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
1995998136 1992716803 2112687664 2065584236 2788268216 2017712094 2125499264 2166273866 1678983130 2949098399 2129229917 1904709910 2137809006 2793539342 1968917437 2180109290 153654188 1119807432 2060704337 2550819555
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... Television news coverage following a presidential debate often presents the debate as a contest between winners and losers by employing a horse race paradigm. The use of this paradigm can help viewers form their assessments of the candidates’ performances, but its overuse can limit serious campaign discourse on the issues. This study examines the effect of postdebate analysis by two cable news networks on the perceived outcome of a 2004 presidential debate and the perceptions of the candidates, finding perceptions of the outcome differing between viewers of the two networks. This finding contributes to our understanding of viewer interaction with postdebate television coverage by focusing on the importance of the sources of information. In a news media environment characterized by abundant choice, it is becoming increasingly easy for Americans to choose news sources slanted toward their own political views rather than sources providing more diverse perspectives. This development poses a challenge to ideals of deliberative democracy if people who consume politically like-minded news disproportionately populate the electoral process, while those presumably reaping the benefits of exposure to more diverse views in the news (e.g., more informed, tolerant attitudes) withdraw from politics. Using panel data collected during the 2008 presidential campaign, this study investigates the proposition that exposure to news slanted toward one's own partisan views increases political participation, while exposure to news with the opposite partisan slant depresses participation. The results suggest that, while exposure to partisan news does not alter the strongly habitual decision to turn out, the hypothesized energizing and enervating effects of exposure do appear for other behavior during the campaign; the partisan hue of the news sources citizens choose to consume affects both when voters decide and their levels of participation over time. This article integrates priming and framing into a cognitive-processing model that illustrates how the effects of watching a presidential debate might be influenced by a priming message as it interacts with an individual’s schema. We examine how the frame of postdebate news analysis primes audience reflection on a previously viewed segment from the 2004 presidential debates (a process we identify as ‘‘primed reflection’’). Results show that the influence of postdebate news analysis is a function of the interaction between the news-analysis frame (policy vs. performance) and the individual-level factors, namely, political knowledge and propensity to reflect on media content. 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. Rather than studying the effects of a programmed debate per se, this article examines the “total information environment” that surrounds the debate. A model explaining how people make judgments about the “winner” of a debate is suggested and tested on survey data (N = 748) collected in the days following the second 1996 presidential debate. Results demonstrate that consumption of debate-related news coverage, interpersonal discussion about the debate, and prior candidate attitudes are associated with perceptions of the winner of the debate. In addition, exposure to news coverage of the debate significantly interacted with exposure to the actual debate, so that the impact of news coverage was stronger for those watching little of the actual debate. How do citizens respond to campaign events? We explore this question with a unique repeated measures survey design, fielded during the 2000 presidential campaign. We model transitions in support for the major party candidates following the party conventions and presidential debates. In the aggregate, Gore support increases following the conventions (but not the debates), while Bush support increases with the debates (but not the conventions). But there is considerable microlevel variation in the data: responsiveness to campaign events is greatest among Independents, undecided voters, and “mismatched partisans,” but exactly how these groups respond differs for each event. Moreover, attitudes toward then President Clinton mediate the effect of the campaign events on voter preferences. Two primary conclusions follow: (1) rich data sets are required to observe the effects of campaign events; (2) the influence of campaign events on vote choice is conditional on previous preferences, partisan dispositions, and political context. Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity. This paper analyses changes in public opinion by tracking political discussions in which people voluntarily engage online. Unlike polls or surveys, our approach does not elicit opinions but approximates what the public thinks by analysing the discussions in which they decide to take part. We measure the emotional content of online discussions in three dimensions (valence, arousal and dominance), paying special attention to deviation around average values, which we use as a proxy for disagreement and polarisation. We show that this measurement of public opinion helps predict presidential approval rates, suggesting that there is a point of connection between online discussions (often deemed not representative of the overall population) and offline polls. We also show that this measurement provides a deeper understanding of the individual mechanisms that drive aggregated shifts in public opinion. Our data spans a period that includes two US presidential elections, the attacks of September 11, and the start of military action in Afghanistan and Iraq. The hypothesis of selective exposure assumes that people seek out information that supports their views and eschew information that conflicts with their beliefs, and that has negative consequences on