Background: The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic. Objective: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework. Methods: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. Results: The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens’ mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P<.05) in the second lockdown, showing increased frustration. Temporal emotion analysis was conducted by modeling the emotion state changes across the four stages of the pandemic, which demonstrated how different emotions emerged and shifted over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles. Conclusions: This study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic recovery and understanding psychological impact via emotion changes, and they could potentially inform health care decision making. This study exploited AI and social media to enhance our understanding of human behaviors in global emergencies, which could lead to improved planning and policy making for future crises.

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中文翻译：

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COVID-19的情感：使用人工智能对自我报告信息进行内容分析

背景：COVID-19大流行已经破坏了全世界的人类社会。突发公共卫生事件之后，大量人员丧生；随之而来的社会限制导致失业，缺乏互动以及心理困扰迅速增加。引入物理疏散法规以管理爆发时，个人，团体和社区广泛参与社交媒体以表达其思想和情感。这种通过互联网传播的自我报告信息交流，囊括了受到大流行影响的所有个人的情感健康和心理健康。目的：本研究旨在利用人工智能（AI）框架，研究随着时间推移在社交媒体上表达的与COVID-19大流行相关的人类情绪。方法：我们的研究探索了流感大流行四个阶段的情感分类，强度，转变和特征，以及与关键主题和主题的一致性：宣布全球健康危机（即大流行前），首次锁定，放松限制，以及第二次锁定。这项研究采用了一个AI框架，该框架包括自然语言处理，单词嵌入，马尔可夫模型以及不断增长的自组织映射算法，这些算法共同用于调查社交媒体对话。该调查是使用2020年1月至2020年9月澳大利亚用户发布的73,000条公共Twitter对话进行的。结果：这项研究的结果使我们能够分析和可视化COVID期间在社交媒体上表达和反映的不同情绪和相关关注-19年大流行，可以用来深入了解公民的心理健康。首先，主题分析表明人们在大流行的四个阶段中表达了多种多样的共同关注。据指出，随着时间的流逝，在社交媒体上表达的个人关注已升级为更广泛的关注。其次，情绪强度和情绪状态转变表明，恐惧和悲伤情绪起初更为突出。然而，随着时间的流逝，情绪逐渐转变为愤怒和厌恶。在第二次锁定中，除悲伤外，负面情绪显着更高（P <.05），表明沮丧情绪增加。通过对大流行四个阶段的情绪状态变化进行建模，可以进行时间情感分析，这表明了随着时间的流逝，不同的情感如何出现和转移。第三，社交媒体用户表达的担忧被归类为配置文件，其中第一锁定配置文件和第二锁定配置文件之间可以看到差异。结论：这项研究表明，在COVID-19大流行期间，社交媒体上表达和记录的各种情感和关注反映了公众的心理健康。尽管这项研究建立了在无法进行身体交流的情况下使用社交媒体来发现有见地的见解的结果，但结果也可能有助于大流行后的恢复和通过情绪变化来了解心理影响，并且它们有可能为医疗保健决策提供依据。这项研究利用AI和社交媒体来增强我们对全球紧急情况下人类行为的了解，

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