Same Words, Different Meanings: Semantic Polarization in Broadcast Media Language Forecasts Polarization on Social Media Discourse
Xiaohan Ding, Mike Horning, Eugenia H. Rho

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…
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Taxonomy
TopicsSocial Media and Politics · Sociopolitical Dynamics in Russia · Opinion Dynamics and Social Influence
