Representation Learning for Identifying Depression Causes in Social Media

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KiML_2023_paper_4.pdf (1.35 MB)

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https://aiisc.ai/kiml2023/accepted.html

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http://hdl.handle.net/11603/30144

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UMBC Computer Science and Electrical Engineering Department
UMBC Student Collection

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Date

2023

Type of Work

conference papers and proceedings
preprints
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CC BY 4.0 DEED Attribution 4.0 International

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Abstract

Social media provides a supportive and anonymous environment for discussing mental health issues, including depression. Existing research on identifying the cause of depression focuses primarily on improving classifier models, while neglecting the importance of learning better data representations. To address this gap, we introduce an architecture that enhances the identification of the cause of depression by learning improved data representations. Our work enables a deeper interpretation of the cause of depression in social media contexts, emphasizing the significance of effective representation learning for this task. Our work can act as a foundation for self-help applications in the field of mental health.