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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/130347
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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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eng |
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eng |
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