论文标题
分析科学文章情绪对COVID-19疫苗接种率的影响
Analyzing the Impact of Sentiments of Scientific Articles on COVID-19 Vaccination Rates
论文作者
论文摘要
在Covid-19大流行的顶峰时，全球许多国家都试图动员疫苗接种运动，以遏制病毒造成的蔓延和死亡人数。传播有关共同疫苗的信息的一种途径是科学文章，该途径为此提供了一定程度的信誉。因此，这增加了如果文章传达有关疫苗接种的积极信息并相反，如果这些物品传达了疫苗接种的可能性，则查看这些文章的人们将被接种疫苗接种。话虽如此，这项研究旨在调查文章情绪与美国疫苗接种相应增加或减少之间的相关性。为此，通过两个步骤进行了基于词典的情感分析：首先，通过名为BeautifulSoup的Python库来刮擦文章内容，其次，Vader被用来根据刮擦文本内容获得每个文章的情感分析分数。结果表明，在美国的平均情感评分与相应的增加或降低的共同疫苗接种率之间的相关性相对较弱。
At the peak of the COVID-19 pandemic, numerous countries worldwide sought to mobilize vaccination campaigns in an attempt to curb the spread and number of deaths caused by the virus. One avenue in which information regarding COVID vaccinations is propagated is that of scientific articles, which provide a certain level of credibility regarding this. Hence, this increases the probability that people who view these articles would get vaccinated if the articles convey a positive message on vaccinations and conversely decreases the probability of vaccinations if the articles convey a negative message. This being said, this study aims to investigate the correlation between article sentiments and the corresponding increase or decrease in vaccinations in the United States. To do this, a lexicon-based sentiment analysis was performed in two steps: first, article content was scraped via a Python library called BeautifulSoup, and second, VADER was used to obtain the sentiment analysis scores for each article based on the scraped text content. Results suggest that there was a relatively weak correlation between the average sentiment score of articles and the corresponding increase or decrease in COVID vaccination rates in the US.
