Breaking the news: Extracting the sparse citation network backbone of online news articles

摘要

Networks of online news articles and blog posts are some of the most commonly used data sets in network science. As a result, they have become a vital piece of network analysis and are used for the evaluation of algorithms that work on large networks, or serve as examples in the analysis of information diffusion and propagation. Similarly, scientific citation networks are part of the bedrock upon which much of modern network analysis is built and have been studied for decades. In this paper, we show that the backbone inherent to networks of online news articles shares significant structural similarities to scientific citation networks once the noise of spurious links is stripped away. We present a data set of news articles that, while it is extremely sparse and lightweight, still contains information relevant to the propagation of information in mass media and is remarkably similar to scientific citation networks, thus opening the door to the use of established methodologies from scientometrics and bibliometrics in the analysis of online news propagation.