Oyebode Oladapo, Ndulue Chinenye, Adib Ashfaq, Mulchandani Dinesh, Suruliraj Banuchitra, Orji Fidelia Anulika, Chambers Christine T, Meier Sandra, Orji Rita
Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
JMIR Med Inform. 2021 Apr 6;9(4):e22734. doi: 10.2196/22734.
The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally.
This paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data.
We applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes.
A total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research.
We uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics.
新冠疫情引发了一场全球健康危机，影响着人类生活的诸多方面。在缺乏疫苗和抗病毒药物的情况下，已实施了一些行为改变措施和政策举措，如保持社交距离，以控制新冠病毒的传播。社交媒体数据能够揭示公众对全球各国政府和卫生机构应对疫情方式的看法，以及该疾病对人们的影响，无论其地理位置如何，同时还能反映出各种阻碍或促进全球疫情防控努力的因素。
本文旨在利用社交媒体数据调查新冠疫情对全球各地人们的影响。
我们应用自然语言处理（NLP）和主题分析，通过社交媒体数据了解公众对新冠疫情的看法、经历和问题。首先，我们从推特、脸书、优兔和三个在线讨论论坛收集了超过4700万条与新冠疫情相关的评论。其次，我们进行了数据预处理，这包括应用NLP技术清理数据并为自动提取关键短语做准备。第三，我们应用NLP方法从超过100万条随机选择的评论中提取有意义的关键短语，并为每个关键短语计算情感得分，然后使用基于词典的技术根据得分分配情感极性（即积极、消极或中性）。第四，我们将相关的消极和积极关键短语归类为类别或宽泛的主题。
共出现了34个消极主题，其中15个是从公众角度来看与新冠疫情相关的健康问题、心理社会问题和社会问题。一些与健康相关的问题包括死亡率上升、健康担忧、不堪重负的卫生系统和健康问题；而一些心理社会问题是因生活中断导致的挫折感、恐慌性购物和恐惧情绪的表达。社会问题包括骚扰、家庭暴力和错误的社会态度。此外，我们的结果还出现了20个积极主题。一些积极主题包括公众意识、鼓励、感恩、更清洁的环境、在线学习、慈善、精神支持和创新性研究。
我们发现了代表公众对新冠疫情看法的各种消极和积极主题，并根据积极主题和其他研究证据推荐了有助于解决健康、心理社会和社会问题的干预措施。这些干预措施将有助于政府、卫生专业人员和机构、各组织及个人努力遏制新冠病毒的传播并将其影响降至最低，以及应对未来的任何疫情。
