Characterizing Political Fake News in Twitter by its Meta-Data
This work addresses the problem of identifying fake news on social media for researchers and policymakers, but it is incremental as it builds on existing meta-data analysis methods.
The study analyzed over 1.5 million tweets from Donald Trump's election day to characterize political fake news on Twitter by examining meta-data differences between viral fake and non-fake tweets, finding significant variations in follower distributions, URL counts, and user verification statuses.
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.