Spotting Fake News: A Social Argumentation Framework for Scrutinizing Alternative Facts

The proliferation of fake news in today's digital world has moved beyond a specific election cycle and now commands headlines globally. In this paper, we propose countering the spread of fake news on social networks by leveraging these crowds to instead help verify alternative facts. We present a prototype social argumentation framework to verify the validity of proposed alternative facts to help curb the propagation of fake news. We utilize fundamental argumentation ideas in a graph-theoretic framework that also incorporates semantic web and linked data principles. The argumentation structure is crowdsourced and mediated by expert moderators in a virtual community.