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
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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... Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... 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. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.