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.