COVID-19 in the Twitterverse, from epidemic to pandemic: information-sharing behavior and Twitter as an information carrier

In this study, we defined a Twitter network as an information channel that includes information sources containing embedded messages. We conducted stage-based comparative analyses of Twitter networks during three periods: the beginning of the COVID-19 epidemic, the period when the epidemic was becoming a global phenomenon, and the beginning of the pandemic. We also analyzed the characteristics of scientific information sources and content on Twitter during the sample period. At the beginning of the epidemic, Twitter users largely shared trustworthy news information sources about the novel coronavirus. Widely shared scientific information focused on clinical investigations and case studies of the new coronavirus as the disease became a pandemic while non-scientific information sources and messages illustrated the social and political aspects of the global outbreak, often including emotional elements. Multiple suspicious, bot-like Twitter accounts were identified as a great connector of the COVID-19 Twitterverse, particularly in the beginning of the global crisis. Our findings suggest that the information carriers, which are information channels, sources, and messages were coherently interlocked, forming an information organism. The study results can help public health organizations design communication strategies, which often require prompt decision-making to manage urgent needs under the circumstances of an epidemic.

[1]  L. Bornmann,et al.  Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors , 2020, PloS one.

[2]  Kyujin Jung,et al.  Citizens' social media use and homeland security information policy: Some evidences from Twitter users during the 2013 North Korea nuclear test , 2014, Gov. Inf. Q..

[3]  S. Clegg.  The SAGE Handbook of Organization Studies , 2006 .

[4]  Guofeng Cao,et al.  Twitter vs News: Concern Analysis of the 2018 California Wildfire Event , 2019, 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC).

[5]  J. Rocklöv,et al.  Using Big Data to Monitor the Introduction and Spread of Chikungunya, Europe, 2017 , 2019, Emerging infectious diseases.

[6]  H. Park,et al.  Risk Communication on Social Media during the Sewol Ferry Disaster , 2019 .

[7]  H. Park,et al.  The effects of infotainment on public reaction to North Korea using hybrid text mining: Content analysis, machine learning-based sentiment analysis, and co-word analysis , 2021 .

[8]  Han Woo Park,et al.  An altmetric investigation of the online visibility of South Korea-based scientific journals , 2018, Scientometrics.

[9]  R. Thackeray,et al.  Adoption and use of social media among public health departments , 2012, BMC Public Health.

[10]  Stephen R. Axley.  Managerial and Organizational Communication in Terms of the Conduit Metaphor , 1984 .

[11]  Haejung Kim,et al.  Social roles and structural signatures of top influentials in the #prayforparis Twitter network , 2019, Quality & Quantity.

[12]  H. Park,et al.  A webometric network analysis of electronic word of mouth (eWOM) characteristics and machine learning approach to consumer comments during a crisis , 2020 .

[13]  Ben Shneiderman,et al.  Analyzing Social Media Networks with NodeXL: Insights from a Connected World , 2010 .

[14]  K. Fisher,et al.  Theories of information behavior , 2005 .

[15]  David Kenneth Berlo,et al.  The process of communication:an introduction to theory and practice , 1960 .

[16]  B. Wellman,et al.  Social Networks, Kinship, and Community in Eastern Europe , 1994 .

[17]  Paul N Zivich,et al.  Social Media- and Internet-Based Disease Surveillance for Public Health. , 2020, Annual review of public health.

[18]  R. Merchant,et al.  Integrating social media into emergency-preparedness efforts. , 2011, The New England journal of medicine.

[19]  Phaedra E Cress.  Using altmetrics and social media to supplement impact factor: maximizing your article's academic and societal impact. , 2014, Aesthetic surgery journal.

[20]  K. Viswanath,et al.  Health Information Seeking , 2011 .

[21]  H. Lucy Thomas,et al.  Using Social Networking Sites for Communicable Disease Control: Innovative Contact Tracing or Breach of Confidentiality? , 2013, Public health ethics.

[22]  Marc A. Smith,et al.  Catalyzing social media scholarship with open tools and data , 2015 .

[23]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[24]  Han Woo Park,et al.  Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea , 2020, Journal of Medical Internet Research.

[25]  Ken Wakita,et al.  Finding community structure in mega-scale social networks: [extended abstract] , 2007, WWW '07.

[26]  David A. Broniatowski,et al.  The COVID-19 Social Media Infodemic Reflects Uncertainty and State-Sponsored Propaganda , 2020, ArXiv.

[27]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[28]  Linda L. Putnam,et al.  Revisiting Metaphors of Organizational Communication , 2006 .

[29]  Hyeoun-Ae Park,et al.  Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies , 2018, Healthcare informatics research.

[30]  Han Woo Park,et al.  Research evaluation of Asian countries using altmetrics: comparing South Korea, Japan, Taiwan, Singapore, and China , 2018, Scientometrics.

[31]  H. Park,et al.  Global-level relationships of international student mobility and research mentions on social media , 2021 .

[32]  Miyoung Chong.  Discovering fake news embedded in the opposing hashtag activism networks on Twitter: #Gunreformnow vs. #NRA , 2019, Open Information Science.

[33]  Sung-Won Yoon,et al.  The EU's Public Diplomacy in Asia and the World through Social Media: Sentiment and Semantic Network Analyses of Official Facebook Pages of European External Action Service and EU Delegation to the Republic of Korea , 2020 .

[34]  Yigang Wei,et al.  Disaster management 2.0: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter) , 2019, Safety Science.

[35]  Miyoung Chong.  Network typology, information sources, and messages of the infodemic twitter network under COVID‐19 , 2020, ASIST.

[36]  N. Thongpapanl,et al.  A comparative study of Twitter utilization in disaster management between public and private organizations , 2019, Journal of Public Affairs.

[37]  Duncan Watts,et al.  Evaluating the fake news problem at the scale of the information ecosystem , 2019, Science Advances.

[38]  Kyujin Jung,et al.  Social Media Use during Japan's 2011 Earthquake: How Twitter Transforms the Locus of Crisis Communication , 2013 .

[39]  Ben Jones,et al.  Mixed uptake of social media among public health specialists. , 2011, Bulletin of the World Health Organization.

[40]  Jeffrey A. Gottfried,et al.  News use across social media platforms 2016 , 2016 .

[41]  Zeynep Tufekci,et al.  Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls , 2014, ICWSM.

[42]  Manuel Rodriguez-Martinez,et al.  Twitter Health Surveillance (THS) System , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[43]  David Rothschild,et al.  Evaluating the fake news problem at the scale of the information ecosystem , 2020, Science Advances.

[44]  Filippo Menczer,et al.  Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak , 2020, ICWSM Workshops.

[45]  Giovanni Colavizza,et al.  Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis , 2020, ArXiv.

[46]  David Easley,et al.  Networks, Crowds, and Markets by David Easley , 2010 .

[47]  Chen Hongsong,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Easley, D. and Kleinberg, J.; 2010) [Book Review] , 2013, IEEE Technology and Society Magazine.