How News Evolves? Modeling News Text and Coverage using Graphs and Hawkes Process
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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 from these graphs using discrete-time Hawkes processes. With our real-world data, we show that analyzing the structures of the graphs and the discrete-time Hawkes process model can yield insights on how the news events were covered and how to predict how it may be covered in the future.
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