"Everything I Disagree With is #FakeNews": Correlating Political Polarization and Spread of Misinformation

TL;DR

This study investigates how political polarization influences the perception and spread of misinformation on Twitter, revealing that polarized users are more likely to label disagreeable content as fake news.

Contribution

It provides empirical evidence linking political polarization to the classification of misinformation, highlighting polarization's role in misinformation dissemination.

Findings

Increased polarization correlates with labeling more content as fake news.

Users tend to perceive disagreeable information as fake.

Polarized URLs are more associated with fake-news hashtags.

Abstract

An important challenge in the process of tracking and detecting the dissemination of misinformation is to understand the political gap between people that engage with the so called "fake news". A possible factor responsible for this gap is opinion polarization, which may prompt the general public to classify content that they disagree or want to discredit as fake. In this work, we study the relationship between political polarization and content reported by Twitter users as related to "fake news". We investigate how polarization may create distinct narratives on what misinformation actually is. We perform our study based on two datasets collected from Twitter. The first dataset contains tweets about US politics in general, from which we compute the degree of polarization of each user towards the Republican and Democratic Party. In the second dataset, we collect tweets and URLs that co-occurred with "fake news" related keywords and hashtags, such as #FakeNews and #AlternativeFact, as well as reactions towards such tweets and URLs. We then analyze the relationship between polarization and what is perceived as misinformation, and whether users are designating information that they disagree as fake. Our results show an increase in the polarization of users and URLs associated with fake-news keywords and hashtags, when compared to information not labeled as "fake news". We discuss the impact of our findings on the challenges of tracking "fake news" in the ongoing battle against misinformation.