Using Twitter Social Media for Depression Detection in the Canadian Population

Depression is a serious public health problem, and an economic burden for the society. Therefore, identifying individuals with depression and providing the support for the people in need is a crucial step for creating a healthier environment. In this research, we explore the task of detecting signs of depression from tweets where people often express their feelings, thoughts, interests, and opinions. We utilize personal narratives collected users with self-reported depression to build a model suitable for predicting depression in a sample of Twitter users that is representative for the Canadian population. The training dataset contains 1,402 users who self-reported depression diagnoses during 2016. Using classical machine learning techniques, we achieved 0.961 F1-score using 10-fold cross validation. We used the CLPsych 2015 dataset as a test dataset, and we reached 0.898 F1-score using deep learning models. Then, we applied the same model on a population sample and obtained a result in line with the depression statistics reported by Statistics Canada for 2015.