Early risk detection of anorexia on social media

Ramíırez-Cifuentes, Diana; Mayans Yern, Marc; Freire, Ana

doi:http://dx.doi.org/10.1007/978-3-030-01437-7_1

Early risk detection of anorexia on social media

Citació

Ramírez-Cifuentes D, Mayans M, Freire A. Early risk detection of anorexia on social media. In: Bodrunova S, editor. Internet Science. 5th International Conference, INSCI 2018, Proceedings; 2018 Oct 24-26; St. Petersburg, Russia. Cham: Springer; 2018. p. 3-14. (LNCS; no. 11193. LNISA; no. 11193). DOI: 10.1007/978-3-030-01437-7_1

Resum

This paper proposes an approach for the early detection of anorexia nervosa (AN) on social media. We present a machine learning approach that processes the texts written by social media users. This method relies on a set of features based on domain-specific vocabulary, topics, psychological processes, and linguistic information extracted from the users’ writings. This approach penalizes the delay in detecting positive cases in order to classify the users in risk as early as possible. Identifying anorexia early, along with an appropriate treatment, improves the speed of recovery and the likelihood of staying free of the illness. The results of this work showed that our proposal is suitable for the early detection of AN symptoms.