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Below results based on the criteria 'media'
Total number of records returned: 10
Politicians and the Press: Who Leads, Who Follows?
Bartels, Larry M.
his paper examines the interplay between politicians and the press in setting the national policy agenda. The data for the analysis consist of daily counts of executive branch activities, congressional activities, New York Times stories, local newspaper stories, and ABC News coverage of Bosnia, Medicare, NAFTA, and Whitewater during the first three years of the Clinton administration. Vector autoregressions suggest that all three media outlets (and the politicians themselves) followed the lead of the executive branch on Bosnia and NAFTA and of Congress on Medicare and Whitewater. However, New York Times coverage led political activities even more than it followed them, with especially strong agenda-setting effects for NAFTA and Whitewater. The independent agenda-setting power of ABC News was substantially less than that of the Times, but still considerable, while local newspapers tended, by and large, to follow the lead of politicians and the national news media. Prepared for presentation at the Annual Meeting of the American Political Science Association, San Francisco, September 1996.
Exploiting a Rare Shift in Communication Flows to Document News Media Persuasion: The 1997 United Kingdom General Election
Using panel data and matching techniques, we exploit a rare change in communication flows -- the endorsement switch to the Labour Party by several prominent British newspapers before the 1997 United Kingdom general election -- to study the persuasive power of the news media. These unusual events provide an opportunity to test for news media persuasion while avoiding methodological pitfalls that have plagued previous studies. By comparing readers of newspapers that switched endorsements to similar individuals who did not read these newspapers, we estimate that these papers persuaded a considerable share of their readers to vote for Labour. Depending on the statistical approach, the point estimates vary from about 10 percent to as high as 25 percent of readers. These findings provide rare, compelling evidence that the news media exert a powerful influence on mass political behavior.
Foreign Media and Protest Diffusion in Authoritarian Regimes: The Case of the 1989 East German Revolution
Does access to foreign media facilitate the diffusion of protest in authoritarian regimes? Apparently for the first time, I test this hypothesis by exploiting a natural experiment in communist East Germany. I take advantage of the fact that West German television broadcasts could be received in most but not all parts of East Germany and conduct a matched analysis in which counties without access to West German television are matched to a comparison group of counties with West German television. Comparing these two groups of East German counties, I find no evidence that West German television affected the speed or depth of protest diffusion during the 1989 East German revolution.
Opium for the Masses: How Foreign Media Can Stabilize Authoritarian Regimes
local average response function
In this case study of the impact of West German television on public support for the East German communist regime, we evaluate the conventional wisdom in the democratization literature that foreign mass media undermine authoritarian rule. We exploit formerly classified survey data and a natural experiment to identify the effect of foreign media exposure using instrumental variable estimators. Contrary to conventional wisdom, East Germans exposed to West German television were more satisfied with life in East Germany and more supportive of the East German regime. To explain this surprising finding, we show that East Germans used West German television primarily as a source of entertainment. Behavioral data on regional patterns in exit visa applications and archival evidence on the reaction of the East German regime to the availability of West German television corroborate this result.
Unpacking the Black Box: Learning about Causal Mechanisms from Experimental and Observational Studies
direct and indirect effects
Understanding causal mechanisms is a fundamental goal of social science research. Demonstrating whether one variable causes a change in another is often insufficient, and researchers seek to explain why such a causal relationship arises. Nevertheless, little is understood about how to identify causal mechanisms in empirical research. Many researchers either informally talk about possible causal mechanisms or attempt to quantify them without explicitly stating the required assumptions. Often, some assert that process tracing in detailed case studies is the only way to evaluate causal mechanisms. Others contend the search for causal mechanisms is so elusive that we should instead focus on causal effects alone. In this paper, we show how to learn about causal mechanisms from experimental and observational studies. Using the potential outcomes framework of causal inference, we formally define causal mechanisms, present general identification and estimation strategies, and provide a method to assess the sensitivity of one's conclusions to the possible violations of key identification assumptions. We also propose several alternative research designs for both experimental and observational studies that may help identify causal mechanisms under less stringent assumptions. The proposed methodology is illustrated using media framing experiments and observational studies of incumbency advantage.
