Identifying Fine-grained Depression Signs in Social Media Posts
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A38JVXFKP" target="_blank" >RIV/00216208:11320/25:38JVXFKP - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195994028&partnerID=40&md5=b499806bfb53c5e843ad0e3465ad157d" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195994028&partnerID=40&md5=b499806bfb53c5e843ad0e3465ad157d</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Identifying Fine-grained Depression Signs in Social Media Posts
Original language description
Natural Language Processing has already proven to be an effective tool for helping in the identification of mental health disorders in text. However, most studies limit themselves to a binary classification setup or base their label set on pre-established resources. By doing so, they don't explicitly model many common ways users can express their depression online, limiting our understanding of what kind of depression signs such models can accurately classify. This study evaluates how machine learning techniques deal with the classification of a fine-grained set of 21 depression signs in social media posts from Brazilian undergraduate students. We found out that model performance is not necessarily driven by a depression sign's frequency on social media posts, since evaluated machine learning techniques struggle to classify the majority of signs of depression typically present in posts. Thus, model performance seems to be more related to the inherent difficulty of identifying a given sign than with its frequency. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Jt. Int. Conf. Comput. Linguist., Lang. Resour. Eval., LREC-COLING - Main Conf. Proc.
ISBN
978-249381410-4
ISSN
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e-ISSN
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Number of pages
11
Pages from-to
8594-8604
Publisher name
European Language Resources Association (ELRA)
Place of publication
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Event location
Torino, Italia
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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