Sentiment Aware Fake News Detection on Online Social Networks

Messages posted to online social networks (OSN) causes a recent stir due to the intended spread of fake news or rumor. This work aims to understand and analyse the characteristics of fake news especially in relation to sentiments, for the automatic detection of fake news and rumors. Based on empirical observations, we propose a hypothesis that there exists a relation between fake messages or rumours and sentiments of the texts posted online. We verify our hypothesis by comparing with the state-of-the-art baseline text-only fake news detection methods that do not consider sentiments. We performed experiments on standard Twitter fake news dataset and show good improvements in detecting fake news or rumor posts.