COVID-19爆发后主流媒体疫苗覆盖率的量化变化:文本挖掘研究

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2022-07-01 DOI:10.2196/35121
Bente Christensen, Daniel Laydon, Tadeusz Chelkowski, Dariusz Jemielniak, Michaela Vollmer, Samir Bhatt, Konrad Krawczyk
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引用次数: 3

摘要

背景:通过疫苗接种实现群体免疫取决于公众的接受程度,而接受程度又取决于公众对疫苗接种的风险和益处的理解。因此,关于疫苗的公共卫生信息传递的基本目标是明确传达往往复杂的信息,并越来越多地打击错误信息。塑造公众理解的主要渠道是主流在线新闻媒体,在这些媒体上,对COVID-19疫苗的报道非常广泛。目的:利用主流网络新闻头版的文本挖掘分析,量化疫苗报道的数量和情绪两极分化。方法: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疫苗相关文章的比例。我们使用BERTopic和命名实体识别进行主题检测,以确定在疫苗背景下提到的主要主题和参与者。我们使用Vader Python模块对所有整理好的英语文章进行情感极化量化。结果:随着新冠肺炎疫情的爆发,提及疫苗的头版文章比例从0.1%上升到4%。负极化文章从2015-2019年的6698篇增加到2020-2021年的28552篇。然而,在COVID-19大流行之前,总体疫苗覆盖率略微呈负极化(57%为阴性),而大流行期间的覆盖率呈正极化(38%为阴性)。结论:在整个大流行期间,疫苗已从次要话题上升为主要新闻媒体头版上广泛讨论的话题。主流网络媒体一直对疫苗持积极的两极分化态度,而大流行前的疫苗新闻主要是负面的。然而,伴随着大流行的是疫苗新闻的数量级增加,由于大流行前的频率较低,这可能会导致人们产生负面情绪。这些结果突出了新闻量和整体两极分化之间的重要相互作用。据我们所知,我们的工作是在COVID-19背景下首次对头版疫苗新闻标题进行系统的文本挖掘研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.

Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.

Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.

Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.

Background: 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.

Objective: We used text-mining analysis on the front pages of mainstream online news to quantify the volume and sentiment polarization of vaccine coverage.

Methods: 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.

Results: 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).

Conclusions: 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.

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