Feb. 9, 2024, 5:43 a.m. | Jos\'e Alberto Ben\'itez-Andrades Jos\'e-Manuel Alija-P\'erez Maria-Esther Vidal Rafael Pastor-Vargas Mar\'ia Teresa G

cs.LG updates on arXiv.org arxiv.org

Background: Eating disorders are increasingly prevalent, and social networks offer valuable information.
Objective: Our goal was to identify efficient machine learning models for categorizing tweets related to eating disorders.
Methods: Over three months, we collected tweets about eating disorders. A 2,000-tweet subset was labeled for: (1) being written by individuals with eating disorders, (2) promoting eating disorders, (3) informativeness, and (4) scientific content. Both traditional machine learning and deep learning models were employed for classification, assessing accuracy, F1 score, and …

algorithm bert classification cs.cl cs.lg development eating disorders encoder identify information machine machine learning machine learning models networks social social networks study traditional machine learning transformer tweets validation

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