On the incidence of depression symptoms on social media

Abstract

Due to their increasing popularity, researchers and health professionals are actively utilizing social media networks as valuable tools to recognize linguistic patterns associated with mental health. In this research, our aim was to better understand to what extent the Beck Depression Inventory (BDI) could undergo automated screening based on users’ social media feeds. To this end, we conducted different experiments to analyze the prevalence of BDI items on social media. We present an approach to categorizing and ranking BDI items considering the quantity of information that can be obtained from social media posts. Given publications written by people who have personally reported being diagnosed with depression, we run different search methods and, based on the number of elements retrieved, we study the prevalence of BDI symptoms at two levels of coverage. Finally, we investigate the impact of prevalence and various characteristics on the efficacy of automated assessment tools. Our analysis indicates that specific elements occur consistently across various search methods and social media platforms, implying a higher prevalence of related symptoms in the data sets analyzed. Interestingly, some items with low incidence in the data sets are those of the BDI questionnaire, whose responses are more accurately estimated using automated methods.