# Temporal Mental Health Dynamics on Social Media

arXiv: https://arxiv.org/abs/2008.13121 · HTML: https://mlsanity.com/paper/2008.13121/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook](https://mlsanity.com/paper/2410.08793/) (2024)
- [Datasets for Depression Modeling in Social Media: An Overview](https://mlsanity.com/paper/2503.21513/) (2025)
- [The Problem of Semantic Shift in Longitudinal Monitoring of Social Media: A Case Study on Mental Health During the COVID-19 Pandemic](https://mlsanity.com/paper/2206.11160/) (2022)
- [A Survey on Multilingual Mental Disorders Detection from Social Media Data](https://mlsanity.com/paper/2505.15556/) (2025)
- [Discovering changes in birthing narratives during COVID-19](https://mlsanity.com/paper/2204.11742/) (2022)

## Abstract (arXiv)

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.