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.
Power dynamics in debates and other types of coverage. Studies have shown that language use and topic control in debates can reflect influence between candidates and indicate power dynamics @cite_30 @cite_23 @cite_25 . More recently, social media have also become an important channel to monitor public opinion on debates in real time @cite_0 @cite_8 and potentially change news media coverage.
@cite_30 @cite_8 @cite_0 @cite_23 @cite_25
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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... 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. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. 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.