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http://dx.doi.org/10.3837/tiis.2022.08.006

Analysis of Social Media Utilization based on Big Data-Focusing on the Chinese Government Weibo  

Li, Xiang (School of Business, Heze University)
Guo, Xiaoqin (College of Politics and Law, Heze University)
Kim, Soo Kyun (Department of Computer Engineering, Jeju National University)
Lee, Hyukku (Department of East-Asia Studies, Graduate School, Pai Chai University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.8, 2022 , pp. 2571-2586 More about this Journal
Abstract
The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.
Keywords
Big Data; Social Media; Text Mining; LDA algorithm; Python; Government Weibo;
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1 Mansoor, M., "Citizens' trust in government as a function of good governance and government agency's provision of quality information on social media during COVID-19," Government Information Quarterly, 38(4), 101597, 2021.
2 Kowalski, R., Esteve, M. and Jankin Mikhaylov, S., "Improving public services by mining citizen feedback: An application of natural language processing," Public Administration, 98(4), 1011-1026, 2020.   DOI
3 Hogenboom, F., Frasincar, F., Kaymak, U., De Jong, F. and Caron, E., "A survey of event extraction methods from text for decision support systems," Decision Support Systems, 85, 12-22, 2016.   DOI
4 Bonson, E., Torres, L., Royo, S. and Flores, F., "Local e-government 2.0: Social media and corporate transparency in municipalities," Government information quarterly, 29(2), 123-132, 2012.   DOI
5 Stieglitz, S. and Dang-Xuan, L., "Emotions and information diffusion in social media-sentiment of microblogs and sharing behavior," Journal of management information systems, 29(4), 217-248, 2013.   DOI
6 Tang, G. and Lee, F. L., "Facebook use and political participation: The impact of exposure to shared political information, connections with public political actors, and network structural heterogeneity," Social science computer review, 31(6), 763-773, 2013.   DOI
7 Sun, Z. and Zheng, Y., "City Government Micro-Blogging Development and Driving Factors in China-Based on the Combination of Big Data and Small Data Analysis for 228 Cities(2011-2017)," Journal of Public Management, 18(01), 77-89+171, 2021.
8 T. Meng, S. Zheng, "Information, Communication and Influence: Government Social Media in Internet Governance--An Exploratory Study through Combining Big Data and Small Data Analysis," Journal of Public Administration, 29-52+205-206, 2017.
9 Wang, X. and Wang, Z., "Government-Civilian Interaction, Public Values and Government Performance Improvement--An Empirical Study of Government Micro Blogs of Beijing, Shanghai and Guangdong," Journal of Public Management, 14(03), 31-43+155, 2017.
10 Grimmer, J. and Stewart, B. M., "Text as data: The promise and pitfalls of automatic content analysis methods for political texts," Political analysis, 21(3), 267-297, 2013.   DOI
11 Mergel, I., Rethemeyer, R. K. and Isett, K., "Big data in public affairs," Public Administration Review, 76(6), 928-937, 2016.   DOI
12 Purwanto, A., Zuiderwijk, A. and Janssen, M., "Citizens' trust in open government data: a quantitative study about the effects of data quality, system quality and service quality," in Proc. of The 21st Annual International Conference on Digital Government Research, pp. 310-318, 2020.
13 Huang, J. and Liu, J., "The Development of Digital Governance in Europe and America and Its Enlightenment to China," Chinese Public Administration, 06, 36-41, 2019.
14 Tang, Z., Miller, A. S., Zhou, Z. and Warkentin, M., "Does government social media promote users' information security behavior towards COVID-19 scams? Cultivation effects and protective motivations," Government Information Quarterly, 38(2), 101572, 2021.
15 Criado, J. I. and Villodre, J., "Revisiting social media institutionalization in government. An empirical analysis of barriers," Government Information Quarterly, 39(2), 101643, 2022.
16 Criado, J. I., Sandoval-Almazan, R. and Gil-Garcia, J. R., "Government innovation through social media. Elsevier," Government Information Quarterly, 30(4), 319-326, 2013.   DOI
17 Blei, D. M., Ng, A. Y. and Jordan, M. I., "Latent dirichlet allocation," Journal of machine Learning research, 3, 993-1022, 2003.
18 Janssen, M. and van den Hoven, J., "Big and Open Linked Data (BOLD) in government: A challenge to transparency and privacy?," Government Information Quarterly, 32(4), 363-368, 2015.   DOI
19 Guo, X., Li, B. and Li, X., "A Study on Synergistic Development of Innovative Public Management and Economic Growth Based on Big Data," Mobile Information Systems, 2022.
20 Rogge, N., Agasisti, T. and De Witte, K., "Big data and the measurement of public organizations' performance and efficiency: The state-of-the-art," Public Policy and Administration, 32(4), 263-281, 2017.   DOI
21 Solka, J. L., "Text data mining: theory and methods," Statistics Surveys, 2, 94-112, 2008.   DOI
22 Anastasopoulos, L. J. and Whitford, A. B., "Machine learning for public administration research, with application to organizational reputation," Journal of Public Administration Research and Theory, 29(3), 491-510, 2019.   DOI
23 Bertot, J. C. and Jaeger, P. T., "The E-Government paradox: Better customer service doesn't necessarily cost less," Government Information Quarterly, 2(25), 149-154, 2008.   DOI
24 Chuang, J., Roberts, M. E., Stewart, B. M., Weiss, R., Tingley, D., Grimmer, J. and Heer, J., "TopicCheck: Interactive alignment for assessing topic model stability," in Proc. of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 175-184, 2015.
25 Mergel, I., "A framework for interpreting social media interactions in the public sector," Government information quarterly, 30(4), 327-334, 2013.   DOI
26 De Vries, H., Bekkers, V. and Tummers, L., "Innovation in the public sector: A systematic review and future research agenda," Public administration, 94(1), 146-166, 2016.   DOI
27 B. Lu, S. Zhang, W. Fan, "Social representations of social media use in government: An analysis of Chinese government microblogging from citizens' perspective," Social Science Computer Review, 34(04), 416-436, 2016.   DOI
28 J. Jiang, W. Wang, "Research on Government Douyin for Public Opinion of Public Emergencies: Comparison with Government Microblog," Journal of Intelligence, 100-106+114, 2020.
29 Xiao, M. and Guo, S., "The Role of Online Public Opinion and Government Microblogs in the Modernization of National Governance," Administrative Tribune, 21(04), 5-10, 2014.