from the dataset is 0.45 by Cohen's Kappa index, which is indicated as a moderate level. Then, we construct a baseline for this task by combining the Bi-GRU-LSTM-CNN with the pre-trained language model to leverage the power of text representation of BERTology. Finally, we suggest a methodology to integrate the baseline model for targeted hate speech detection into the online streaming system for practical application in preventing hateful and offensive content on social media.