Temporal Mental Health Dynamics on Social Media
This work addresses mental health monitoring for public health stakeholders, but it is incremental as it applies an existing method to new data.
The authors tackled the problem of tracking temporal mental health dynamics on social media by deploying a system during the COVID-19 pandemic, producing encouraging results for pandemic-related trends and Christmas Depression.
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.