  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.
