Towards Measuring the Severity of Depression in Social Media via Text Classification

Psychologists have used tests or carefully designed survey questions, such as Beck’s Depression Inventory (BDI), to identify the presence of depression and to assess its severity level. On the other hand, methods for automatic depression detection have gained increasing interest since all the inform...

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Detalles Bibliográficos
Autores principales: Burdisso, Sergio, Errecalde, Marcelo Luis, Montes y Gómez, Manuel
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2019
Materias:
Ciencias Informáticas
Text Classification
Depression Level Estimation
Beck’s Depression Inventory
SS3
CLEF eRisk 2019
Reddit
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/91042
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-91042
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Text Classification
Depression Level Estimation
Beck’s Depression Inventory
SS3
CLEF eRisk 2019
Reddit
spellingShingle Ciencias Informáticas
Text Classification
Depression Level Estimation
Beck’s Depression Inventory
SS3
CLEF eRisk 2019
Reddit
Burdisso, Sergio
Errecalde, Marcelo Luis
Montes y Gómez, Manuel
Towards Measuring the Severity of Depression in Social Media via Text Classification
topic_facet Ciencias Informáticas
Text Classification
Depression Level Estimation
Beck’s Depression Inventory
SS3
CLEF eRisk 2019
Reddit
description Psychologists have used tests or carefully designed survey questions, such as Beck’s Depression Inventory (BDI), to identify the presence of depression and to assess its severity level. On the other hand, methods for automatic depression detection have gained increasing interest since all the information available in social media, such as Twitter and Facebook, enables novel measurement based on language use. These methods learn to characterize depression through natural language use and have shown that, in fact, language usage can provide strong evidence in detecting depressive people. However, not much attention has been paid to measuring finer grain relationships between both aspects, such as how is connected the language usage with the severity level of depression. The present study is a first step towards that direction. First, we train a binary text classifier to detect “depressed” users and then we use its confidence values to estimate the user’s clinical depression level. In order to do that, our system has to fill the standard BDI depression questionnaire on users’ behalf, based only on the text of users’ postings. Our proposal, publicly tested in the eRisk 2019 T3 task, obtained promising results. This offers very interesting evidence of the potential of our method to estimate the level of depression directly form user’s posts in social media.
format Objeto de conferencia
Objeto de conferencia
author Burdisso, Sergio
Errecalde, Marcelo Luis
Montes y Gómez, Manuel
author_facet Burdisso, Sergio
Errecalde, Marcelo Luis
Montes y Gómez, Manuel
author_sort Burdisso, Sergio
title Towards Measuring the Severity of Depression in Social Media via Text Classification
title_short Towards Measuring the Severity of Depression in Social Media via Text Classification
title_full Towards Measuring the Severity of Depression in Social Media via Text Classification
title_fullStr Towards Measuring the Severity of Depression in Social Media via Text Classification
title_full_unstemmed Towards Measuring the Severity of Depression in Social Media via Text Classification
title_sort towards measuring the severity of depression in social media via text classification
publishDate 2019
url http://sedici.unlp.edu.ar/handle/10915/91042
work_keys_str_mv AT burdissosergio towardsmeasuringtheseverityofdepressioninsocialmediaviatextclassification
AT errecaldemarceloluis towardsmeasuringtheseverityofdepressioninsocialmediaviatextclassification
AT montesygomezmanuel towardsmeasuringtheseverityofdepressioninsocialmediaviatextclassification
bdutipo_str Repositorios
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