Traditional Machine Learning Models and Bidirectional Encoder
  Representations From Transformer (BERT)-Based Automatic Classification of
  Tweets About Eating Disorders: Algorithm Development and Validation Study
José Alberto Benítez-Andrades José-Manuel Alija-Pérez Maria-Esther Vidal Rafael Pastor-Vargas María Teresa García-Ordás
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
computational time.
  Results: From 1,058,957 collected tweets, transformer-based bidirectional
encoder representations achieved the highest F1 scores (71.1%-86.4%) across all
four categories.
  Conclusions: Transformer-based models outperform traditional techniques in
classifying eating disorder-related tweets, though they require more
computational resources.