Analyzing public opinion on COVID-19 through different perspectives and stages.

IF 3.2 Q1 Computer Science APSIPA Transactions on Signal and Information Processing Pub Date : 2021-03-17 eCollection Date: 2021-01-01 DOI:10.1017/ATSIP.2021.5
Yuqi Gao, Hang Hua, Jiebo Luo
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Abstract

In recent months, COVID-19 has become a global pandemic and had a huge impact on the world. People under different conditions have very different attitudes toward the epidemic. Due to the real-time and large-scale nature of social media, we can continuously obtain a massive amount of public opinion information related to the epidemic from social media. In particular, researchers may ask questions such as "how is the public reacting to COVID-19 in China during different stages of the pandemic?", "what factors affect the public opinion orientation in China?", and so on. To answer such questions, we analyze the pandemic-related public opinion information on Weibo, China's largest social media platform. Specifically, we have first collected a large amount of COVID-19-related public opinion microblogs. We then use a sentiment classifier to recognize and analyze different groups of users' opinions. In the collected sentiment-orientated microblogs, we try to track the public opinion through different stages of the COVID-19 pandemic. Furthermore, we analyze more key factors that might have an impact on the public opinion of COVID-19 (e.g. users in different provinces or users with different education levels). Empirical results show that the public opinions vary along with the key factors of COVID-19. Furthermore, we analyze the public attitudes on different public-concerning topics, such as staying at home and quarantine. In summary, we uncover interesting patterns of users and events as an insight into the world through the lens of a major crisis.

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从不同角度和阶段分析有关 COVID-19 的舆论。
近几个月来,COVID-19 已成为全球性流行病,对世界产生了巨大影响。不同条件下的人们对这一流行病的态度大相径庭。由于社交媒体的实时性和大规模性,我们可以不断地从社交媒体中获取与疫情相关的海量舆情信息。特别是,研究人员可能会提出 "在疫情的不同阶段,中国公众对 COVID-19 的反应如何?"、"影响中国舆论导向的因素有哪些?"等问题。为了回答这些问题,我们分析了中国最大的社交媒体平台--微博上与疫情相关的舆情信息。具体来说,我们首先收集了大量与 COVID-19 相关的舆情微博。然后,我们使用情感分类器来识别和分析不同用户群体的观点。在收集到的以情感为导向的微博中,我们试图追踪 COVID-19 大流行不同阶段的舆情。此外,我们还分析了更多可能影响 COVID-19 舆论的关键因素(如不同省份的用户或不同教育程度的用户)。实证结果表明,公众意见随着 COVID-19 的关键因素而变化。此外,我们还分析了公众对不同公众关注话题的态度,如留在家中和隔离。总之,我们发现了用户和事件的有趣模式,通过重大危机的视角洞察世界。
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
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