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.
Influences between the media and politicians. Although our work focuses on media selection of highlights, politicians often behave based on their beliefs about media preferences, which suggests complex dynamics between the media and politicians @cite_33 @cite_43 @cite_28 @cite_22 @cite_19 . For instance, discuss politicians' increasing adaptation to different news values and formats in the presence of media abundance. Also relevant is research on the influence of politicians on the media, including agenda-setting, rhetorical positioning, and framing @cite_3 @cite_7 @cite_4 @cite_13 @cite_5 .
@cite_4 @cite_22 @cite_33 @cite_7 @cite_28 @cite_3 @cite_43 @cite_19 @cite_5 @cite_13
1128822470 2788268216 1968917437 2112687664 2166273866 2396708059 2793539342 2101865658 2483704788 2129229917 1904709910 1976267568 1995998136 2077742072 1119807432 153654188 2095655043 2039222427 2079373549 91442942
For over a generation, social scientists have tried to categorize the relationship between journalists and politicians. Which side holds power and influence over the other? Some scholars propose “active” theories: journalists have preferences and the power to impose them on anyone seeking media attention. Other scholars argue journalists are essentially “reactive,” dutifully writing down what politicians say with little ability to add alternate perspectives. In this dissertation, I propose both camps are extremes based on a fundamental misunderstanding of how journalists can apply their preferences on news content. Politicians and other sources provide information to reporters, bloggers and other new media writers. Each writer then chooses how to respond to this information. Journalistic power – whether we are discussing traditional media outlets or newer partisan media organizations – is best understood as a set of if : then propositions.The empirical sections of the dissertation consist of three separate studies, each of which focuses on one set of inputs and the output from a particular set of news organizations. The first study focuses on how presidents schedule press conferences at particular times and places. I find scheduling influences how much attention journalists give a conference, which in turn influences the balance of opinion found in stories. The second study shows how journalists resist but may ultimately give in to evasive responses, by examining quotations on a statement-by-statement basis. The third study examines some of the most popular phrases from the 2008 election, comparing how a wide range of media organizations responded to the same set of political and non-political ideas. Put together, these studies offer a common theoretical framework for comparing traditional and new media organizations, allowing for commonalities as well as differences. 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. 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. 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. 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. Social media play an increasingly important part in the communication strategies of political campaigns by reflecting information about the policy preferences and opinions of political actors and their public followers. In addition, the content of the messages provides rich information about the political issues and the framing of those issues during elections, such as whether contested issues concern Europe or rather extend pre-existing national debates. In this study, we survey the European landscape of social media using tweets originating from and referring to political actors during the 2014 European Parliament election campaign. We describe the language and national distribution of the messages, the relative volume of different types of communications, and the factors that determine the adoption and use of social media by the candidates. We also analyze the dynamics of the volume and content of the communications over the duration of the campaign with reference to both the EU integration dimension of the debate and the prominence of the most visible list-leading candidates. Our findings indicate that the lead candidates and their televised debate had a prominent influence on the volume and content of communications, and that the content and emotional tone of communications more reflects preferences along the EU dimension of political contestation rather than classic national issues relating to left-right differences. 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. This article examines the way in which the news media frame public policy issues and the extent to which other political players (e.g., interest groups, politicians) influence this issue framing process. Our analysis focuses on the issue of gun control, comparing the rhetoric generated by interest groups and public officials on the Brady Bill and Assault Weapons Ban with actual network news coverage of this legislation from 1988 to 1996. Results indicate that both sets of political players employed several interpretative issue frames and worked hard to put their preferred themes on the agenda. However, at times, the media intervened in the framing process, especially as the debate matured. Specifically, the news media (a) structured the overall tone of the gun control debate, (b) adopted a distribution of framing perspectives different from that of politicians and interest groups, and (c) packaged policy discourse more often than not in terms of the "culture of violence" theme. These findings point toward... Abstract Propagation of political ideologies in social networks has shown a substantial impact on voting behavior. Both the contents of the messages (the ideology) and the politicians' influence on their online audiences (their followers) have been associated with such an impact. In this study we evaluate which of these factors exerted a major role in deciding electoral results of the 2015 Colombian regional elections by evaluating the linguistic similarity of political ideologies and their influence on the Twitter sphere. The electoral results proved to be strongly associated with tweets and retweets and not with the linguistic content of their ideologies or politicians' followers in Twitter. Finally, suggestions for new ways to analyze electoral processes are discussed. 