Integrating Emotional Features for Stance Detection Aimed at Social Network Security: A Multi-Task Learning Approach
Qiumei Pu, Fangli Huang, Fude Li, Jieyao Wei, Shan Jiang · Electronics · 2025
Stance detection seeks to identify the public’s position on a specific topic, providing critical insights for applications such as recommendation systems and rumor detection, which are essential for maintaining a secure social media environment. As one of China’s most influential social media platforms, Weibo significantly shapes public discourse within its complex social network structure. Despite recent advancements in stance detection research on Weibo, many studies fail to adequately address the nuanced emotional features present in text, limiting detection accuracy and effectiveness, and potentially compromising online security. This paper proposes a stance detection approach based on multi-task learning that considers the influence of emotional features to tackle these challenges. Our method utilizes a RoBERTa pre-trained model in the shared layer to extract textual features for both stance detection and sentiment analysis. In the stance detection module, a BiLSTM model captures deeper temporal information, followed by three independent modules dedicated to extracting semantic features for specific stances. Concurrently, the sentiment analysis module employs a BiLSTM model to predict emotional polarity. The experimental results on the NLPCC2016-task4 dataset demonstrate that our approach outperforms existing methods, highlighting the effectiveness of integrating sentiment analysis with stance detection to enhance both accuracy and reliability, ultimately contributing to the security of social networks.