Quantifying Media Influence on Covid-19 Mask-Wearing Beliefs

How political beliefs change in accordance with media exposure is a complicated matter. Some studies have been able to demonstrate that groups with different media diets in the aggregate (e.g., U.S. media consumers ingesting partisan news) arrive at different beliefs about policy issues, but proving this from data at a granular level -- at the level of attitudes expressed in news stories -- remains difficult. In contrast to existing opinion formation models that describe granular detail but are not data-driven, or data-driven studies that rely on simple keyword detection and miss linguistic nuances, being able to identify complicated attitudes in news text and use this data to drive models would enable more nuanced empirical study of opinion formation from media messaging. This study contributes a dataset as well as an analysis that allows the mapping of attitudes from individual news stories to aggregate changes of opinion over time for an important public health topic where opinion differed in the U.S. by partisan media diet: Covid mask-wearing beliefs. By gathering a dataset of U.S. news media stories, from April 6 to June 8, 2020, annotated according to Howard 2020's Face Mask Perception Scale for their statements regarding Covid-19 mask-wearing, we demonstrate fine-grained correlations between media messaging and empirical opinion polling data from a Gallup survey conducted during the same period. We also demonstrate that the data can be used for quantitative analysis of pro- and anti-mask sentiment throughout the period, identifying major events that drove opinion changes. This dataset is made publicly available and can be used by other researchers seeking to evaluate how mask-wearing attitudes were driven by news media content. Additionally, we hope that its general method can be used to enable other media researchers to conduct more detailed analyses of media effects on opinion.

政治信仰如何随着媒体曝光而改变是一个复杂的问题。一些研究能够证明，总体上具有不同媒体接触习惯的群体（例如，接触党派新闻的美国媒体消费者）对政策问题会产生不同的看法，但从微观层面的数据——从新闻报道所表达的态度层面——来证明这一点仍然很困难。与那些描述微观细节但非数据驱动的现有意见形成模型，或者依赖简单关键词检测而忽略语言细微差别的数据驱动研究不同，能够识别新闻文本中的复杂态度并利用这些数据来驱动模型，将能够对媒体信息影响下的意见形成进行更细致入微的实证研究。本研究提供了一个数据集以及一项分析，该分析能够将个别新闻报道中的态度映射到一个重要的公共卫生话题（在美国，因党派媒体倾向不同而观点各异的新冠口罩佩戴信念）随时间推移的总体意见变化上。通过收集2020年4月6日至6月8日期间的美国新闻媒体报道数据集，并根据霍华德2020年的口罩感知量表对其关于新冠病毒口罩佩戴的陈述进行标注，我们展示了媒体信息与同期盖洛普调查的实证民意调查数据之间的精细关联。我们还证明了这些数据可用于对整个期间支持和反对戴口罩的情绪进行定量分析，确定推动意见变化的重大事件。这个数据集是公开的，可供其他研究人员用于评估新闻媒体内容如何影响口罩佩戴态度。此外，我们希望其通用方法能够被其他媒体研究人员用于对媒体对意见的影响进行更详细的分析。