Hawkes process marked with topics and its application to Twitter data analysis

Abstract

On Twitter, complex networks of information propagation are observed. In this article, we propose a method to model the time interval of user's tweets by combining the marked Hawkes process and the Latent Dirichlet Allocation method. We also propose a method that quantifies how individual topics in tweets excite future tweets and visualize the results as a topic-wise Hawkes graph. As an application to actual data, we analyze the tweets from the American, Chinese, and British embassies from February 1, 2020, to September 27, 2020, a period that roughly corresponds to the initial outbreak of the COVID-19 pandemic.