---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Haya
      foaf_name: Halimeh, Haya
      foaf_surname: Halimeh
      foaf_workInfoHomepage: http://www.librecat.org/personId=87673
  - foaf_Person:
      foaf_givenName: Matthew
      foaf_name: Caron, Matthew
      foaf_surname: Caron
      foaf_workInfoHomepage: http://www.librecat.org/personId=60721
  - foaf_Person:
      foaf_givenName: Oliver
      foaf_name: Müller, Oliver
      foaf_surname: Müller
      foaf_workInfoHomepage: http://www.librecat.org/personId=72849
  dct_date: 2023^xs_gYear
  dct_language: eng
  dct_subject:
  - Social Media and Healthcare Technology
  - early depression detection
  - liwc
  - mental health
  - transfer learning
  - transformer architectures
  dct_title: 'Early Depression Detection with Transformer Models: Analyzing the Relationship
    between Linguistic and Psychology-Based Features@'
...