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