BERT Model-Based Approach for Detecting Categories of Tweets in the Field of Eating Disorders (ED)

Eating disorders (ED) are among the most widespread mental illnesses in our society today. This research work presents the study of deep learning models applied to the domain of eating disorders. For this purpose, a collection of messages from the social network Twitter was compiled using web scraping techniques. After collecting a total amount of 1,085,957 tweets, a subset of 2,000 tweets was manually classified. This classification made it possible to differentiate tweets written by people who suffer or have suffered from an ED from those written by people who have not suffered from an ED. After this, 6 predictive models based on Bidirectional Encoder Representations from Transformers (BERT) were created and a comparison was made by evaluating which model scored the best. The best scoring model was RoBERTa using the pre-trained roberta-base model with an accuracy of 87.5%.