A Stance Detection Model Based on Sentiment Analysis and Toxic Language Detection

In this paper, we present a stance detection model grounded in multi-task learning, specifically designed to address the intricate challenge of text stance analysis within social media comments. This model is structured with an embedding network, an encoder module, a sophisticated multi-task attention mechanism, an ensemble module, and a classification output layer. To augment the performance of stance detection, we employed sentiment analysis and toxicity language detection as auxiliary tasks. The sentiment analysis plays a pivotal role in enabling the model to capture the public opinion inclinations of both individual and collective users. By delving into these inclinations, our model can extract fine-grained stance elements, offering a more nuanced understanding of users’ positions. On the other hand, toxicity language detection aids in modeling the extreme tendencies of social media users towards specific events. It identifies manifestations of hatred, offensiveness, discrimination, and insult, thereby allowing the model to reconstruct users’ genuine stance information from these extreme expressions. Through the synergy of multi-task joint learning, the accuracy and reliability of the stance detection were significantly improved. To validate the efficacy of our proposed model, we selected two hot events as representative cases, one from the Chinese Weibo platform and the other from the English Twitter platform. A series of comprehensive tasks, including developing crawler programs, collecting data, performing data preprocessing, and conducting data annotation, were systematically executed. Subsequently, we applied our model to detect the stances within the comments related to these two events, categorizing them into three classes: support, opposition, and ambiguity. The experimental results demonstrate that our stance detection model, which integrates sentiment analysis and toxicity language detection, substantially improves the detection accuracy, outperforming traditional methods.