A Dataset for Research on Depression in Social Media

Abstract

Language provides a unique window into thoughts, enabling direct assessment of mental-state alterations. Due to their increasing popularity, online social media platforms have become promising means to study different mental disorders. However, the lack of available datasets can hinder the development of innovative diagnostic methods. Tools to assist health practitioners in screening and monitoring individuals under potential risk are essential. In this paper, we present a new a dataset to foster the research on automatic detection of depression. To this end, we present a methodology for automatically collecting large samples of depression and non-depression posts from online social media. Furthermore, we perform a benchmark on the dataset to establish a point of reference for researchers who are interested in using it.