Between article and topic: news events as level of analysis and their computational identification

When comparing media coverage or analysing which content people are exposed to, researchers need to abstract from individual articles. At the same time, aggregating them into broad topics or issues often is too coarse and loses nuance. Both theoretically and methodologically, the analysis of an appropriate intermediate level of aggregation is underdeveloped. This article advances research in various areas of journalism studies by developing a theoretical argument for introducing the “news event” as a level of analysis. Based on this, we discuss several computational approaches to empirically detect such news events in large corpora of news coverage in an unsupervised manner. We provide two approaches: One based on traditional tf·idf based cosine similarities, and one that relies on word embeddings, in particular the softcosine measure. Both methods, combined with a network clustering algorithm, perform very well in detecting news events. We apply this method in a case study of 45k news articles from different outlets, in which we show that different news outlets have distinct profiles in the events they cover.