text: 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.
  @cite_30: One of the key tasks for analyzing conversational data is segmenting it into coherent topic segments. However, most models of topic segmentation ignore the social aspect of conversations, focusing only on the words used. We introduce a hierarchical Bayesian nonparametric model, Speaker Identity for Topic Segmentation (SITS), that discovers (1) the topics used in a conversation, (2) how these topics are shared across conversations, (3) when these topics shift, and (4) a person-specific tendency to introduce new topics. We evaluate against current unsupervised segmentation models to show that including person-specific information improves segmentation performance on meeting corpora and on political debates. Moreover, we provide evidence that SITS captures an individual's tendency to introduce new topics in political contexts, via analysis of the 2008 US presidential debates and the television program Crossfire.
  @cite_8: Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodology and visual representations that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events.
  @cite_0: While the newspaper industry is in crisis and less time and resources are available for newsgathering, social media turn out to be a convenient and cheap beat for (political) journalism. This article investigates the use of Twitter as a source for newspaper coverage of the 2010 British and Dutch elections. Almost a quarter of the British and nearly half of the Dutch candidates shared their thoughts, visions, and experiences on Twitter. Subsequently, these tweets were increasingly quoted in newspaper coverage. We present a typology of the functions tweets have in news reports: they were either considered newsworthy as such, were a reason for further reporting, or were used to illustrate a broader news story. Consequently, we will show why politicians were successful in producing quotable tweets. While this paper, which is part of a broader project on how journalists (and politicians) use Twitter, focuses upon the coverage of election campaigns, our results indicate a broader trend in journalism. In the future, the reporter who attends events, gathers information face-to-face, and asks critical questions might instead aggregate information online and reproduce it in journalism discourse thereby altering the balance of power between journalists and sources.
  @cite_23: Identifying influential speakers in multi-party conversations has been the focus of research in communication, sociology, and psychology for decades. It has been long acknowledged qualitatively that controlling the topic of a conversation is a sign of influence. To capture who introduces new topics in conversations, we introduce SITS--Speaker Identity for Topic Segmentation--a nonparametric hierarchical Bayesian model that is capable of discovering (1) the topics used in a set of conversations, (2) how these topics are shared across conversations, (3) when these topics change during conversations, and (4) a speaker-specific measure of "topic control". We validate the model via evaluations using multiple datasets, including work meetings, online discussions, and political debates. Experimental results confirm the effectiveness of SITS in both intrinsic and extrinsic evaluations.
  @cite_25: We study the topic dynamics of interactions in political debates using the 2012 Republican presidential primary debates as data. We show that the tendency of candidates to shift topics changes over the course of the election campaign, and that it is correlated with their relative power. We also show that our topic shift features help predict candidates’ relative rankings.
summary: 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.