A comparison of text representation approaches for early detection of anorexia

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 eatin...

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Detalles Bibliográficos
Autores principales:	Villegas, María Paula, Errecalde, Marcelo Luis, Cagnina, Leticia
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2021
Materias:	Ciencias Informáticas Early Risk Detection Anorexia Detection Learned Text Representations Temporal Variation of Terms
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/130347
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-130347
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institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas Early Risk Detection Anorexia Detection Learned Text Representations Temporal Variation of Terms
spellingShingle	Ciencias Informáticas Early Risk Detection Anorexia Detection Learned Text Representations Temporal Variation of Terms Villegas, María Paula Errecalde, Marcelo Luis Cagnina, Leticia A comparison of text representation approaches for early detection of anorexia
topic_facet	Ciencias Informáticas Early Risk Detection Anorexia Detection Learned Text Representations Temporal Variation of Terms
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.
format	Objeto de conferencia Objeto de conferencia
author	Villegas, María Paula Errecalde, Marcelo Luis Cagnina, Leticia
author_facet	Villegas, María Paula Errecalde, Marcelo Luis Cagnina, Leticia
author_sort	Villegas, María Paula
title	A comparison of text representation approaches for early detection of anorexia
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	comparison of text representation approaches for early detection of anorexia
publishDate	2021
url	http://sedici.unlp.edu.ar/handle/10915/130347
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A comparison of text representation approaches for early detection of anorexia

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