Modeling and Predicting News Consumption on Twitter

While much is known about how people tweet and interact on Twitter, surprisingly little is known about how the news items tweeted by journalists -- news tweets -- act as a distribution channel for the news that is spread by social media reading and sharing. This paper aims to fill this gap by analyzing the dynamics of news on Twitter, by revealing what drives users to consume news, and by developing a news consumption prediction model. We present the Twitter News Model (TNM), a computational data-driven approach to elucidate the dynamics of news consumption on Twitter. We apply the TNM to a dataset of interactions between users and journalists/newspapers to reveal what drives users' consumption of news on Twitter, and predictively relate users' news beliefs, motivations, and attitudes to their consumption of news. Our findings reveal that news motivations, followed by news attitudes and news beliefs, impact users' behavior of news consumption on Twitter.