XBert - A Model for Hate Speech Detection in Vietnamese Text

Abstract

— In the digital age, social media's pervasive influence has inadvertently escalated the prevalence of hate speech and offensive comments, with alarming implications for mental health. There is increasing evidence indicating a clear correlation between two factors. exposure to such toxic online content and the onset of depression among users, particularly affecting vulnerable groups like content creators and channel owners. Addressing this critical issue, our research introduces XBert, a model for detecting hostile and provocative language in Vietnamese. We propose an approach related to data preprocessing, improved tokenization, and model fine-tuning. We have modified the architecture of the Roberta model, used the EDA technique, and added a dropout parameter to the tokenizer. Our model achieved an accuracy of 99.75% and an F1-Macro score of 98.05%. This is a promising result for a model detecting provocative and hostile language in Vietnamese.