Assessing the Capability of Government Information Intervention and Socioeconomic Factors of Information Sharing during the COVID-19 Pandemic: A Cross-Country Study Using Big Data Analytics
(1) Background: This study introduces a novel computational approach to examine government capabilities in information intervention for risk management, influential agents in a global information network, and the socioeconomic factors of information-sharing behaviors of the public across regions dur...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Behavioral Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-328X/12/6/190 |
| _version_ | 1827662338483814400 |
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| author | Sejung Park Rong Wang |
| author_facet | Sejung Park Rong Wang |
| author_sort | Sejung Park |
| collection | DOAJ |
| description | (1) Background: This study introduces a novel computational approach to examine government capabilities in information intervention for risk management, influential agents in a global information network, and the socioeconomic factors of information-sharing behaviors of the public across regions during the COVID-19 pandemic. (2) Methods: Citation network analysis was employed to gauge the online visibility of governmental health institutions across regions. A bipartite exponential random graph modeling (ERGM) procedure was conducted to measure network dynamics. (3) Results: COVID-19 response agencies in Europe had the highest web impact, whereas health agencies in North America had the lowest. Various stakeholders, such as businesses, non-profit organizations, governments, and educational institutions played a key role in sharing the COVID-19 response by agencies’ information given on their websites. Income inequality and GDP per capita were associated with the high online visibility of governmental health agencies. Other factors, such as population size, an aging population, death rate, and case percentage, did not contribute to the agencies’ online visibility, suggesting that demographic characteristics and health status are not predictors of sharing government resources. (4) Conclusions: A combination of citation network analysis and ERGM helps reveal information flow dynamics and understand the socioeconomic consequences of sharing the government’s COVID-19 information during the pandemic. |
| first_indexed | 2024-03-10T00:23:41Z |
| format | Article |
| id | doaj.art-515dc704dbf54528a05ce97d96db0c21 |
| institution | Directory Open Access Journal |
| issn | 2076-328X |
| language | English |
| last_indexed | 2024-03-10T00:23:41Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Behavioral Sciences |
| spelling | doaj.art-515dc704dbf54528a05ce97d96db0c212023-11-23T15:37:06ZengMDPI AGBehavioral Sciences2076-328X2022-06-0112619010.3390/bs12060190Assessing the Capability of Government Information Intervention and Socioeconomic Factors of Information Sharing during the COVID-19 Pandemic: A Cross-Country Study Using Big Data AnalyticsSejung Park0Rong Wang1Division of Global & Interdisciplinary Studies, Pukyong National University, Busan 48513, KoreaDepartment of Human and Organizational Development, Peabody College of Education and Human Development, Vanderbilt University, Nashville, TN 37240, USA(1) Background: This study introduces a novel computational approach to examine government capabilities in information intervention for risk management, influential agents in a global information network, and the socioeconomic factors of information-sharing behaviors of the public across regions during the COVID-19 pandemic. (2) Methods: Citation network analysis was employed to gauge the online visibility of governmental health institutions across regions. A bipartite exponential random graph modeling (ERGM) procedure was conducted to measure network dynamics. (3) Results: COVID-19 response agencies in Europe had the highest web impact, whereas health agencies in North America had the lowest. Various stakeholders, such as businesses, non-profit organizations, governments, and educational institutions played a key role in sharing the COVID-19 response by agencies’ information given on their websites. Income inequality and GDP per capita were associated with the high online visibility of governmental health agencies. Other factors, such as population size, an aging population, death rate, and case percentage, did not contribute to the agencies’ online visibility, suggesting that demographic characteristics and health status are not predictors of sharing government resources. (4) Conclusions: A combination of citation network analysis and ERGM helps reveal information flow dynamics and understand the socioeconomic consequences of sharing the government’s COVID-19 information during the pandemic.https://www.mdpi.com/2076-328X/12/6/190information behaviorsCOVID-19risk managementbig data analyticssocial network analysisexponential random graph modeling |
| spellingShingle | Sejung Park Rong Wang Assessing the Capability of Government Information Intervention and Socioeconomic Factors of Information Sharing during the COVID-19 Pandemic: A Cross-Country Study Using Big Data Analytics Behavioral Sciences information behaviors COVID-19 risk management big data analytics social network analysis exponential random graph modeling |
| title | Assessing the Capability of Government Information Intervention and Socioeconomic Factors of Information Sharing during the COVID-19 Pandemic: A Cross-Country Study Using Big Data Analytics |
| title_full | Assessing the Capability of Government Information Intervention and Socioeconomic Factors of Information Sharing during the COVID-19 Pandemic: A Cross-Country Study Using Big Data Analytics |
| title_fullStr | Assessing the Capability of Government Information Intervention and Socioeconomic Factors of Information Sharing during the COVID-19 Pandemic: A Cross-Country Study Using Big Data Analytics |
| title_full_unstemmed | Assessing the Capability of Government Information Intervention and Socioeconomic Factors of Information Sharing during the COVID-19 Pandemic: A Cross-Country Study Using Big Data Analytics |
| title_short | Assessing the Capability of Government Information Intervention and Socioeconomic Factors of Information Sharing during the COVID-19 Pandemic: A Cross-Country Study Using Big Data Analytics |
| title_sort | assessing the capability of government information intervention and socioeconomic factors of information sharing during the covid 19 pandemic a cross country study using big data analytics |
| topic | information behaviors COVID-19 risk management big data analytics social network analysis exponential random graph modeling |
| url | https://www.mdpi.com/2076-328X/12/6/190 |
| work_keys_str_mv | AT sejungpark assessingthecapabilityofgovernmentinformationinterventionandsocioeconomicfactorsofinformationsharingduringthecovid19pandemicacrosscountrystudyusingbigdataanalytics AT rongwang assessingthecapabilityofgovernmentinformationinterventionandsocioeconomicfactorsofinformationsharingduringthecovid19pandemicacrosscountrystudyusingbigdataanalytics |