@inproceedings{sekulic-strube-2019-adapting,
    title = "Adapting Deep Learning Methods for Mental Health Prediction on Social Media",
    author = "Sekulic, Ivan  and
      Strube, Michael",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/D19-5542/",
    doi = "10.18653/v1/D19-5542",
    pages = "322--327",
    abstract = "Mental health poses a significant challenge for an individual{'}s well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a challenge of detecting social media users' mental status through deep learning-based models, moving away from traditional approaches to the task. In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders. Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model{'}s word-level attention weights."
}