Volume 140, Number 6, December 2022
|Number of page(s)||5|
|Section||Statistical physics and networks|
|Published online||13 December 2022|
Hawkes process marked with topics and its application to Twitter data analysis
1 Graduate School of Mathematical Sciences, University of Tokyo - 3-8-1, Komaba, Meguro-ku, Tokyo, 153-0041, Japan
2 Japan Science and Technology, CREST - Kawaguchi, Japan
3 National Institute of Informatics - 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan
4 Tokio Marine and Nichido Risk Consulting Co. Ltd. - 1-5-1, Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan
(a) E-mail: firstname.lastname@example.org (corresponding author)
Received: 4 August 2022
Accepted: 30 November 2022
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.
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