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    This study introduces a novel depression detection model that integrates cognitive distortions and user-level data from social media. The model achieves state-of-the-art results by analyzing text for cognitive distortions and user behavior patterns.

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    Area of Science:

    • Computational Social Science
    • Mental Health Informatics
    • Artificial Intelligence

    Background:

    • Depression is a prevalent mental illness significantly impairing daily functioning.
    • Social media data analysis is increasingly used for depression detection.
    • Existing methods often overlook cognitive distortions and comprehensive user-level information.

    Purpose of the Study:

    • To develop an advanced depression detection model incorporating cognitive distortions and user-level data.
    • To address limitations in current social media-based depression detection approaches.
    • To improve the accuracy and comprehensiveness of identifying depression through digital footprints.

    Main Methods:

    • A novel model fusing cognitive distortions and user-level information was developed.
    • A cognitive distortion encoder and post-level distortion aware mechanism were employed.
    • Multi-feature adaptive weighting identified key information, reducing data noise.

    Main Results:

    • The proposed model achieved state-of-the-art performance on a public dataset.
    • Experiments demonstrated the effectiveness of integrating cognitive distortions.
    • The study highlighted the importance of various user-level information in depression detection.

    Conclusions:

    • Fusing cognitive distortions and user-level information significantly enhances depression detection accuracy.
    • The developed model offers a more holistic approach to identifying depression via social media.
    • Further research can explore specific cognitive distortion learning strategies and user-level data impacts.