Global Mental Health and Neuroethics - Chapter 6: Social media big data analysis for mental health research

Elsevier, Mental Health in a Digital World Global Mental Health in Practice 2022, Pages 109-143
Akkapon Wongkoblap, Miguel A. Vadillo, Vasa Curcin

Mental health problems are widely recognized as a major public health challenge worldwide. This highlights the need for effective tools for detecting mental health disorders in the population. Social media data is a promising source of information where people publish rich personal information that can be mined to extract valuable psychological information. However, social media data poses its own set of challenges, such as the specific terms and expressions used on different platforms, interactions between different users through likes and shares, and the need to disambiguate between statements about oneself and about third parties. Traditionally, social media natural language processing (NLP) techniques have looked at text classifiers and user classification models separately, which presents a challenge for researchers wanting not only to combine text sentiment and user sentiment analysis but also to extract user’s narratives from the textual content.