Christensen Bente, Laydon Daniel, Chelkowski Tadeusz, Jemielniak Dariusz, Vollmer Michaela, Bhatt Samir, Krawczyk Konrad
Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark.
Department of Infectious Disease Epidemiology MRC Centre for Global Infectious Disease Analysis Imperial College London London United Kingdom.
JMIR Infodemiology. 2022 Sep 20;2(2):e35121. doi: 10.2196/35121. eCollection 2022 Jul-Dec.
Achieving herd immunity through vaccination depends upon the public's acceptance, which in turn relies on their understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear communication of often complex information and, increasingly, the countering of misinformation. The primary outlet shaping public understanding is mainstream online news media, where coverage of COVID-19 vaccines was widespread.
We used text-mining analysis on the front pages of mainstream online news to quantify the volume and sentiment polarization of vaccine coverage.
We analyzed 28 million articles from 172 major news sources across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of overall articles devoted to vaccines. We performed topic detection using BERTopic and named entity recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all collated English-language articles.
The proportion of front-page articles mentioning vaccines increased from 0.1% to 4% with the outbreak of COVID-19. The number of negatively polarized articles increased from 6698 in 2015-2019 to 28,552 in 2020-2021. However, overall vaccine coverage before the COVID-19 pandemic was slightly negatively polarized (57% negative), whereas coverage during the pandemic was positively polarized (38% negative).
Throughout the pandemic, vaccines have risen from a marginal to a widely discussed topic on the front pages of major news outlets. Mainstream online media has been positively polarized toward vaccines, compared with mainly negative prepandemic vaccine news. However, the pandemic was accompanied by an order-of-magnitude increase in vaccine news that, due to low prepandemic frequency, may contribute to a perceived negative sentiment. These results highlight important interactions between the volume of news and overall polarization. To the best of our knowledge, our work is the first systematic text mining study of front-page vaccine news headlines in the context of COVID-19.
通过接种疫苗实现群体免疫取决于公众的接受程度，而公众的接受程度又依赖于他们对疫苗风险和益处的理解。因此，关于疫苗的公共卫生信息传递的基本目标是清晰地传达往往很复杂的信息，并且越来越多地要应对错误信息。塑造公众理解的主要渠道是主流在线新闻媒体，其中关于新冠疫苗的报道很广泛。
我们对主流在线新闻的头版进行文本挖掘分析，以量化疫苗报道的数量和情感极化情况。
我们分析了2015年7月至2021年4月期间来自11个国家172个主要新闻来源的2800万篇文章。我们采用基于关键词的频率分析来估计专门讨论疫苗的文章在所有文章中所占的比例。我们使用BERTopic进行主题检测，并使用命名实体识别来识别在疫苗相关背景下提到的主要主题和行为主体。我们使用Vader Python模块对所有整理好的英文文章进行情感极化量化。
随着新冠疫情的爆发，头版文章中提及疫苗的比例从0.1%上升到了4%。负面极化文章的数量从2015 - 2019年的6698篇增加到了2020 - 2021年的28552篇。然而，在新冠疫情大流行之前，总体疫苗报道略微呈负面极化（57%为负面），而在疫情期间的报道呈正面极化（38%为负面）。
在整个疫情期间，疫苗已从一个边缘话题上升为主要新闻媒体头版广泛讨论的话题。与疫情前主要为负面的疫苗新闻相比，主流在线媒体对疫苗的报道呈正面极化。然而，疫情期间疫苗新闻数量增加了一个数量级，由于疫情前频率较低，这可能导致一种负面情绪的感觉。这些结果突出了新闻数量与总体极化之间的重要相互作用。据我们所知，我们的工作是在新冠疫情背景下对头版疫苗新闻标题进行的首次系统文本挖掘研究。
