ArXiv Preprint
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
Jiageng Wu, Xian Wu, Yining Hua, Shixu Lin, Yefeng Zheng, Jie Yang
2023-02-23
