News Sharing User Behaviour on Twitter: A Comprehensive Data Collection of News Articles and Social Interactions

Abstract

Online social media are changing the news industry and revolutionizing the traditional role of journalists and newspapers. In this scenario, investigating the behaviour of users in relationship to news sharing is relevant, as it provides means for understanding the impact of online news, their propagation within social communities, their impact on the formation of opinions, and also for effectively detecting individual stances relative to specific news or topics.

Our contribution is two-fold. First, we build a robust pipeline for collecting datasets describing news sharing; the pipeline takes as input a list of news sources and generates a large collection of articles, of the accounts that provide them on the social media either directly or by retweeting, and of the social activities performed by these accounts. Second, we also provide a large-scale dataset, built using the aforementioned tool, that can be used to study the social behavior of Twitter users and their involvement in the dissemination of news items. Finally we show an application of our data collection in the context of political stance classification and we suggest other potential usages of the presented resources.