Citizen Investigators

Abstract

Fake news and alternative facts have dominated the news cycle of late. We have developed a usabiliy prototype system that uses social argumentation to verify the validity of proposed alternative facts and help in the detection 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.

Introduction

The phenomenon of fake news and the rise of "alternative facts" have dominated the news cycle of late. Although these terms are new, reliance upon propaganda and misinformation predates the Internet, not just in politics but in communication exchange in general. Critical thinking and evidence-based reasoning are essential for countering propaganda and misinformation intended to manipulate public opinion.

Computational approaches for addressing fake news have so far focused mainly on automated tools. These tools flag previously identified hoaxes; or automatically detect fake news articles using natural language processing techniques with pre-existing ground truth; or track the viral-like transmission of hoaxes. None of the existing approaches, however, deal with verification of the alternative facts which constitute the semantic content of such articles.

In such cases, argumentation has been shown to be a natural, substantiated approach for analyzing the veracity and reliability of assertions and claims. In fact, in considering how to assess critical thinking, some assert the need to identify conclusions, reasons, and assumptions as well as judging the quality of arguments and developing positions on an issue. Using this sort of evidence based reasoning not only has the potential to identify fake news to a greater extent but also to imbibe users with the critical thinking ability to navigate future fake news articles