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.

本研究分析近十年来 CNN 和福克斯新闻的关闭字幕及 Twitter 上语篇信息，探讨语言表述在电视和社交媒体之间的关系，研究结果表明不同播出平台的语义极化现象与趋势出现了较大的差异，尤其是 2016 年之后，两个频道的讨论话题在语义上的差异越来越显著，这种语言上的分化在很大程度上塑造了 Twitter 上的语义互动。