On the Origin, Proliferation and Tone of Fake News

Since the advent of social media, we have turned towards consuming news from stand-alone websites to popular social media sites (i.e. Facebook, Twitter, Reddit, etc.); we have noticed a growing number of fake news articles spread across the internet. Existing methods for fake news detection mainly focus on natural language processing and machine learning models to analyze the legitimacy of the news content in order to detect whether it is legit or fake. Currently, there are not many approaches aimed at testing, validating, and ideally refining the findings from traditional fake news detection literature as obtained via surveys and understanding how fake news originate and spread in first place. This paper presents three crucial hypotheses studies that are derived from analyses like, 1) media outlets that publish fake news (origin), 2) social media users who post or share fake news (proliferation), and 3) linguistic (tone) in which fake news are written. The hypotheses are tested on two real-world datasets and results are provided. We envision that this study paves the way to design and develop new multifarious fusion models to detect fake news.