Title: Predicting Tomorrow's Headline using Today's Twitter Deliberations,
Abstract: Predicting the popularity of news article is a challenging task. Existing
literature mostly focused on article contents and polarity to predict
popularity. However, existing research has not considered the users' preference
towards a particular article. Understanding users' preference is an important
aspect for predicting the popularity of news articles. Hence, we consider the
social media data, from the Twitter platform, to address this research gap. In
our proposed model, we have considered the users' involvement as well as the
users' reaction towards an article to predict the popularity of the article. In
short, we are predicting tomorrow's headline by probing today's Twitter
discussion. We have considered 300 political news article from the New York
Post, and our proposed approach has outperformed other baseline models.