基于使用自然语言处理的社交媒体评论的COVID-19流行病引发的健康、心理和社会问题
Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing
摘要：
COVID-19大流行已造成全球健康危机，影响人类生活的多个方面。在缺乏疫苗和抗病毒药物的情况下，已经实施了一些行为改变和政策措施，例如物理距离，以控制冠状病毒的传播。社交媒体数据可以揭示公众对全球各国政府和卫生机构如何处理大流行病的看法，以及这种疾病对人们的影响，无论他们的地理位置与阻碍或促进全球控制大流行病传播的各种因素一致。本文旨在利用社交媒体数据调查COVID-19大流行对全球人们的影响。我们应用自然语言处理和主题分析，利用社交媒体数据了解公众对COVID-19疫情的意见、体验和问题。首先，我们从Twitter、Facebook、YouTube和三个在线论坛收集了超过4700万条与COVID-19相关的评论。其次，我们执行数据预处理，包括应用NLP技术来清理和准备自动主题提取的数据。第三，我们应用上下文感知的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词或主题，并计算每个主题的情感得分，并使用基于词典的技术根据得分分配情感极性。第四，我们将相关主题归类为更广泛的主题。共出现34个负面主题，其中15个为公众视角的健康相关问题、心理社会问题及与COVID-19疫情相关的社会问题。此外，从我们的研究结果中还发现了20个积极的主题。最后，我们建议的干预措施，可以帮助解决基于积极的主题和其他补救的想法植根于研究的负面问题。
英文摘要：
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 behavioural change and policy initiatives, such as physical distancing, have been implemented to control the spread of the coronavirus. Social media data can reveal public perceptions toward how governments and health agencies across the globe are handling the pandemic, as well as 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 globally using social media data. We apply 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 collect over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we perform data preprocessing which involves applying NLP techniques to clean and prepare the data for automated theme extraction. Third, we apply context-aware NLP approach to extract meaningful keyphrases or themes from over 1 million randomly selected comments, as well as compute sentiment scores for each theme and assign sentiment polarity based on the scores using lexicon-based technique. Fourth, we categorize related themes into broader themes. A total of 34 negative themes emerged, out of which 15 are health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. In addition, 20 positive themes emerged from our results. Finally, we recommend interventions that can help address the negative issues based on the positive themes and other remedial ideas rooted in research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
