Integrating clinical anxiety scales with pre-trained language models for anxiety recognition on social media

Leveraging natural language processing to identify anxiety states from social media has been widely studied. However, existing research lacks deep user-level semantic modeling and effective anxiety feature extraction. Additionally, the absence of clinical domain knowledge in current models limits their interpretability and medical relevance. Therefore, in this paper we propose a framework for anxiety recognition that combines pre-trained language models and clinical anxiety scales. The framework first leverages pre-trained language models to extract semantic representations of social media posts and anxiety scales, respectively. Second, the anxiety score of each post is calculated by the semantic similarity between the post and the anxiety scale, and the posts with high anxiety scores are selected. Finally, a joint training strategy consisting of sequence classification (corresponding to anxiety scores) and text classification (corresponding to high anxiety score posts) is developed for anxiety recognition. To evaluate the effectiveness of the proposed framework, we construct a Dataset for Anxiety User Recognition (DAUR) which contains 3,170 users with 936,400 posts on Sina Weibo platform. In addition, to explore the capability of our method in the scenario of early anxiety recognition, we remove the posts with obvious anxiety keywords and all posts following them from the anxiety users to obtain a new dataset DAUR_PRE. Comparative experiments demonstrate that the proposed anxiety recognition framework outperforms existing methods on both datasets and achieves high classification accuracy on DAUR_PRE.