@inproceedings{wang-etal-2024-esdm,
    title = "{ESDM}: Early {S}ensing Depression Model in Social Media Streams",
    author = "Wang, Bichen  and
      Zi, Yuzhe  and
      Zhao, Yanyan  and
      Deng, Pengfei  and
      Qin, Bing",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.556",
    pages = "6288--6298",
    abstract = "Depression impacts millions worldwide, with increasing efforts to use social media data for early detection and intervention. Traditional Risk Detection (TRD) uses a user{'}s complete posting history for predictions, while Early Risk Detection (ERD) seeks early detection in a user{'}s posting history, emphasizing the importance of prediction earliness. However, ERD remains relatively underexplored due to challenges in balancing accuracy and earliness, especially with evolving partial data. To address this, we introduce the Early Sensing Depression Model (ESDM), which comprises two modules classification with partial information module (CPI) and decision for classification moment module (DMC), alongside an early detection loss function. Experiments show ESDM outperforms benchmarks in both earliness and accuracy.",
}

Depression impacts millions worldwide, with increasing efforts to use social media data for early detection and intervention. Traditional Risk Detection (TRD) uses a user{'}s complete posting history for predictions, while Early Risk Detection (ERD) seeks early detection in a user{'}s posting history, emphasizing the importance of prediction earliness. However, ERD remains relatively underexplored due to challenges in balancing accuracy and earliness, especially with evolving partial data. To address this, we introduce the Early Sensing Depression Model (ESDM), which comprises two modules classification with partial information module (CPI) and decision for classification moment module (DMC), alongside an early detection loss function. Experiments show ESDM outperforms benchmarks in both earliness and accuracy.
Wang, Bichen Zi, Yuzhe Zhao, Yanyan Deng, Pengfei Qin, Bing
ESDM: Early Sensing Depression Model in Social Media Streams