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Big Data Mining and Analytics 2021, 4(4): 242-251 https://doi.org/10.26599/BDMA.2021.9020010
Open Access | Issue | Published: 26 August 2021

Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks

Show Author's Information Xueting Liao,  Danyang Zheng( ),  Xiaojun Cao
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
Keywords:
COVID-19, clustering, Twitter, online social network
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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Abstract Full text About this article

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.


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About this article

Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks

Show Author's information Xueting Liao, Danyang Zheng( ), Xiaojun Cao
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

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-19, clustering, Twitter, online social network

References(45)

[1]
Everyone included: Social impact of COVID-19, https://www.un.org/development/desa/dspd/everyone-included-covid-19.html, 2020.
DOI
[2]
Wikipedia, COVID-19 pandemic, https://en.wikipedia.org/wiki/COVID-19pandemic, 2021.
[3]
Domestic travel during the COVID-19 pandemic, https://www.cdc.gov/coronavirus/2019-ncov/travelers/travel-during-covid19.html, 2020.
[4]
Travelers prohibited from entry to the United States, https://www.cdc.gov/coronavirus/2019-ncov/travelers/from-other-countries.html, 2020.
[5]
K. Cohen, Tokyo 2020 Olympics officially postponed until 2021, https://tv5.espn.com/olympics/story/_/id/28946033/tokyo-olympics-officially-postponed-2021, 2020.
[6]
Wikipedia, RNA virus, https://en.wikipedia.org/wiki/RNAvirus, 2021.
[7]
How does fake news of 5G and COVID-19 spread worldwide?, https://www.medicalnewstoday.com/articles/5g-doesnt-cause-covid-19-but-the-rumor-it-does-spread-like-a-virus, 2021.
[8]
L. J. Chang, W. Li, L. Qin, W. J. Zhang, and S. Y. Yang, pSCAN: Fast and exact structural graph clustering, IEEE Trans. Knowl. Data Eng., vol. 29, no. 2, pp. 387-401, 2017.
DOI Google Scholar
[9]
R. El Bacha and T. T. Zin, Ranking of influential users based on user-tweet bipartite graph, in Proc. of 2018 IEEE Int. Conf. Service Operations and Logistics, and Informatics (SOLI), Singapore, 2018, pp. 97-101.
DOI
[10]
A. Rodríguez, C. Argueta, and Y. L. Chen, Automatic detection of hate speech on facebook using sentiment and emotion analysis, in Proc. of 2019 Int. Conf. Artificial Intelligence in Information and Communication (ICAIIC), Okinawa, Japan, 2019, pp. 169-174.
DOI
[11]
J. Zhou and C. Kwan, Missing link prediction in social networks, in Proc. 15th Int. Symp. Neural Networks, Minsk, Belarus, 2018, pp. 346-354.
DOI
[12]
A. Reyes-Menendez, J. R. Saura, and C. Alvarez-Alonso, Understanding #worldEnvironmentDay user opinions in twitter: A topic-based sentiment analysis approach, Int. J. Environ. Res. Public Health, vol. 15, no. 11, p. 2537, 2018.
DOI Google Scholar
[13]
C. H. Tan, L. L. Lee, J. Tang, L. Jiang, M. Zhou, and P. Li, User-level sentiment analysis incorporating social networks, in Proc. 17th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, New York, NY, USA, 2011, pp. 1397-1405.
DOI
[14]
A. Giachanou and F. Crestani, Like it or not: A survey of twitter sentiment analysis methods, ACM Comput. Surv., vol. 49, no. 2, p. 28, 2016.
DOI Google Scholar
[15]
R. R. Iyer, J. Chen, H. N. Sun, and K. Y. Xu, A heterogeneous graphical model to understand user-level sentiments in social media, arXiv preprint arXiv: 1912.07911, 2019.
Google Scholar
[16]
H. B. Deng, J. W. Han, H. Li, H. Ji, H. N. Wang, and Y. Lu, Exploring and inferring user-user pseudo-friendship for sentiment analysis with heterogeneous networks, Stat. Anal. Data Min., vol. 7, no. 4, pp. 308-321, 2014.
DOI Google Scholar
[17]
C. A. Phillips, Multipartite graph algorithms for the analysis of heterogeneous data, PhD dissertation, Univ. Tennessee, Knoxville, TN, USA, 2015.
[18]
D. W. Zhou, S. Zhang, M. Y. Yildirim, S. Alcorn, H. H. Tong, H. Davulcu, and J. R. He, A local algorithm for structure-preserving graph cut, in Proc. 23rd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Halifax, Canada, 2017, pp. 655-664.
DOI
[19]
P. M. Comar, P. N. Tan, and A. K. Jain, A framework for joint community detection across multiple related networks, Neurocomputing, vol. 76, no. 1, pp. 93-104, 2012.
DOI Google Scholar
[20]
Y. Z. Sun, Y. T. Yu, and J. W. Han, Ranking-based clustering of heterogeneous information networks with star network schema, in Proc. 15th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Paris, France, 2009, pp. 797-806.
DOI
[21]
D. D. Lee and H. S. Seung, Algorithms for non-negative matrix factorization, in Proc. 13th Int. Conf. Neural Information Proc. Systems, Cambridge, MA, USA, 2001, pp. 535-541.
