Identification of depression and PTSD among Twitter users using pre-trained language model
Abstract: Suicide is a global health issue and early diagnosis is necessary for effective treatment. Recent advancements in natural language processing has aided the identification of mental health disorders in social media. This paper investigated the efficacy of pre-trained language model (PLM) in identifying depression and post-traumatic stress disorder (PTSD) with Twitter data. Leveraging the CLPysch 2015 dataset (which constitutes of tweets from users with depression, PTSD and neither condition), we implemented various experimental designs using Long Short Term Memory (LSTM) and attention. The results demonstrate that while performance decreases for multi-nominal classification, the detection of mental health conditions improves with the implementation of attention. This study also underscores the complexity of differentiating between overlapping lexicons with multiple mental health conditions and highlights the potential of PLMs in supporting mental health diagnosis.
Paper Type: Short
Research Area: NLP Applications
Research Area Keywords: healthcare applications
Contribution Types: Model analysis & interpretability, Publicly available software and/or pre-trained models
Languages Studied: English
Submission Number: 772
Loading