RT info:eu-repo/semantics/article
T1 Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study
A1 Benítez Andrades, José Alberto
A1 Alija Pérez, José Manuel
A1 Vidal, María Esther
A1 Pastor Vargas, Rafael
A1 García Ordás, María Teresa
A2 Ingenieria de Sistemas y Automatica
K1 Informática
K1 Ingeniería de sistemas
K1 natural language processing
K1 eating disorders
K1 bert
K1 transfer learning
K1 3301 Ingeniería y Tecnología Aeronáuticas
AB [EN] 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.
PB JMIR
LK https://hdl.handle.net/10612/18221
UL https://hdl.handle.net/10612/18221
NO Benítez-Andrades, J. A., Alija-Pérez, J.-M., Vidal, M.-E., Pastor-Vargas, R., & García-Ordás, M. T. (2022). Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study. JMIR Medical Informatics, 10(2), e34492. https://doi.org/10.2196/34492
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 20-may-2024