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

翻译：COVID-19大流行对全球健康造成了严重损害。尽管已过去三年，世界仍在与该病毒作斗争。人们日益担忧COVID-19对感染者心理健康的影响，他们更易出现抑郁症状，这将对患者个人乃至全球产生持久后果。早期检测与干预可降低COVID-19患者的抑郁风险。本文通过社交媒体分析，研究了COVID-19感染与抑郁之间的关联。首先，我们构建了一个包含COVID-19患者感染前后社交媒体活动信息的数据库。其次，对该数据库进行了广泛分析，以探究具有高抑郁风险的COVID-19患者特征。最后，我们提出了一种用于早期预测抑郁风险的深度神经网络模型。该模型将日常情绪波动视为精神信号，并通过知识蒸馏融合文本与情感特征。实验结果表明，我们提出的框架在检测抑郁风险方面优于基准模型，AUROC达到0.9317，AUPRC达到0.8116。该模型有望帮助公共卫生机构对高风险患者实施及时干预。