Predicting Tomorrow's Headline using Today's Twitter Deliberations

TL;DR

This paper introduces a model that leverages Twitter discussions to predict the popularity of news articles, emphasizing user involvement and reactions to improve prediction accuracy.

Contribution

It is the first to incorporate Twitter user engagement and reactions into news popularity prediction, addressing a gap in existing content-focused models.

Findings

Model outperforms baseline approaches on political news data

Incorporating user reactions improves prediction accuracy

Effective in predicting tomorrow's headlines based on today's Twitter data

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.