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

TL;DR

This paper introduces a novel method combining graph-based semantic modeling and Hawkes processes to analyze the temporal evolution of news content, providing insights into news coverage dynamics and source identification.

Contribution

It presents a new approach to model news text evolution over time using directed multi-graphs and multivariate Hawkes processes, integrating semantic and temporal information.

Findings

The method captures both publication volume and semantic content changes over time.

It can distinguish between major news outlets and entertainment sources based on news patterns.

The approach offers new insights into the dynamics of news coverage.

Abstract

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.