  This article presents a preliminary approach towards characterizing political
fake news on Twitter through the analysis of their meta-data. In particular, we
focus on more than 1.5M tweets collected on the day of the election of Donald
Trump as 45th president of the United States of America. We use the meta-data
embedded within those tweets in order to look for differences between tweets
containing fake news and tweets not containing them. Specifically, we perform
our analysis only on tweets that went viral, by studying proxies for users'
exposure to the tweets, by characterizing accounts spreading fake news, and by
looking at their polarization. We found significant differences on the
distribution of followers, the number of URLs on tweets, and the verification
of the users.