[22]
N. Gillis, The why and how of nonnegative matrix factorization, arXiv preprint arXiv: 1401.5226v2, 2014.
Google Scholar
[23]
H. Abdi and L. J. Williams, Principal component analysis, WIRs Comput. Stat., vol. 2, no. 4, pp. 433-459, 2010.
DOI Google Scholar
[24]
M. E. Wall, A. Rechtsteiner, and L. M. Rocha, Singular value decomposition and principal component analysis, in A Practical Approach to Microarray Data Analysis, D. P. Berrar, W. Dubitzky, M. Granzow, eds. Norwell, MA, USA: Springer, 2003, pp. 91-109.
[25]
C. Ding, T. Li, W. Peng, and H. Park, Orthogonal nonnegative matrix t-factorizations for clustering, in Proc. 12th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Philadelphia, PA, USA, 2006, pp. 126-135.
DOI
[26]
D. Kim, S. Sra, and I. S. Dhillon, Fast newton-type methods for the least squares nonnegative matrix approximation problem, in Proc. 2007 SIAM Int. Conf. Data Mining, Minneapolis, MN, USA, 2007, pp. 343-354.
DOI
[27]
C. J. Lin, On the convergence of multiplicative update algorithms for nonnegative matrix factorization, IEEE Trans. Neural Netw., vol. 18, no. 6, pp. 1589-1596, 2007.
DOI Google Scholar
[28]
J. Kim and H. Park, Toward faster nonnegative matrix factorization: A new algorithm and comparisons, in Proc. of 2008 Eighth IEEE Int. Conf. Data Mining, Pisa, Italy, 2008, pp. 353-362.
DOI
[29]
F. Wang and P. Li, Efficient nonnegative matrix factorization with random projections, in Proc. 2010 SIAM Int. Conf. Data Mining, Columbus, OH, USA, 2010, pp. 281-292.
DOI
[30]
M. Annett and G. Kondrak, A comparison of sentiment analysis techniques: Polarizing movie blogs, in Proc. 21st Conference of the Canadian Society for Computational Studies of Intelligence, Windsor, Canada, 2008, pp. 25-35.
DOI
[31]
R. Hillmann and M. Trier, Sentiment polarization and balance among users in online social networks, http://aisel.aisnet.org/amcis2012/proceedings/VirtualCommunities/10, 2021.
[32]
M. Del Vicario, G. Vivaldo, A. Bessi, F. Zollo, A. Scala, G. Caldarelli, and W. Quattrociocchi, Echo chambers: Emotional contagion and group polarization on facebook, Sci. Rep., vol. 6, p. 37825, 2016.
DOI Google Scholar
[33]
S. M. Mohammad, X. D. Zhu, S. Kiritchenko, and J. Martin, Sentiment, emotion, purpose, and style in electoral tweets, Informat. Proc. Manag., vol. 51, no. 4, pp. 480-499, 2015.
DOI Google Scholar
[34]
K. Chakraborty, S. Bhattacharyya, R. Bag, and A. Hassanien, Sentiment analysis on a set of movie reviews using deep learning techniques, in Social Network Analytics Computational Research Methods and Techniques, Cambridge, MA, USA, 2019, pp. 127-147.
DOI
[35]
K. Sailunaz and R. Alhajj, Emotion and sentiment analysis from twitter text, J. Comput. Sci., vol. 36, p. 101003, 2019.
DOI Google Scholar
[36]
H. Meisheri, K. Ranjan, and L. Dey, Sentiment extraction from consumer-generated noisy short texts, in Proc. of 2017 IEEE Int. Conf. Data Mining Workshops (ICDMW), New Orleans, LA, USA, 2017, pp. 399-406.
DOI
[37]
A. S. M. Alharbi and E. de Doncker, Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information, Cogn. Syst. Res., vol. 54, pp. 50-61, 2019.
DOI Google Scholar
[38]
M. E. J. Newman, Modularity and community structure in networks, Proc. Natl. Acad. Sci. USA, vol. 103, no. 23, pp. 8577-8582, 2006.
DOI Google Scholar
[39]
M. Wang, C. K. Wang, J. X. Yu, and J. Zhang, Community detection in social networks: An in-depth benchmarking study with a procedure-oriented framework, Proc. VLDB Endow., vol. 8, no. 10, pp. 998-1009, 2015.
DOI Google Scholar
[40]
D. Cai, X. F. He, X. Y. Wu, and J. W. Han, Non-negative matrix factorization on manifold, in Proc. 2008 8th IEEE Int. Conf. Data Mining, Pisa, Italy, 2008, pp. 63-72.
DOI
[41]
H. Wang, F. P. Nie, H. Huang, and F. Makedon, Fast nonnegative matrix tri-factorization for large-scale data co-clustering, in Proc. 22nd Int. Joint Conf. Artificial Intelligence, Barcelona, Spain, 2011, pp. 1553-1558.
[42]
TextBlob: Simplified text processing, https://textblob.readthedocs.io/en/dev/, 2020.
[43]
C. H. Q. Ding, T. Li, and M. I. Jordan, Convex and semi-nonnegative matrix factorizations, IEEE Trans. Patt. Anal. Mach. Intell., vol. 32, no. 1, pp. 45-55, 2010.
DOI Google Scholar
[44]
H. Abe and H. Yadohisa, Orthogonal nonnegative matrix tri-factorization based on tweedie distributions, Adv. Data Anal. Classi., vol. 13, no. 4, pp. 825-853, 2019.
DOI Google Scholar
[45]
P. K. Shivaswamy and T. Jebara, Permutation invariant SVMs, in Proc. 23rd Int. Conf. Machine Learning, Pittsburgh, PA, USA, 2006, pp. 817-824.
DOI
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Received: 25 February 2021
Revised: 02 June 2021
Accepted: 04 June 2021
Published: 26 August 2021
Issue date: December 2021

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