2017/12/16 by Julio Amador, Amador, Julio, Axel Oehmichen +3
Computer Science · Mathematics · Physics and Astronomy · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Misinformation and Its Impacts #Opinion Dynamics and Social Influence #Social Media and Politics #Social and Information Networks (cs.SI) #cs.CL #cs.SI #stat.ML
paper · pdf · doi:10.48550/arxiv.1712.05999
arxiv created 2017/12/16 · openalex publication_date 2017/12/16 · arxiv updated 2017/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.