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Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

BACKGROUND: Eating disorders affect an increasing number of people. Social networks provide information that can help. OBJECTIVE: We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. METHODS: We collected tweets related to eating disorder...

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Autores principales: Benítez-Andrades, José Alberto, Alija-Pérez, José-Manuel, Vidal, Maria-Esther, Pastor-Vargas, Rafael, García-Ordás, María Teresa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914746/
https://www.ncbi.nlm.nih.gov/pubmed/35200156
http://dx.doi.org/10.2196/34492
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author Benítez-Andrades, José Alberto
Alija-Pérez, José-Manuel
Vidal, Maria-Esther
Pastor-Vargas, Rafael
García-Ordás, María Teresa
author_facet Benítez-Andrades, José Alberto
Alija-Pérez, José-Manuel
Vidal, Maria-Esther
Pastor-Vargas, Rafael
García-Ordás, María Teresa
author_sort Benítez-Andrades, José Alberto
collection PubMed
description BACKGROUND: Eating disorders affect an increasing number of people. Social networks provide information that can help. OBJECTIVE: We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. METHODS: We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. RESULTS: A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer–based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%). CONCLUSIONS: Bidirectional encoder representations from transformer–based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder–related tweets.
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spelling pubmed-89147462022-03-12 Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study Benítez-Andrades, José Alberto Alija-Pérez, José-Manuel Vidal, Maria-Esther Pastor-Vargas, Rafael García-Ordás, María Teresa JMIR Med Inform Original Paper BACKGROUND: Eating disorders affect an increasing number of people. Social networks provide information that can help. OBJECTIVE: We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. METHODS: We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. RESULTS: A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer–based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%). CONCLUSIONS: Bidirectional encoder representations from transformer–based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder–related tweets. JMIR Publications 2022-02-24 /pmc/articles/PMC8914746/ /pubmed/35200156 http://dx.doi.org/10.2196/34492 Text en ©José Alberto Benítez-Andrades, José-Manuel Alija-Pérez, Maria-Esther Vidal, Rafael Pastor-Vargas, María Teresa García-Ordás. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 24.02.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Benítez-Andrades, José Alberto
Alija-Pérez, José-Manuel
Vidal, Maria-Esther
Pastor-Vargas, Rafael
García-Ordás, María Teresa
Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study
title Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study
title_full Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study
title_fullStr Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study
title_full_unstemmed Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study
title_short Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study
title_sort traditional machine learning models and bidirectional encoder representations from transformer (bert)–based automatic classification of tweets about eating disorders: algorithm development and validation study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914746/
https://www.ncbi.nlm.nih.gov/pubmed/35200156
http://dx.doi.org/10.2196/34492
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