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Open Access

Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks

Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
Suzhou Key Laboratory of Advanced Optical Communication Network Technology, School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
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Abstract

The COVID-19 pandemic has hit the world hard. The reaction to the pandemic related issues has been pouring into social platforms, such as Twitter. Many public officials and governments use Twitter to make policy announcements. People keep close track of the related information and express their concerns about the policies on Twitter. It is beneficial yet challenging to derive important information or knowledge out of such Twitter data. In this paper, we propose a Tripartite Graph Clustering for Pandemic Data Analysis (TGC-PDA) framework that builds on the proposed models and analysis: (1) tripartite graph representation, (2) non-negative matrix factorization with regularization, and (3) sentiment analysis. We collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth data. Our framework can detect the communities of Twitter users and analyze the topics that are discussed in the communities. The extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions, which would provide policy makers useful information and statistics for decision making.

Keywords

COVID-19clusteringonline social networkTwitter

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Big Data Mining and Analytics
Volume 4 Issue 4,
December 2021
Pages 242-251
DOI: 10.26599/BDMA.2021.9020010
Cite this article:
Liao X, Zheng D, Cao X. Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks. Big Data Mining and Analytics, 2021, 4(4): 242-251. https://doi.org/10.26599/BDMA.2021.9020010

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Received: 25 February 2021
Revised: 02 June 2021
Accepted: 04 June 2021
Published: 26 August 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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