ABSTRACT

We compare the social media discourses on COVID-19 vaccines constructed by U.S. politicians, medical experts, and government agencies, and investigate how various contextual factors influence the likelihood of government agencies politicizing the issue. Taking the political corpus and the medical corpus as two extremes, we propose a language-based definition of politicization of science and measure it on a continuous scale. By building a machine learning classifier that captures subtle linguistic indicators of politicization and applying it to two years of government agencies’ Facebook posting history, we demonstrate that: 1) U.S. politicians heavily politicized COVID-19 vaccines, medical experts conveyed minimal politicization, and government agencies’ discourse was a mix of the two, yet more closely resembled medical experts;’ 2) increasing COVID-19 infection rates reduced government agencies’ politicization tendencies; 3) government agencies in Democratic-leaning states were more likely to politicize COVID-19 vaccines than those in Republican-leaning states; and 4) the degree of politicization did not significantly differ across agencies’ jurisdiction levels. We discuss the conceptualization of politicization of science, the incumbency effect, and government communication as an emerging area for political communication research.

摘要

我们比较了美国政客、医学专家和政府机构构建的关于 COVID-19 疫苗的社交媒体话语，并调查了各种背景因素如何影响政府机构将这一问题政治化的可能性。将政治语料库和医学语料库作为两个极端，我们提出了基于语言的科学政治化定义，并在连续尺度上对其进行了测量。通过构建一个机器学习分类器，捕捉微妙的政治化语言指标，并将其应用于政府机构两年的 Facebook 发布历史，我们证明：1) 美国政客将 COVID-19 疫苗严重政治化，医学专家传达的政治化程度最低，政府机构的话语是两者的混合体，但更接近于医学专家；2) COVID-19 感染率的增加降低了政府机构的政治化倾向；3) 与倾向共和党的州相比，倾向民主党的州的政府机构更有可能将 COVID-19 疫苗政治化；4) 政治化程度在不同机构的管辖级别之间没有显着差异。我们讨论了科学政治化的概念化、在职效应和作为政治传播研究新兴领域的政府传播。