@inproceedings{zhang-etal-2023-sentiment,
    title = "Sentiment-guided Transformer with Severity-aware Contrastive Learning for Depression Detection on Social Media",
    author = "Zhang, Tianlin  and
      Yang, Kailai  and
      Ananiadou, Sophia",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.9",
    doi = "10.18653/v1/2023.bionlp-1.9",
    pages = "114--126",
    abstract = "Early identification of depression is beneficial to public health surveillance and disease treatment. There are many models that mainly treat the detection as a binary classification task, such as detecting whether a user is depressed. However, identifying users{'} depression severity levels from posts on social media is more clinically useful for future prevention and treatment. Existing severity detection methods mainly model the semantic information of posts while ignoring the relevant sentiment information, which can reflect the user{'}s state of mind and could be helpful for severity detection. In addition, they treat all severity levels equally, making the model difficult to distinguish between closely-labeled categories. We propose a sentiment-guided Transformer model, which efficiently fuses social media posts{'} semantic information with sentiment information. Furthermore, we also utilize a supervised severity-aware contrastive learning framework to enable the model to better distinguish between different severity levels. The experimental results show that our model achieves superior performance on two public datasets, while further analysis proves the effectiveness of all proposed modules.",
}

Early identification of depression is beneficial to public health surveillance and disease treatment. There are many models that mainly treat the detection as a binary classification task, such as detecting whether a user is depressed. However, identifying users{'} depression severity levels from posts on social media is more clinically useful for future prevention and treatment. Existing severity detection methods mainly model the semantic information of posts while ignoring the relevant sentiment information, which can reflect the user{'}s state of mind and could be helpful for severity detection. In addition, they treat all severity levels equally, making the model difficult to distinguish between closely-labeled categories. We propose a sentiment-guided Transformer model, which efficiently fuses social media posts{'} semantic information with sentiment information. Furthermore, we also utilize a supervised severity-aware contrastive learning framework to enable the model to better distinguish between different severity levels. The experimental results show that our model achieves superior performance on two public datasets, while further analysis proves the effectiveness of all proposed modules.
Zhang, Tianlin Yang, Kailai Ananiadou, Sophia
Sentiment-guided Transformer with Severity-aware Contrastive Learning for Depression Detection on Social Media