The COVID-19 pandemic has provided psych challenges for many in society. One such challenge is the anxiety that is created in many people faced with the risk of death from the disease. Another issue is understanding how individuals cope psychologically with the threat of death from the disease. In this study we examine the manifestation of death anxiety and various coping mechanisms through the lens of terror management theory (TMT) and online platforms. We take a novel approach to testing the theory using big data analytics and machine learning, focusing on the user-generated content of Twitter users. Based on a sample of all tweets in the UK mentioning COVID-19 terms over a 5-month period, we evaluate dictionary mentions of anxiety and death, and various TMT defense mechanisms, and calculate the pattern of latent death anxiety or ‘terror’ states of Twitter users via Hidden Markov Models. The research identifies four online ‘terror’ states, with high death and anxiety mentions during the peak of the pandemic. Further we examine various TMT defense mechanisms that have been proposed in the literature for coping with death anxiety and find that online social connection, achievement and religion all play important roles in improving the model and explaining movement between states. The paper concludes with various implications of the study for future research and practice.

COVID-19 大流行给社会上的许多人带来了心理挑战。其中一个挑战是许多面临死于疾病风险的人所产生的焦虑。另一个问题是了解个人如何在心理上应对疾病造成的死亡威胁。在这项研究中，我们通过恐怖管理理论 (TMT) 和在线平台的视角来研究死亡焦虑的表现和各种应对机制。我们采用一种新颖的方法使用大数据分析和机器学习来测试该理论，重点关注 Twitter 用户的用户生成内容。基于 5 个月内英国所有提及 COVID-19 术语的推文样本，我们评估字典中提到的焦虑和死亡，以及各种 TMT 防御机制，并通过隐马尔可夫模型计算 Twitter 用户潜在死亡焦虑或“恐怖”状态的模式。该研究确定了四种在线“恐怖”状态，在大流行高峰期间提到了很高的死亡和焦虑。此外，我们研究了文献中提出的应对死亡焦虑的各种 TMT 防御机制，发现在线社会联系、成就和宗教在改进模型和解释状态之间的移动方面都发挥着重要作用。本文总结了该研究对未来研究和实践的各种影响。此外，我们研究了文献中提出的应对死亡焦虑的各种 TMT 防御机制，发现在线社会联系、成就和宗教在改进模型和解释状态之间的移动方面都发挥着重要作用。本文总结了该研究对未来研究和实践的各种影响。此外，我们研究了文献中提出的应对死亡焦虑的各种 TMT 防御机制，发现在线社会联系、成就和宗教在改进模型和解释状态之间的移动方面都发挥着重要作用。本文总结了该研究对未来研究和实践的各种影响。