Predicting Tomorrow's Headline using Today's Twitter Deliberations
This addresses the challenge of popularity prediction for news articles by leveraging social media data, though it is incremental as it builds on existing content-based methods.
The paper tackles the problem of predicting news article popularity by incorporating user preferences from Twitter discussions, achieving better performance than baseline models on a dataset of 300 political news articles from the New York Post.
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.