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With the growth of online news over the past decade, empirical studies onpolitical discourse and news consumption have focused on the phenomenon offilter bubbles and echo chambers. Yet recently, scholars have revealed limitedevidence around the impact of such phenomenon, leading some to argue thatpartisan segregation across news audiences cannot be fully explained by onlinenews consumption alone and that the role of traditional legacy media may be assalient in polarizing public discourse around current events. In this work, weexpand the scope of analysis to include both online and more traditional mediaby investigating the relationship between broadcast news media language andsocial media discourse. By analyzing a decade's worth of closed captions (2million speaker turns) from CNN and Fox News along with topically correspondingdiscourse from Twitter, we provide a novel framework for measuring semanticpolarization between America's two major broadcast networks to demonstrate howsemantic polarization between these outlets has evolved (Study 1), peaked(Study 2) and influenced partisan discussions on Twitter (Study 3) across thelast decade. Our results demonstrate a sharp increase in polarization in howtopically important keywords are discussed between the two channels, especiallyafter 2016, with overall highest peaks occurring in 2020. The two stationsdiscuss identical topics in drastically distinct contexts in 2020, to theextent that there is barely any linguistic overlap in how identical keywordsare contextually discussed. Further, we demonstrate at scale, how such partisandivision in broadcast media language significantly shapes semantic polaritytrends on Twitter (and vice-versa), empirically linking for the first time, howonline discussions are influenced by televised media.

Same Words, Different Meanings: Semantic Polarization in Broadcast Media Language Forecasts Polarization on Social Media Discourse