Enhanced word embeddings for anorexia nervosa detection on social media

Abstract

Anorexia Nervosa (AN) is a serious mental disorder that has been proved to be traceable on social media through the analysis of users’ written posts. Here we present an approach to generate word embeddings enhanced for a classification task dedicated to the detection of Reddit users with AN. Our method extends Word2vec’s objective function in order to put closer domain-specific and semantically related words. The approach is evaluated through the calculation of an average similarity measure, and via the usage of the embeddings generated as features for the AN screening task. The results show that our method outperforms the usage of fine-tuned pre-learned word embeddings, related methods dedicated to generate domain adapted embeddings, as well as representations learned on the training set using Word2vec. This method can potentially be applied and evaluated on similar tasks that can be formalized as document categorization problems. Regarding our use case, we believe that this approach can contribute to the development of proper automated detection tools to alert and assist clinicians.