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. Is social media a valid indicator of political behavior? There is considerable debate about the validity of data extracted from social media for studying offline behavior. To address this issue, we show that there is a statistically significant association between tweets that mention a candidate for the U.S. House of Representatives and his or her subsequent electoral performance. We demonstrate this result with an analysis of 542,969 tweets mentioning candidates selected from a random sample of 3,570,054,618, as well as Federal Election Commission data from 795 competitive races in the 2010 and 2012 U.S. congressional elections. This finding persists even when controlling for incumbency, district partisanship, media coverage of the race, time, and demographic variables such as the district's racial and gender composition. Our findings show that reliable data about political behavior can be extracted from social media. 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... This article identifies key changes in society and the media that have shaped political communication in many democracies over the postwar period. Three distinct ages are described. In the first, much political communication was subordinate to relatively strong and stable political institutions and beliefs. In the second, faced with a more mobile electorate, the parties increasingly "professionalized" and adapted their communications to the news values and formats of limited-channel television. In the third (still emerging) age of media abundance, political communication may be reshaped by five trends: intensified professionalizing imperatives, increased competitive pressures, anti-elitist populism, a process of "centrifugal diversification," and changes in how people receive politics. This system is full of tensions, sets new research priorities, and reopens long-standing issues of democratic theory. 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. 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. Politics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods—they are no substitute for careful thought and close reading and require extensive and problem-specific validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation. Language is the medium for politics and political conflict. Candidates debate and state policy positions during a campaign. Once elected, representatives write and debate legislation. After laws are passed, bureaucrats solicit comments before they issue regulations. Nations regularly negotiate and then sign peace treaties, with language that signals the motivations and relative power of the countries involved. News reports document the day-to-day affairs of international relations that provide a detailed picture of conflict and cooperation. Individual candidates and political parties articulate their views through party platforms and manifestos. Terrorist groups even reveal their preferences and goals through recruiting materials, magazines, and public statements. These examples, and many others throughout political science, show that to understand what politics is about we need to know what political actors are saying and writing. Recognizing that language is central to the study of politics is not new. To the contrary, scholars of politics have long recognized that much of politics is expressed in words. But scholars have struggled when using texts to make inferences about politics. The primary problem is volume: there are simply too many political texts. Rarely are scholars able to manually read all the texts in even moderately sized corpora. And hiring coders to manually read all documents is still very expensive. The result is that Display Omitted How 2.3 Facebook users consumed different information.Qualitatively different information is consumed in a similar way.Users more prone to interact with false claims are usually exposed to conspiracy rumors. In this work we study, on a sample of 2.3million individuals, how Facebook users consumed different information at the edge of political discussion and news during the last Italian electoral competition. Pages are categorized, according to their topics and the communities of interests they pertain to, in (a) alternative information sources (diffusing topics that are neglected by science and main stream media); (b) online political activism; and (c) main stream media. We show that attention patterns are similar despite the different qualitative nature of the information, meaning that unsubstantiated claims (mainly conspiracy theories) reverberate for as long as other information. Finally, we classify users according to their interaction patterns among the different topics and measure how they responded to the injection of 2788 false information. Our analysis reveals that users which are prominently interacting with conspiracists information sources are more prone to interact with intentional false claims. Transmitting messages in the most efficient way as possible has always been one of politicians’ main concerns during electoral processes. Due to the rapidly growing number of users, online social networks have become ideal platforms for politicians to interact with their potential voters. Exploiting the available potential of these tools to maximize their influence over voters is one of politicians’ actual challenges. To step in this direction, we have analyzed the user activity in the online social network Twitter, during the 2011 Spanish Presidential electoral process, and found that such activity is correlated with the election results. We introduce a new measure to study political sentiment in Twitter, which we call the relative support. We have also characterized user behavior by analyzing the structural and dynamical patterns of the complex networks emergent from the mention and retweet networks. Our results suggest that the collective attention is driven by a very small fraction of users. Furthermo... In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.