Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features

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dc.contributor.authorHalimeh, Haya
dc.contributor.authorCaron, Matthew
dc.contributor.authorMüller, Oliver
dc.date.accessioned2022-12-27T19:08:11Z
dc.date.available2022-12-27T19:08:11Z
dc.date.issued2023-01-03
dc.description.abstractClinical 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.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.415
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.urihttps://hdl.handle.net/10125/103046
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSocial Media and Healthcare Technology
dc.subjectearly depression detection
dc.subjectliwc
dc.subjectmental health
dc.subjecttransfer learning
dc.subjecttransformer architectures
dc.titleEarly Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features
dc.type.dcmitext
prism.startingpage3377

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