Background: It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring. Objective: The objective of this study is to examine COVID-19–related discussions, concerns, and sentiments using tweets posted by Twitter users. Methods: We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, “coronavirus,” “COVID-19,” “quarantine”) from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets. Results: Popular unigrams included “virus,” “lockdown,” and “quarantine.” Popular bigrams included “COVID-19,” “stay home,” “corona virus,” “social distancing,” and “new cases.” We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics. Conclusions: This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.

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中文翻译：

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Twitter关于COVID-19大流行的讨论和情感：机器学习方法

背景：衡量公众对COVID-19大流行的反应非常重要。Twitter是涉及公众反应监测的信息流行病学研究的重要数据源。目的：这项研究的目的是使用Twitter用户发布的推文检查与COVID-19相关的讨论，关注和观点。方法：从2020年3月7日至4月21日，我们使用20个主题标签（例如“冠状病毒”，“ COVID-19”，“隔离区”）列表分析了与COVID-19大流行相关的400万条Twitter消息。机器学习方法，即潜在狄利克雷分配（LDA），用于识别收集的推文中的流行字母和二字组，显着主题和主题以及情感。结果：流行的会标包括“病毒”，“锁定”和“隔离”。受欢迎的二元组包括“ COVID-19”，“呆在家里”，“日冕病毒”，“社会疏远，”和“新案件”。我们确定了13个讨论主题并将其分为5个不同主题：（1）采取公共卫生措施以减缓COVID-19的传播；（2）与COVID-19相关的社会污名；（3）COVID-19新闻，案例和死亡，（4）美国的COVID-19，和世界其他地区的（5）COVID-19。在所有确定的主题中，COVID-19传播的主要情绪是期望可以采取措施，然后是与不同主题相关的混杂的信任，愤怒和恐惧感。与其他主题相比，当人们讨论新的COVID-19病例和死亡时，公开推文显示出一种强烈的恐惧感。结论：这项研究表明，Twitter数据和机器学习方法可用于信息流行病学研究，可以研究COVID-19大流行期间不断发展的公众讨论和情绪。随着形势的迅速发展，Twitter上始终有几个主题占据主导地位，例如确诊病例和死亡率，预防措施，卫生当局和政府政策，COVID-19污名和负面的心理反应（例如恐惧）。对Twitter讨论和关注的实时监控和评估可以为公共卫生应急响应和计划提供有用的数据。与大流行相关的恐惧，污名和心理健康问题已经很明显，并且可能在第二波COVID-19浪潮或当前大流行的新潮出现时继续影响公众信任。例如确诊的病例和死亡率，预防措施，卫生当局和政府政策，COVID-19的污名和负面的心理反应（例如恐惧）。对Twitter讨论和关注的实时监视和评估可以为公共卫生应急响应和计划提供有用的数据。与大流行相关的恐惧，污名和心理健康问题已经很明显，并且可能在第二波COVID-19浪潮或当前大流行的新潮出现时继续影响公众信任。例如确诊的病例和死亡率，预防措施，卫生当局和政府政策，COVID-19的污名和负面的心理反应（例如恐惧）。对Twitter讨论和关注的实时监视和评估可以为公共卫生应急响应和计划提供有用的数据。与大流行相关的恐惧，污名和心理健康问题已经很明显，并且可能在第二波COVID-19浪潮或当前大流行的新潮出现时继续影响公众信任。

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