Misinformation in Social Media Platforms and Web Articles: a Dataset to Infer User Stance

Bhashithe Abeysinghe, Gyandeep Reddy Vulupala, Rajshekhar Sunderraman

Abstract: Social media is filled with news articles, interesting stories and entertainment news. It is a given that along with most of these legitimate stories and articles some of them may also be misinformation. The problem of detecting misinformation and identifying whether an article title misleads readers has been studied. However, work related to user's stance of a shared article has not been explored much. This paper tries to fill that gap by introducing a public dataset which includes users text contribution, article title, article content and a link to the article.