A Large-scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts
This addresses hate speech detection for Vietnamese social media users, but it is incremental as it applies existing methods to a new dataset.
The authors tackled hate speech detection on Vietnamese social media by introducing ViHSD, a human-annotated dataset with over 30,000 comments labeled as CLEAN, OFFENSIVE, or HATE, and evaluated it using deep learning and transformer models.
In recent years, Vietnam witnesses the mass development of social network users on different social platforms such as Facebook, Youtube, Instagram, and Tiktok. On social medias, hate speech has become a critical problem for social network users. To solve this problem, we introduce the ViHSD - a human-annotated dataset for automatically detecting hate speech on the social network. This dataset contains over 30,000 comments, each comment in the dataset has one of three labels: CLEAN, OFFENSIVE, or HATE. Besides, we introduce the data creation process for annotating and evaluating the quality of the dataset. Finally, we evaluated the dataset by deep learning models and transformer models.