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Monitoring news content automatically is an important problem. The newscontent, unlike traditional text, has a temporal component. However, few workshave explored the combination of natural language processing and dynamic systemmodels. One reason is that it is challenging to mathematically model thenuances of natural language. In this paper, we discuss how we built a noveldataset of news articles collected over time. Then, we present a method ofconverting news text collected over time to a sequence of directedmulti-graphs, which represent semantic triples (Subject -> Predicate}->Object). We model the dynamics of specific topological changes in thesegraphs using a set of multivariate count series, which we fit the discrete-timeHawkes process. With our real-world data, we show that the multivariate timeseries contain both dynamic information of how many articles/words werepublished each day and semantic information of the content of the articles.This yields novel insights into how news events are covered. We show with theexperiment that our approach can be used to infer from a sequence of newsarticles if the articles were published by major or entertainment news outlets.

How News Evolves? Modeling News Text and Coverage using Graphs and Hawkes Process