Intelligent Systems Laboratory, Superior School of Technology, Amazonas State University, Manaus, Brazil.
JMIR Public Health Surveill. 2021 Feb 10;7(2):e24585. doi: 10.2196/24585.
The COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet.
The purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil.
This work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts.
In this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic.
This study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities.
COVID-19 大流行正在严重影响全球人民。目前，了解这一现象及其对人民生活影响的一个重要方法是监测互联网上的社交网络和新闻。
本研究旨在提出一种从新闻媒体和社交媒体中捕获主要讨论主题和主题的方法，并应用该方法分析 COVID-19 大流行对巴西的影响。
这项工作提出了一种基于主题建模的方法，即实体识别和文本情感分析，以比较 Twitter 帖子和新闻，并可视化 COVID-19 大流行的演变和影响。我们将分析重点放在巴西，这是大流行的一个重要中心；因此，我们面临着解决巴西葡萄牙语文本的挑战。
在这项工作中，我们收集和分析了来自新闻媒体的 18413 篇文章和来自巴西的 1299084 名用户发布的 1597934 条推文。结果表明，所提出的方法随着时间的推移提高了主题情感分析的性能，从而能够更好地监测互联网媒体。此外，使用该工具，我们提取了一些有关 COVID-19 大流行在巴西演变的有趣见解。例如，我们发现 Twitter 对新闻媒体的主题报道相似；主要实体相似，但主题分布和实体多样性不同。此外，两种媒体都存在一些对政治主题的负面情绪的方面，并且一种特定药物的提及率很高，这表明在大流行期间存在高度的政治两极化。
本研究确定了新闻和社交媒体中讨论的主要主题，以及它们的情感如何随时间演变。可以了解公众在大流行期间的主要关注点，因此获得的所有信息都有助于当局做出决策。
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