How Successful Is Transfer Learning for Detecting Anorexia on Social Media?

Abstract

Anorexia is a mental disorder that involves serious abnormalities in nutritional intake behavior. This behavior leads to significant weight loss, which can lead to severe malnutrition. Specifically, eating disorders exhibit the highest mortality rate of any mental illness. Early identification of anorexia, along with appropriate treatment, improves the speed of recovery in patients. Presently there is a strong and consistent association between social media use and eating concerns. Natural Language Processing, a branch of artificial intelligence, has the potential to contribute towards early anorexia detection in textual data. Currently, there is still a long way to go in the identification of anorexia on social media due to the low number of texts available and in fact, most of these are focused on the treatment of English texts. The main contribution of this paper is the application of transfer learning techniques using Transformer-based models for detecting anorexia in tweets written in Spanish. In particular, we compare the performance between already available multilingual and monolingual models, and we conduct an error analysis to understand the capabilities of these models for Spanish.

Keywords

• anorexia detection
• transfer learning
• BERT
• text classification
• Natural Language Processing