Abstract: The impacts of the Internet and the ability for information to flow in real-time to all corners of the globe has brought many benefits to society. How-ever, this capability has downsides. Information can be inexact, misleading or indeed downright and deliberately false. Fake news has now entered the common vernacular. In this work, we consider fake news with specific regard to social media. We hypothesise that fake news typically deals with emotive topics that are deliberated targeted to cause a reaction and encourages the spread of information. As such, we explore sentiment analysis of real and fake news as reported in social networks (Twitter). Specifically, we develop an AWS-based Cloud platform utilising news contained in the untrustworthy resource FakeNewsNet and a more trusted resource CredBank. We train algorithms using Naive Bayes, Decision Tree and Bi-LSTM for sentiment classification and feature selection. We show how social media sentiment can be used to improve the accuracy in identification of fake news from real news.
Authors: Richard Sinnott (University of Melbourne, Australia)