No Polarization From Partisan News: Over-Time Evidence From Trace Data

Magdalena Wojcieszak,Ke M Huang-Isherwood,Sjifra De Leeuw,Seungsu Lee,Brian Weeks,Ericka Menchen-Trevino

doi:10.1177/19401612211047194

Abstract

Many blame partisan news media for polarization in America. This paper examines the effects of liberal, conservative, and centrist news on affective and attitude polarization. To this end, we rely on two studies that combine two-wave panel surveys (N1 = 303, N2 = 904) with twelve months worth of web browsing data submitted by the same participants comprising roughly thirty-eight million visits. We identify news exposure using an extensive list of news domains and develop a machine learning classifier to identify exposure to political news within these domains. The results offer a robust pattern of null findings. Exposure to partisan and centrist news websites—no matter if it is congenial or crosscutting—does not enhance polarization. These null effects also emerge among strong and weak partisans as well as Democrats and Republicans alike. We argue that these null results accurately portray the reality of limited effects of news in the “real world.” Politics and partisan news account for a small fraction of citizens’ online activities, less than 2 percent in our trace data, and are nearly unnoticeable in the overall information and communication ecology of most individuals.

Highlights

Over the past fifty years, the American public has grown increasingly polarized along party lines (Iyengar et al 2019)
Whereas most scholars focus on partisan media as the key culprit, there are reasons to believe that centrist news outlets can exert polarizing effects (Arceneaux and Johnson 2015), especially among strong partisans, a notion we test
Not aligned with past evidence, we argue that our null findings portray the reality of effects of partisan news in the real world more accurately, as we outline in the discussion

Summary

Introduction

Over the past fifty years, the American public has grown increasingly polarized along party lines (Iyengar et al 2019). Prior to taking the surveys, respondents in both studies shared their browsing history data stored on their computers, resulting in data for over thirty-seven million visits We use those behavioral traces to predict over-time changes in people’s self-reported polarization from exposure to partisan and centrist domains and to explicitly political articles within those domains, as determined by a neural binary classifier, built on top of a large transformerbased language model, BERT. Actual online exposure to liberal and conservative news sites, whether congenial or dissimilar, did not make respondents’ policy attitudes more extreme (i.e., attitude polarization), did not make people more hostile toward out-party supporters (i.e., affective polarization), and exerted no significant polarizing effects among Democrats or Republicans or even strong partisans. The former may thwart consensual governance and leads to disproportionate representation of extreme voices in the political arena (see Mason 2018), while the latter leads partisans to distrust the government run by the opposing party (Hetherington and Rudolph 2015) and influences citizens’ nonpolitical behaviors, biasing decisions in the labor market, for instance (Iyengar et al 2019)

Methods

Results

Conclusion