Early Detection of Eating Disorders using Social Media
Blanca Tébar and Anandha Gopalan
Abstract:
Eating disorders (EDs) are the mental illnesses with
the highest mortality rate, resulting in around 10,200 annual
deaths worldwide. However, with early treatment, the probability
of dying from an eating disorder can be reduced from 19-20%
to 2-3%. Many techniques have been developed to leverage
social media to detect whether an individual is suffering or
not from this mental health condition. However, many of them
are subject to the number of posts uploaded by the user being
analysed rather than taking other factors into account. In this
work, we present a novel approach for the early detection of
eating disorders that maximises the amount of information that
can be extracted from a single post. We use a set of features
characterising eating disorders related content, the embedding
of the texts and the personal information contained in each post
to train a feature fusion model able to identify all test sample
users at risk, achieving a 100% recall and 0.97% F1 score for
the positive class.