Comparison of news commonality and churn in international news outlets with TARO

Abstract

The past decades have seen an increase in academic research and public debates on online news and journalism in general, with an emphasis on fake news and low-quality reporting. This paper presents TARO: a model and a software framework for the collection and analysis of online news sources. The novel aspects of the TARO model and framework are: the distinction between abstract pieces of news and concrete news items, news comparison techniques based on similarity on embedded spaces, and the management of rolling news via so-called snapshot extensions. One advantage of TARO is the ability to perform comparative analysis of international news sources in various languages and across time zones. To prove the applicability and soundness of TARO, two quantitative cases studies related to the concept of churnalism are also presented in this paper. The two case studies provide quantitative insights on two tendencies of news outlets: news commonality (publishing the same news) and news churn (quickly removing recent news to make space for even more recent news).