our society. Few researchers have recently found counter evidence of selective exposure in social media: users are exposed to politically diverse articles. No work has looked at what happens after exposure, particularly how individuals react to such exposure, though. Users might well be exposed to diverse articles but share only the partisan ones. To test this, we study partisan sharing on Facebook: the tendency for users to predominantly share like-minded news articles and avoid conflicting ones. We verified four main hypotheses. That is, whether partisan sharing: 1) exists at all; 2) changes across individuals (e.g., depending on their interest in politics); 3) changes over time (e.g., around elections); and 4) changes depending on perceived importance of topics. We indeed find strong evidence for partisan sharing. To test whether it has any consequence in the real world, we built a web application for BBC viewers of a popular political program, resulting in a controlled experiment involving more than 70 individuals. Based on what they share and on survey data, we find that partisan sharing has negative consequences: distorted perception of reality. However, we do also find positive aspects of partisan sharing: it is associated with people who are more knowledgeable about politics and engage more with it as they are more likely to vote in the general elections. In this paper we investigate the effect of the context of interaction on the extent to which a contributor's perspective bias is displayed through their lexical choice. We present a series of experiments on political discussion data. Our experiments indicate that (i) when people quote contributors with an opposing view, they tend to quote the words that are less strongly associated with the opposing view. (ii) Nevertheless, in quoting their opponents, the displayed bias of their word distributions shifts towards that of their opponents. (iii) The personal bias of the speaker is displayed most clearly through the words that are not quoted, (iv) although characteristics of the quoted message do have a measurable effect on the words that are included in the contribution. And, finally, (v) posts are influenced by the displayed bias of previous posts in a thread. This study provides the first direct assessment of the extent to which citizens encounter news and opinion challenging their political views via mass media. The widely accepted conjecture that people refuse to hear the other side is based upon self-reports of media exposure, rather than direct observation of it. In light of this long-acknowledged limitation, I leverage unique data tracking partisanship as well as actual exposure to media collected 24 7 via passive tracking devices. Contrary to previous understandings, the vast majority of citizens consume predominately centrist information, while frequently encountering ideological programming challenging their views. In fact, the best predictor of how much conservative news you watch is how much liberal news you watch, regardless of partisanship. The demonstration of widespread exposure to diverse viewpoints challenges claims asserting that resistance to political influence occurs at the exposure stage of the persuasion process. A review of research suggests that the desire for opinion reinforcement may play a more important role in shaping individuals’ exposure to online political information than an aversion to opinion challenge. The article tests this idea using data collected via a webadministered behavior-tracking study with subjects recruited from the readership of 2 partisan online news sites (N = 727). The results demonstrate that opinion-reinforcing information promotes news story exposure while opinion-challenging information makes exposure only marginally less likely. The influence of both factors is modest, but opinionreinforcing information is a more important predictor. Having decided to view a news story, evidence of an aversion to opinion challenges disappears: There is no evidence that individuals abandon news stories that contain information with which they disagree. Implications and directions for future research are discussed. News consumers expect news outlets to be objective and balanced in their reports of events and opinions. However, there is a growing body of evidence of bias in the media caused by underlying political and socio-economic viewpoints. Previous studies have tried to classify the partiality of the media, but there is little work on quantifying it, and less still on the nature of this partiality. The vast amount of content published in social media enables us to quantify the inclination of the press to pre-defined sides of the socio-political spectrum. To describe such tendencies, we use tweets to automatically compute a news outlet’s political and socio-economic orientation. Results show that the media have a measurable bias, and illustrate this by showing the favoritism of Chilean media for the ruling political parties in the country. This favoritism becomes clearer as we empirically observe a shift in the position of the mass media when there is a change in government. Even though relative differences in bias between news outlets can be observed, public awareness of the bias of the media landscape as a whole appears to be limited by the political space defined by the news that we receive as a population. We found that the nature of the bias is reflected in the vocabulary used and the entities mentioned by different news outlets. A survey conducted among news consumers confirms that media bias has an impact on the coverage of controversial topics and that this is perceivable by the general audience. Having a more accurate method to measure and characterize media bias will help readers position outlets in the socio-economic landscape, even when a (sometimes opposite) self-declared position is stated. This will empower readers to better reflect on the content provided by their news outlets of choice. We hypothesize that in the real world, as opposed to the lab, the norm is for people to experience friendly media that favor their