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

TL;DR

This study investigates how semantic polarization between CNN and Fox News has evolved over a decade and how it influences partisan discourse on Twitter, revealing increasing divergence especially after 2016 and 2020.

Contribution

It introduces a novel framework for measuring semantic polarization across traditional broadcast media and social media, linking televised language to online partisan discussions.

Findings

Semantic polarization increased sharply after 2016.

2020 saw the highest peaks in linguistic divergence.

Broadcast media language significantly influences Twitter discourse.

Abstract

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.