Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1784667807136350208 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8914746 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT benitezandradesjosealberto traditionalmachinelearningmodelsandbidirectionalencoderrepresentationsfromtransformerbertbasedautomaticclassificationoftweetsabouteatingdisordersalgorithmdevelopmentandvalidationstudy AT alijaperezjosemanuel traditionalmachinelearningmodelsandbidirectionalencoderrepresentationsfromtransformerbertbasedautomaticclassificationoftweetsabouteatingdisordersalgorithmdevelopmentandvalidationstudy AT vidalmariaesther traditionalmachinelearningmodelsandbidirectionalencoderrepresentationsfromtransformerbertbasedautomaticclassificationoftweetsabouteatingdisordersalgorithmdevelopmentandvalidationstudy AT pastorvargasrafael traditionalmachinelearningmodelsandbidirectionalencoderrepresentationsfromtransformerbertbasedautomaticclassificationoftweetsabouteatingdisordersalgorithmdevelopmentandvalidationstudy AT garciaordasmariateresa traditionalmachinelearningmodelsandbidirectionalencoderrepresentationsfromtransformerbertbasedautomaticclassificationoftweetsabouteatingdisordersalgorithmdevelopmentandvalidationstudy |