political predispositions when political favoritism is perceived at all. For this reason, media are generally limited in their ability to create cross-cutting exposure. We test this hypothesis using representative survey data drawn from 11 different countries with varying media systems. We further hypothesize that television will contribute more to cross-cutting exposure than newspapers. Finally, and most importantly, we test the hypothesis that the more the structure of a country's media system parallels that of its political parties, the more that country's population will be dominated by exposure to like-minded views via mass media. We find confirmation for all 3 of these hypotheses and discuss their implications for the role of mass media in providing exposure to cross-cutting political perspectives. Social media channels such as Twitter have emerged as platforms for crowds to respond to public and televised events such as speeches and debates. However, the very large volume of responses presents challenges for attempts to extract sense from them. In this work, we present an analytical method based on joint statistical modeling of topical influences from the events and associated Twitter feeds. The model enables the auto-segmentation of the events and the characterization of tweets into two categories: (1) episodic tweets that respond specifically to the content in the segmentsof the events, and (2) steady tweets that respond generally about the events. By applying our method to two large sets of tweets in response to President Obama's speech on the Middle East in May 2011 and a Republican Primary debate in September 2011, we present what these tweets were about. We also reveal the nature and magnitude of the influences of the event on the tweets over its timeline. In a user study, we further show that users find the topics and the episodic tweets discovered by our method to be of higher quality and more interesting as compared to the state-of-the-art, with improvements in the range of 18-41 . Using social media for political discourse is becoming common practice, especially around election time. One interesting aspect of this trend is the possibility of pulsing the public’s opinion about the elections, and that has attracted the interest of many researchers and the press. Allegedly, predicting electoral outcomes from social media data can be feasible and even simple. Positive results have been reported, but without an analysis on what principle enables them. Our work puts to test the purported predictive power of socialmedia metrics against the 2010 US congressional elections. Here, we applied techniques that had reportedly led to positive election predictions in the past, on the Twitter data collected from the 2010 US congressional elections. Unfortunately, we find no correlation between the analysis results and the electoral outcomes, contradicting previous reports. Observing that 80 years of polling research would support our findings, we argue that one should not be accepting predictions about events using social media data as a black box. Instead, scholarly research should be accompanied by a model explaining the predictive power of social media, when there is one. It is widely thought that news organizations exhibit ideological bias, but rigorously quantifying such slant has proven methodologically challenging. Through a combination of machine learning and crowdsourcing techniques, we investigate the selection and framing of political issues in 15 major U.S. news outlets. Starting with 803,146 news stories published over 12 months, we first used supervised learning algorithms to identify the 14 of articles pertaining to political events. We then recruited 749 online human judges to classify a random subset of 10,950 of these political articles according to topic and ideological position. Our analysis yields an ideological ordering of outlets consistent with prior work. We find, however, that news outlets are considerably more similar than generally believed. Specifically, with the exception of political scandals, we find that major news organizations present topics in a largely non-partisan manner, casting neither Democrats nor Republicans in a particularly favorable or unfavorable light. Moreover, again with the exception of political scandals, there is little evidence of systematic differences in story selection, with all major news outlets covering a wide variety of topics with frequency largely unrelated to the outlet's ideological position. Finally, we find that news organizations express their ideological bias not by directly advocating for a preferred political party, but rather by disproportionately criticizing one side, a convention that further moderates overall differences. 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]. Social media have been extensively praised for increasing democratic discussion on social issues related to policy and politics. However, what happens when this powerful communication tools are exploited to manipulate online discussion, to change the public perception of political entities, or even to try affecting the outcome of political elections? In this study we investigated how the presence of social media bots, algorithmically driven entities that on the surface appear as legitimate users, affect political discussion around the 2016 U.S. Presidential election. By leveraging state-of-the-art social bot detection algorithms, we uncovered a large fraction of user population that may not be human, accounting for a significant portion of generated content (about one-fifth of the entire conversation). We inferred political partisanships from hashtag adoption, for both humans and bots, and studied spatio-temporal communication, political support dynamics, and influence mechanisms by discovering the level of network embeddedness of the bots. Our findings suggest that the presence of social media bots can indeed negatively affect democratic political discussion rather than improving it, which in turn can potentially alter public opinion and endanger the integrity of the Presidential election.