Methods for Extremely Large Scale Media Experiments and Observational Studies
We develop statistical methods and large scale data engineering approaches for estimating the effects of a large number of mass and specialized media sites on opinions expressed in the daily flow of millions of social media posts. We first describe the instruments we adapt, develop, and validate for summarizing detailed opinions in social media posts, and then outline the procedures we devised for acquiring and summarizing news content from, and web traffic to, large numbers of media outlets. We then derive statistical methods for estimating the causal effect of changes in the news on social media opinions appropriate for observational, quasi-experimental, and experimental settings
How Newspapers Reveal Political Power
Political science is in large part the study of power, but power itself is difficult to measure. We argue that we can use newspaper coverage—in particular, the relative amount of space devoted to particular subjects in newspapers—to measure the relative power of an important set of political actors and offices. We use a new dataset containing nearly 50 million historical newspaper pages from 2,700 local U.S. newspapers over the years 1877–1977. We define and discuss a measure of power we develop based on observed word frequencies, and we validate it through a series of analyses. Overall, we find that the relative coverage of political actors and of political offices is a strong indicator of political power for the cases we study. To illustrate its usefulness, we then apply the measure to understand when (and where) state party committees lost their power. Taken together, the paper sheds light on the nature of political news coverage and offers both a new dataset and a new measure for studying political power in a wide set of contexts.
Issues in Machine Learning and Approaches to Analyzing Media Coverage of the Economy
text as data
How media outlets decide to cover economic news can have a large impact on citizens' sociotropic perceptions of the economy and therefore also their political behavior. Previous studies often rely on dictionary methods to measure the tone and scope of media coverage of the economy. In this paper we show that this approach leads to low accuracy and noisy estimates. Instead, we propose the use of machine learning methods to automatically classify large datasets of news articles and transcripts. Using a corpus of articles about the economy published in The New York Times from 1947 to 2010, we assess the performance of sentiment classifiers that make different choices regarding the unit of analysis (sentences or larger text units), source of codings (crowd workers or undergraduate students), and coding aggregation (choosing the modal category or treating coders' responses as multiply imputed datasets). Our analysis provides practical recommendations about the trade-offs inherent to the automatic classification of media content, an area of increased interest for researchers in political communication. As an application of our suggested approach, we examine the relationship between media coverage of the U.S. economy, economic indicators at the macro level, and citizens' perceptions of the economy.
An Unbiased Measure of Media Bias Using Latent Topic Models
Systematic attempts to measure partisan bias in the media tended to focus on estimating presentation bias, also known as “slant” or “spin,” using article keywords, phrases and citations. Groseclose and Milyo (2005) measure media bias by comparing interest group citations in news sources to interest group citations by members of Congress. Gentzkow and Shapiro (2010) measure bias by comparing newspaper article language with language in Congressional speeches. News in the era of Google and the Internet, however, is more prone to a form of agenda-setting bias sometimes called selection bias in which partisan narratives are constructed by selecting stories about topics which fit these narratives (Groeling 2013). While the expression of a partisan agenda among pre-Internet news organizations often required journalists to spin the same stories that their competitors were reporting on, contemporary news organizations can now more easily write a series of stories that appear to be "spin-free" but yet together form a highly biased partisan narrative. In this paper, we use probabilistic topic models to measure bias among 13 of the top online news sources and rank these organizations accordingly.
Out-of-Step, but in the News? The Incumbent Bias of a Milquetoast Press
Text as Data
Why do citizens routinely fail to vote out-of-step representatives out of office and what institutions can help voters hold politicians more accountable? While third-party monitors like the press have the potential to foster democratic accountability by highlighting the behavior of out-of-step representatives for their constituents, do newspapers actually pull the fire alarm on out-of-step incumbents? Using an ensemble “SuperLearner” approach to classify all local newspaper coverage of candidates in the 2010 House election, I incorporate uncertainty into text-based machine learning estimates of candidate coverage by bootstrapping training sets from a learning set classified by research assistants. After validating the machine learning predictions on an out-of-sample test set, I also analyze the learning set directly to further validate the text as data results. I find that newspapers provide overwhelmingly neutral coverage with little candidate criticism, failing to pull the fire alarm on out-of-step incumbents.