With the growth of online news over the past decade, empirical studies on political discourse and news consumption have focused on the phenomenon of filter bubbles and echo chambers. Yet recently, scholars have revealed limited evidence around the impact of such phenomenon, leading some to argue that partisan segregation across news audiences cannot be fully explained by online news consumption alone and that the role of traditional legacy media may be as salient in polarizing public discourse around current events. In this work, we expand the scope of analysis to include both online and more traditional media by investigating the relationship between broadcast news media language and social media discourse. By analyzing a decade's worth of closed captions (2 million speaker turns) from CNN and Fox News along with topically corresponding discourse from Twitter, we provide a novel framework for measuring semantic polarization between America's two major broadcast networks to demonstrate how semantic polarization between these outlets has evolved (Study 1), peaked (Study 2) and influenced partisan discussions on Twitter (Study 3) across the last decade. Our results demonstrate a sharp increase in polarization in how topically important keywords are discussed between the two channels, especially after 2016, with overall highest peaks occurring in 2020. The two stations discuss identical topics in drastically distinct contexts in 2020, to the extent that there is barely any linguistic overlap in how identical keywords are contextually discussed. Further, we demonstrate at scale, how such partisan division in broadcast media language significantly shapes semantic polarity trends on Twitter (and vice-versa), empirically linking for the first time, how online discussions are influenced by televised media.
翻译:随着过去十年在线新闻的增长,关于政治话语和新闻消费的实证研究侧重于过滤泡沫和回声室的现象。然而,最近,学者们就这一现象的影响揭示了有限的证据。然而,最近,学者们就这一现象的影响揭示了有限的证据,导致一些人争辩说,光靠在线新闻消费并不能充分解释新闻受众之间的党派隔离,传统遗留媒体的作用可能同样突出地使公众关于当前事件的言论两极分化。在这项工作中,我们扩大了分析范围,通过调查广播媒体语言和社交媒体对话之间的关系,将在线媒体和传统媒体都包括进来。通过分析CNN和Fox新闻10年来的闭路标题(200万个发言者转转转转)以及Twitter的对主题相应言论,我们为衡量美国两大主要广播网络之间的语义两极分化情况提供了一个新的框架,以展示这些媒体之间的语义两极分化在过去十年中是如何演化的(图解 3) 。通过我们的结果显示,两条频道之间对主题的重要关键词词词库的讨论急剧增加,特别是在2016年之后,总体而言,在2020年的最高时段里,我们之间是如何呈现出一个截然不同的。