Political speeches and debates play an important role in shaping the images of politicians, and the public often relies on media outlets to select bits of political communication from a large pool of utterances. It is an important research question to understand what factors impact this selection process. To quantitatively explore the selection process, we build a three-decade dataset of presidential debate transcripts and post-debate coverage. We first examine the effect of wording and propose a binary classification framework that controls for both the speaker and the debate situations. We find that crowdworkers can only achieve an accuracy of 60 in this task, indicating that media choices are not entirely obvious. Our classifiers outperform crowdworkers on average, mainly in primary debates. We also compare important factors from crowdworkers’ free responses with those from data-driven methods and find interesting differences. Few crowdworkers mentioned that “context matters”, whereas our data show that well-quoted sentences are more distinct from the previous utterance by the same speaker than less-quoted sentences. Finally, we examine the aggregate effect of media preferences towards different wordings to understand the extent of fragmentation among media outlets. By analyzing a bipartite graph built from quoting behavior in our data, we observe a decreasing trend in bipartisan coverage.
The effect of post-debate coverage on public opinion. Studies have shown that media choices about coverage can have serious consequences @cite_37 @cite_24 @cite_38 @cite_27 @cite_17 @cite_1 @cite_50 . For instance, show that in the 2004 U.S. election, citizens who only read the news coverage rated Kerry more negatively compared to those who watched the debate firsthand, because media outlets highlighted the moment of Kerry outing Cheney's lesbian daughter, although this moment did not catch much attention from the live audience. develop a mobile app to collect real-time feedback for presidential debates. discuss the media's critical tendency and its partisan consequences in the U.S.
@cite_38 @cite_37 @cite_1 @cite_24 @cite_27 @cite_50 @cite_17
2125499264 1992716803 2576785659 1995998136 2065584236 2594152500 2017712094
We demonstrate that the news media's “spin” or analysis following the last presidential debate in 2004 influenced citizens' evaluations of the candidates. The media's “instant analyses” in the twenty-four hours following the debate was decidedly one-sided, favoring President Bush more than Senator Kerry. We show that the news media's spin persuaded potential voters to alter their attitudes regarding the competing candidates. We rely on a multimethodological approach, including an experiment with a quasi-experimental component, a public opinion survey, and a content analysis. To examine the media's spin, we conducted a content analysis of news coverage on television, on the Internet, and in newspapers for the twenty-four hours following the final 2004 presidential debate. Second, to examine how citizens reacted to the media's coverage, we relied on a representative public opinion survey conducted immediately following the debate. In addition, we conducted an experiment where certain individuals were expose... We measure media bias by estimating ideological scores for several major media outlets. To compute this, we count the times that a particular media outlet cites various think tanks and policy groups, and then compare this with the times that members of Congress cite the same groups. Our results show a strong liberal bias: all of the news outlets we examine, except Fox News' Special Report and the Washington Times, received scores to the left of the average member of Congress. Consistent with claims made by conservative critics, CBS Evening News and the New York Times received scores far to the left of center. The most centrist media outlets were PBS NewsHour, CNN's Newsnight, and ABC's Good Morning America; among print outlets, USA Today was closest to the center. All of our findings refer strictly to news content; that is, we exclude editorials, letters, and the like. "The editors in Los Angeles killed the story. They told Witcover that it didn't ‘come off’ and that it was an ‘opinion’ story.… The solution was simple, they told him. All he had to do was get other people to make the same points and draw the same conclusions and then write the article in their words" (emphasis in original). Timothy Crouse, Boys on the Bus [1973, p. 116]. Political speeches and debates play an important role in shaping the images of politicians, and the public often relies on media outlets to select bits of political communication from a large pool of utterances. It is an important research question to understand what factors impact this selection process. To quantitatively explore the selection process, we build a three-decade dataset of presidential debate transcripts and post-debate coverage. We first examine the effect of wording and propose a binary classification framework that controls for both the speaker and the debate situations. We find that crowdworkers can only achieve an accuracy of 60 in this task, indicating that media choices are not entirely obvious. Our classifiers outperform crowdworkers on average, mainly in primary debates. We also compare important factors from crowdworkers’ free responses with those from data-driven methods and find interesting differences. Few crowdworkers mentioned that “context matters”, whereas our data show that well-quoted sentences are more distinct from the previous utterance by the same speaker than less-quoted sentences. Finally, we examine the aggregate effect of media preferences towards different wordings to understand the extent of fragmentation among media outlets. By analyzing a bipartite graph built from quoting behavior in our data, we observe a decreasing trend in bipartisan coverage. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 .