Temporal Mental Health Dynamics on Social Media

TL;DR

This paper presents a methodology for analyzing how mental health indicators change over time on social media, using data mining and a case study during the COVID-19 pandemic to inform strategic decisions.

Contribution

It introduces a novel approach for capturing and analyzing temporal mental health dynamics from social media data during a global crisis.

Findings

Encouraging results in detecting pandemic-related mental health trends

Identification of Christmas Depression phenomenon in social media data

Methodology supports strategic mental health decision-making

Abstract

We describe a set of experiments for building a temporal mental health dynamics system. We utilise a pre-existing methodology for distant-supervision of mental health data mining from social media platforms and deploy the system during the global COVID-19 pandemic as a case study. Despite the challenging nature of the task, we produce encouraging results, both explicit to the global pandemic and implicit to a global phenomenon, Christmas Depression, supported by the literature. We propose a methodology for providing insight into temporal mental health dynamics to be utilised for strategic decision-making.