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Emergency information diffusion on online social media during storm Cindy in U.S.
Open accessAbstract
Abstract Social media plays a critical role in propagating emergency information during disasters. Governmental agencies have opened social media accounts for emergency communication channels. To understand the underlying mechanism of user behaviors and engagement, this study employs social network analysis to investigate information network and diffusion across news, weather agencies, governmental agencies, organizations and the public during the 2017 Storm Cindy in the U.S. This study identified certain types of Twitter users (news and weather agencies) were dominant as information sources and information diffusers (the public and organizations). However, the information flow in the network was controlled by numerous types of users including news, agency, weather agencies and the public. The results highlighted the importance of understanding the unique characteristics of social media and networks for better emergency communication system.
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“The role of Twitter as the key platform for rapid communication during emergencies is widely documented (Kim et al. 2018), as users tend to use this specific platform as the medium for concise and targeted messaging to online communities.”
From Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life (2020) · cites this paper as Kim et al. 2018
Citations
Cites
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Finding community structure in very large networks
Clauset, A., Newman, M. E. J., & Moore, C. (2004) · Physical Review E · doi:10.1103/physreve.70.066111 · Cited by 7,390
Predicting the Present with Google Trends
CHOI, H., & VARIAN, H. (2012) · Economic Record · doi:10.1111/j.1475-4932.2012.00809.x · Cited by 2,354
Social Capital and Community Resilience
Aldrich, D. P., & Meyer, M. A. (2015) · American Behavioral Scientist · doi:10.1177/0002764214550299 · Cited by 2,331
Community detection algorithms: A comparative analysis
Lancichinetti, A., & Fortunato, S. (2009) · Physical Review E · doi:10.1103/physreve.80.056117 · Cited by 2,181
Evolution of the social network of scientific collaborations
Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(02)00736-7 · Cited by 1,972
Empirical study of topic modeling in Twitter
Hong, L., & Davison, B. D. (2010) · doi:10.1145/1964858.1964870 · Cited by 1,182
Social Capital: A Missing Link to Disaster Recovery
Nakagawa, Y., & Shaw, R. (2004) · International Journal of Mass Emergencies & Disasters · doi:10.1177/028072700402200101 · Cited by 969
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Metadata
- Authors
- Kim, J., Bae, J., & Hastak, M.
- Affiliations
- Purdue University West Lafayette (US)
- University of Skövde (SE)
- Corresponding author
- Hastak, Makarand
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 40
- Pages
- 153-165
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2793817064
- Semantic Scholar
- 989435d478b46830e9c51a00e7879c6e170fc642
- Cited by
- 166
- References
- 64
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 14 · 2023: 27 · 2022: 24 · 2021: 27 · 2020: 27 · 2019: 20 · 2018: 5
- FWCI
- 43.23
- Type
- journal article
- Language
- en
- Open access
- green
- urn.kb.se
- Licence
- Locations
- International Journal of Information Management (publishedVersion)
- University Library of Skövde (University of Skövde) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Communication › Public Relations and Crisis Communication
- Social Sciences › Sociology and Political Science › Disaster Management and Resilience
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- social media, emergency communication, Twitter, information networks, user engagement, news agencies, government social media accounts, disaster information sharing
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