Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese

The rise of social media has led to the increasing of comments on online forums. However, there still exists some invalid comments which were not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for classifying 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 proposed 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 achieved 78.59% and 59.40% F1-score for identifying constructive and toxic comments separately. Besides, to have an objective assessment for the dataset, we implement a variety of baseline models as traditional Machine Learning and Deep Neural Network-Based models. With the results, we can solve some problems on the online discussions and develop the framework for identifying constructiveness and toxicity Vietnamese social media comments automatically.