Integrating clinical anxiety scales with pre-trained language models for anxiety recognition on social media
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel framework for recognizing anxiety from social media using language models and clinical scales. The method enhances early anxiety detection by analyzing semantic similarities in user posts.
Area Of Science
- Computational Linguistics
- Psychology
- Digital Health
Background
- Social media analysis for mental health is growing, but lacks deep semantic understanding and clinical integration.
- Current models for anxiety recognition from social media often lack interpretability and medical relevance due to limited domain knowledge.
Purpose Of The Study
- To develop an advanced framework for anxiety recognition from social media by integrating pre-trained language models with clinical anxiety scales.
- To improve user-level semantic modeling and anxiety feature extraction for more accurate mental health monitoring.
- To enhance the interpretability and medical relevance of anxiety recognition models.
Main Methods
- Utilized pre-trained language models to extract semantic representations from social media posts and clinical anxiety scales.
- Calculated anxiety scores based on semantic similarity between user posts and anxiety scales.
- Implemented a joint training strategy involving sequence and text classification for robust anxiety recognition.
- Constructed a large-scale dataset (DAUR) and a subset (DAUR_PRE) for early anxiety detection.
Main Results
- The proposed framework demonstrated superior performance compared to existing methods on both the DAUR and DAUR_PRE datasets.
- Achieved high classification accuracy, particularly in the challenging scenario of early anxiety recognition on DAUR_PRE.
- The integration of clinical scales improved the model's ability to identify nuanced anxiety states.
Conclusions
- The novel framework effectively identifies anxiety states from social media by combining deep semantic modeling with clinical insights.
- The approach shows significant promise for early anxiety detection and personalized mental health monitoring.
- Future research can build upon this framework to develop more sophisticated and clinically validated digital mental health tools.
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