Coronavirus disease (COVID-19) pandemic has changed various aspects of people's lives and behaviors. At this stage, there are no other ways to control the natural progression of the disease than adopting mitigation strategies such as wearing masks, watching distance, and washing hands. Moreover, at this time of social distancing, social media plays a key role in connecting people and providing a platform for expressing their feelings. In this study, we tap into social media to surveil the uptake of mitigation and detection strategies, and capture issues and concerns about the pandemic. In particular, we explore the research question, "how much can be learned regarding the public uptake of mitigation strategies and concerns about COVID-19 pandemic by using natural language processing on Reddit posts?" After extracting COVID-related posts from the four largest subreddit communities of North Carolina over six months, we performed NLP-based preprocessing to clean the noisy data. We employed a custom Named-entity Recognition (NER) system and a Latent Dirichlet Allocation (LDA) method for topic modeling on a Reddit corpus. We observed that 'mask', 'flu', and 'testing' are the most prevalent named-entities for "Personal Protective Equipment", "symptoms", and "testing" categories, respectively. We also observed that the most discussed topics are related to testing, masks, and employment. The mitigation measures are the most prevalent theme of discussion across all subreddits.

翻译：科罗纳病毒( COVID-19 ) 传染病改变了人们生活和行为的各个方面。 在现阶段,除了采取诸如戴面罩、观望距离和洗手等缓解战略之外,没有其他方法可以控制疾病自然蔓延。此外,在目前社会动荡时期,社交媒体在连接人们和提供表达情感的平台方面发挥着关键作用。在这项研究中,我们利用社交媒体来监视缓解和检测战略的采用,并捕捉有关该流行病的问题和关切。特别是,我们探讨了研究问题,“通过在Reddit 站上使用自然语言处理,在公众接受减缓战略和对COVID-19流行病的关切方面可以学到多少?”在从北卡罗来纳州四个最大的子编辑社区提取与COVID有关的职位六个月之后,我们进行了基于NLP的预处理来清理这些噪音数据。我们采用了一种习惯命名实体识别系统(NER) 和一种冷冻狄里特利特尔特( LDA) 分配(LDA) 模式,用于在再施用原体上进行主题建模。我们观察到的“mask” 最普遍的测量、最常用设备” 和“ 测试” 最常用的“ 最常用的“ 测试” 最常用” 和最常用的“ 和最常用的“ 测试”的“最常用设备” 和最常用的“最常用的“最常用” 测试” 测试” 主题” 。