Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks
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 conc...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2021-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2021.9020010 |
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| author | Xueting Liao Danyang Zheng Xiaojun Cao |
| author_facet | Xueting Liao Danyang Zheng Xiaojun Cao |
| author_sort | Xueting Liao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cccb8f80e54046feb34bb1b59b2eab68 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-cccb8f80e54046feb34bb1b59b2eab682025-02-02T06:50:33ZengTsinghua University PressBig Data Mining and Analytics2096-06542021-12-014424225110.26599/BDMA.2021.9020010Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social NetworksXueting Liao0Danyang Zheng1Xiaojun Cao2<institution content-type="dept">Department of Computer Science</institution>, <institution>Georgia State University</institution>, <city>Atlanta</city>, <state>GA</state> <postal-code>30302</postal-code>, <country>USA</country><institution>Suzhou Key Laboratory of Advanced Optical Communication Network Technology, School of Electronic and Information Engineering, Soochow University</institution>, <city>Suzhou</city> <postal-code>215006</postal-code>, <country>China</country><institution content-type="dept">Department of Computer Science</institution>, <institution>Georgia State University</institution>, <city>Atlanta</city>, <state>GA</state> <postal-code>30302</postal-code>, <country>USA</country>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.https://www.sciopen.com/article/10.26599/BDMA.2021.9020010covid-19clusteringonline social networktwitter |
| spellingShingle | Xueting Liao Danyang Zheng Xiaojun Cao Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks Big Data Mining and Analytics covid-19 clustering online social network |
| title | Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks |
| title_full | Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks |
| title_fullStr | Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks |
| title_full_unstemmed | Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks |
| title_short | Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks |
| title_sort | coronavirus pandemic analysis through tripartite graph clustering in online social networks |
| topic | covid-19 clustering online social network |
| url | https://www.sciopen.com/article/10.26599/BDMA.2021.9020010 |
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