Collaborative Research: Mass Media and Representative Democracy

本项目研究大众传媒在代议制民主政府运作中的作用。 它侧重于媒体内容中政策线索的频率，准确性和清晰度。 它还探讨了公众如何利用这些线索来告知他们对美国政策的偏好。 调查人员开始有三个重要的命题。 首先，一个积极响应的民主公众只需要对政策和政策变化有基本的了解。第二，必要的信息（在某些领域）在媒体内容中很容易获得。 第三，公民会根据这些线索调整自己的政策偏好。该项目测试这些主张，利用媒体报道的政策领域的差异，利用该国不同地区的多个新闻媒体，以了解意见-政策联系的变化。 调查人员假设，有些领域的媒体报道提供了相当准确的政策观点，而另一些领域则缺乏媒体内容。公众对政策的看法和偏好的准确性应该相应地受益或受损。 这个项目提供了一个独特的，“大”数据驱动的调查如何（以及何时，为什么）代议制民主的工作。该项目的核心是一个自动化的内容分析数据集，包括过去35年来关于美国公共政策的数百万条新闻报道和电视新闻记录，涉及一系列支出领域。 新闻内容来自全文数据库（主要是LIGHT-Nexis），并使用字典和机器学习方法进行分析。 通过人工编码验证了该方法的可靠性和有效性。这些内容分析数据与现有的预算时间序列和民意调查一起进行审查。 分析提供了第一个大规模的探索，各种政策信息，做，不，出现在媒体报道。这将使团队能够评估个人的政策偏好如何根据他们消费的与政策相关的媒体内容而变化。 作为评估这种影响的一部分，该小组将直接比较媒体对政策的报道和八个政策领域的实际政策。 结果提供了一个新的和独特的观点，媒体在代议制民主中发挥的作用，特别是在公众对政策的反应，跨问题领域和随着时间的推移。

General AbstractThis project examines the role of mass media in the functioning of representative democratic government. It focuses on the frequency, accuracy, and clarity of policy cues in media content. It also explores how the public uses these cues to inform their preferences for policy in the US. The investigators begin with three important propositions. First, a responsive democratic public requires only basic levels of knowledge about policy and policy change. Second,the necessary information is (in some domains) readily available in media content. Third, citizens pick up on these cues and adjust their policy preferences accordingly. This project tests these propositions, leveraging differences in media coverage across policy areas drawing upon multiple news outlets in different regions of the country to understand variation in the opinion-policy link. The investigators hypothesize that there are areas where media coverage provides a reasonably accurate view of policy, and others in which media content is lacking. The accuracy of public perceptions of, and preferences for, policy should benefit or suffer accordingly. This project provides a unique, "big" data-driven investigation into how (and when, and why) representative democracy works. Technical AbstractThe centerpiece of this project is an automated content-analytic dataset comprised millions of news stories and television news transcripts on US public policy, across a range of spending areas, over the past 35 years. News content is drawn from full-text databases (primarily Lexis-Nexis) and analyzed using both dictionary and machine-learning approaches. The reliability and validity of the method is tested through human coding. These content-analytic data are examined alongside existing budgetary time series and opinion polling. Analyses offer the first large-scale exploration into the kinds of policy information that do, and do not, appear in media coverage. This will allow the team to assess how the policy preferences of individuals might change depending on the policy-relevant media content they consume. As part of the assessment of this influence, the team will provide a direct comparison of media coverage of policy and actual policy across eight policy areas. Results provide a new and unique view into the role that media play in representative democracy, particularly in public responsiveness to policy, across issue areas and over time.