---
_id: '45270'
abstract:
- lang: eng
  text: Clinical depression is a serious mental disorder that poses challenges for
    both personal and public health. Millions of people struggle with depression each
    year, but for many, the disorder goes undiagnosed or untreated. Over the last
    decade, early depression detection on social media emerged as an interdisciplinary
    research field. However, there is still a gap in detecting hesitant, depression-susceptible
    individuals with minimal direct depressive signals at an early stage. We, therefore,
    take up this open point and leverage posts from Reddit to fill the addressed gap.
    Our results demonstrate the potential of contemporary Transformer architectures
    in yielding promising predictive capabilities for mental health research. Furthermore,
    we investigate the model’s interpretability using a surrogate and a topic modeling
    approach. Based on our findings, we consider this work as a further step towards
    developing a better understanding of mental eHealth and hope that our results
    can support the development of future technologies.
author:
- first_name: Haya
  full_name: Halimeh, Haya
  id: '87673'
  last_name: Halimeh
- first_name: Matthew
  full_name: Caron, Matthew
  id: '60721'
  last_name: Caron
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Halimeh H, Caron M, Müller O. Early Depression Detection with Transformer
    Models: Analyzing the Relationship between Linguistic and Psychology-Based Features.
    In: <i>Hawaii International Conference on System Sciences</i>. ; 2023.'
  apa: 'Halimeh, H., Caron, M., &#38; Müller, O. (2023). Early Depression Detection
    with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based
    Features. <i>Hawaii International Conference on System Sciences</i>. Hawaii International
    Conference on System Sciences.'
  bibtex: '@inproceedings{Halimeh_Caron_Müller_2023, title={Early Depression Detection
    with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based
    Features}, booktitle={Hawaii International Conference on System Sciences}, author={Halimeh,
    Haya and Caron, Matthew and Müller, Oliver}, year={2023} }'
  chicago: 'Halimeh, Haya, Matthew Caron, and Oliver Müller. “Early Depression Detection
    with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based
    Features.” In <i>Hawaii International Conference on System Sciences</i>, 2023.'
  ieee: 'H. Halimeh, M. Caron, and O. Müller, “Early Depression Detection with Transformer
    Models: Analyzing the Relationship between Linguistic and Psychology-Based Features,”
    presented at the Hawaii International Conference on System Sciences, 2023.'
  mla: 'Halimeh, Haya, et al. “Early Depression Detection with Transformer Models:
    Analyzing the Relationship between Linguistic and Psychology-Based Features.”
    <i>Hawaii International Conference on System Sciences</i>, 2023.'
  short: 'H. Halimeh, M. Caron, O. Müller, in: Hawaii International Conference on
    System Sciences, 2023.'
conference:
  end_date: 2023-01-06
  name: Hawaii International Conference on System Sciences
  start_date: 2023-01-03
date_created: 2023-05-25T10:25:21Z
date_updated: 2024-01-10T15:16:37Z
department:
- _id: '195'
- _id: '196'
keyword:
- Social Media and Healthcare Technology
- early depression detection
- liwc
- mental health
- transfer learning
- transformer architectures
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://scholarspace.manoa.hawaii.edu/items/2ddab486-5d2f-4302-8de3-a8b24017da3d
oa: '1'
publication: Hawaii International Conference on System Sciences
publication_status: published
related_material:
  link:
  - relation: confirmation
    url: https://hdl.handle.net/10125/103046
status: public
title: 'Early Depression Detection with Transformer Models: Analyzing the Relationship
  between Linguistic and Psychology-Based Features'
type: conference
user_id: '60721'
year: '2023'
...