A comparison of text representation approaches for early detection of anorexia

Autores
Villegas, María Paula; Errecalde, Marcelo Luis; Cagnina, Leticia
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The excessive use of filters on photos, along with the hundreds of profiles on social networks that abuse retouching to reduce centimeters of their bodies, and the existing social pressure on body image, has caused an increase in Eating Disorders (ED). Thus, anorexia, bulimia nervosa and binge eating disorder are the main eating disorders that put physical and mental health at risk, especially among the very young people. Fortunately, there are technologies that allow the early detection of certain problems in different areas, in particular those related to safety and health, such as the mentioned above. Our main objective in this work is to analyze how different representations of texts behave for the early detection of people suffering from anorexia. Although we focus on ED, we believe that these results could be extended to other risks such as depression, gambling, etc. We employ k-TVT, an efficient and effective method used previously in the detection of signs of depression, as well as other more elaborated approaches such as Word2Vec, GloVe, and BERT. To compare the performance of these methods, we worked on a data collection provided by the eRisk 2018 laboratory to detect signs of anorexia disorder. Regarding the results, the performance of the different approaches was quite similar, with k-TVT and BERT being slightly better. We also conclude that k-TVT continues to be efficient with its flexibility, low dimension and easy computing being the more attractive characteristics.
Workshop: WBDMD - Base de Datos y Minería de Datos
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Early Risk Detection
Anorexia Detection
Learned Text Representations
Temporal Variation of Terms
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/130347

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network_name_str SEDICI (UNLP)
spelling A comparison of text representation approaches for early detection of anorexiaVillegas, María PaulaErrecalde, Marcelo LuisCagnina, LeticiaCiencias InformáticasEarly Risk DetectionAnorexia DetectionLearned Text RepresentationsTemporal Variation of TermsThe excessive use of filters on photos, along with the hundreds of profiles on social networks that abuse retouching to reduce centimeters of their bodies, and the existing social pressure on body image, has caused an increase in Eating Disorders (ED). Thus, anorexia, bulimia nervosa and binge eating disorder are the main eating disorders that put physical and mental health at risk, especially among the very young people. Fortunately, there are technologies that allow the early detection of certain problems in different areas, in particular those related to safety and health, such as the mentioned above. Our main objective in this work is to analyze how different representations of texts behave for the early detection of people suffering from anorexia. Although we focus on ED, we believe that these results could be extended to other risks such as depression, gambling, etc. We employ k-TVT, an efficient and effective method used previously in the detection of signs of depression, as well as other more elaborated approaches such as Word2Vec, GloVe, and BERT. To compare the performance of these methods, we worked on a data collection provided by the eRisk 2018 laboratory to detect signs of anorexia disorder. Regarding the results, the performance of the different approaches was quite similar, with k-TVT and BERT being slightly better. We also conclude that k-TVT continues to be efficient with its flexibility, low dimension and easy computing being the more attractive characteristics.Workshop: WBDMD - Base de Datos y Minería de DatosRed de Universidades con Carreras en Informática2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf301-310http://sedici.unlp.edu.ar/handle/10915/130347enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-633-574-4info:eu-repo/semantics/reference/hdl/10915/129809info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:32:47Zoai:sedici.unlp.edu.ar:10915/130347Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:32:47.956SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A comparison of text representation approaches for early detection of anorexia
title A comparison of text representation approaches for early detection of anorexia
spellingShingle A comparison of text representation approaches for early detection of anorexia
Villegas, María Paula
Ciencias Informáticas
Early Risk Detection
Anorexia Detection
Learned Text Representations
Temporal Variation of Terms
title_short A comparison of text representation approaches for early detection of anorexia
title_full A comparison of text representation approaches for early detection of anorexia
title_fullStr A comparison of text representation approaches for early detection of anorexia
title_full_unstemmed A comparison of text representation approaches for early detection of anorexia
title_sort A comparison of text representation approaches for early detection of anorexia
dc.creator.none.fl_str_mv Villegas, María Paula
Errecalde, Marcelo Luis
Cagnina, Leticia
author Villegas, María Paula
author_facet Villegas, María Paula
Errecalde, Marcelo Luis
Cagnina, Leticia
author_role author
author2 Errecalde, Marcelo Luis
Cagnina, Leticia
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Early Risk Detection
Anorexia Detection
Learned Text Representations
Temporal Variation of Terms
topic Ciencias Informáticas
Early Risk Detection
Anorexia Detection
Learned Text Representations
Temporal Variation of Terms
dc.description.none.fl_txt_mv The excessive use of filters on photos, along with the hundreds of profiles on social networks that abuse retouching to reduce centimeters of their bodies, and the existing social pressure on body image, has caused an increase in Eating Disorders (ED). Thus, anorexia, bulimia nervosa and binge eating disorder are the main eating disorders that put physical and mental health at risk, especially among the very young people. Fortunately, there are technologies that allow the early detection of certain problems in different areas, in particular those related to safety and health, such as the mentioned above. Our main objective in this work is to analyze how different representations of texts behave for the early detection of people suffering from anorexia. Although we focus on ED, we believe that these results could be extended to other risks such as depression, gambling, etc. We employ k-TVT, an efficient and effective method used previously in the detection of signs of depression, as well as other more elaborated approaches such as Word2Vec, GloVe, and BERT. To compare the performance of these methods, we worked on a data collection provided by the eRisk 2018 laboratory to detect signs of anorexia disorder. Regarding the results, the performance of the different approaches was quite similar, with k-TVT and BERT being slightly better. We also conclude that k-TVT continues to be efficient with its flexibility, low dimension and easy computing being the more attractive characteristics.
Workshop: WBDMD - Base de Datos y Minería de Datos
Red de Universidades con Carreras en Informática
description The excessive use of filters on photos, along with the hundreds of profiles on social networks that abuse retouching to reduce centimeters of their bodies, and the existing social pressure on body image, has caused an increase in Eating Disorders (ED). Thus, anorexia, bulimia nervosa and binge eating disorder are the main eating disorders that put physical and mental health at risk, especially among the very young people. Fortunately, there are technologies that allow the early detection of certain problems in different areas, in particular those related to safety and health, such as the mentioned above. Our main objective in this work is to analyze how different representations of texts behave for the early detection of people suffering from anorexia. Although we focus on ED, we believe that these results could be extended to other risks such as depression, gambling, etc. We employ k-TVT, an efficient and effective method used previously in the detection of signs of depression, as well as other more elaborated approaches such as Word2Vec, GloVe, and BERT. To compare the performance of these methods, we worked on a data collection provided by the eRisk 2018 laboratory to detect signs of anorexia disorder. Regarding the results, the performance of the different approaches was quite similar, with k-TVT and BERT being slightly better. We also conclude that k-TVT continues to be efficient with its flexibility, low dimension and easy computing being the more attractive characteristics.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
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