# Exploring Social Media for Early Detection of Depression in COVID-19 Patients

## TL;DR

This study analyzes social media data to identify early signs of depression in COVID-19 patients and proposes a deep learning model that outperforms existing methods in predicting depression risk.

## Contribution

It introduces a novel deep neural network that integrates mood swings and emotional cues from social media to predict depression in COVID-19 patients.

## Key findings

- Model achieves AUROC of 0.9317
- Model achieves AUPRC of 0.8116
- Outperforms baseline methods

## Abstract

The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly,We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients