Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese
This addresses the need to filter harmful and uninformative comments in Vietnamese online forums, though it is incremental as it applies existing methods to a new language-specific dataset.
The paper tackles the problem of detecting constructive and toxic speech in Vietnamese social media comments by creating a dataset of 10,000 human-annotated comments and proposing a system using PhoBERT, achieving F1-scores of 78.59% for constructive and 59.40% for toxic classification.
The rise of social media has led to the increasing of comments on online forums. However, there still exists invalid comments which are not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we obtain F1-scores of 78.59% and 59.40% for classifying constructive and toxic comments, respectively. Besides, we implement various baseline models as traditional Machine Learning and Deep Neural Network-Based models to evaluate the dataset. With the results, we can solve several tasks on the online discussions and develop the framework for identifying constructiveness and toxicity of Vietnamese social media comments automatically.