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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
Empirical comparison of algorithms for network community detection
Leskovec, J., Lang, K. J., & Mahoney, M. W. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772755 · Cited by 965
Rodríguez, H., Quarantelli, E. L., & Dynes, R. R. (2007) · Handbooks of sociology and social research · doi:10.1007/978-0-387-32353-4 · Cited by 952
Yates, D., & Paquette, S. (2010) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2010.10.001 · Cited by 881
Houston, J. B., Hawthorne, J., Perreault, M. F., Park, E. H., Goldstein Hode, M., Halliwell, M. R., Turner McGowen, S. E., Davis, R., Vaid, S., McElderry, J. A., et al. (2015) · Disasters · doi:10.1111/disa.12092 · Cited by 869
Harnessing the Crowdsourcing Power of Social Media for Disaster Relief
Gao, H., Barbier, G., & Goolsby, R. (2011) · IEEE Intelligent Systems · doi:10.1109/mis.2011.52 · Cited by 779
Using Social Media to Enhance Emergency Situation Awareness
Yin, J., Lampert, A., Cameron, M., Robinson, B., & Power, R. (2012) · IEEE Intelligent Systems · doi:10.1109/mis.2012.6 · Cited by 652
Social network analysis: Characteristics of online social networks after a disaster
Kim, J., & Hastak, M. (2017) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.08.003 · Cited by 644
Self-citationSocial Media in Disaster Risk Reduction and Crisis Management
Alexander, D. (2013) · Science and Engineering Ethics · doi:10.1007/s11948-013-9502-z · Cited by 593
Analyzing (social media) networks with NodeXL
Smith, M. A., Shneiderman, B., Milić-Frayling, N., Rodrigues, E. M., Barash, V., Dunne, C., Capone, T., Perer, A., & Gleave, E. (2009) · International Conference on Communities and Technologies (C&T) · doi:10.1145/1556460.1556497 · Cited by 530
Real-Time Crisis Mapping of Natural Disasters Using Social Media
Middleton, S. E., Middleton, L., & Modafferi, S. (2014) · IEEE Intelligent Systems · doi:10.1109/mis.2013.126 · Cited by 439
Blei, D. M. (2011) · doi:10.1145/2107736.2107741 · Cited by 439
Huang, Y., Street‐Perrott, F. A., Metcalfe, S. E., Brenner, M., Moreland, M., & Freeman, K. H. (2001) · Science · doi:10.1126/science.1060143 · Cited by 415
Extracting Information Nuggets from Disaster- Related Messages in Social Media
Imran, M., Elbassuoni, S., Castillo, C., Díaz, F., & Meier, P. P. (2013) · Cited by 387
Luo, Q., & Zhong, D. (2015) · Tourism Management · doi:10.1016/j.tourman.2014.07.007 · Cited by 374
The power of people: social capital’s role in recovery from the 1995 Kobe earthquake
Aldrich, D. P. (2010) · Natural Hazards · doi:10.1007/s11069-010-9577-7 · Cited by 362
Blei, D. M., Carin, L., & Dunson, D. B. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.938079 · Cited by 357
The role of social media in local government crisis communications
Graham, M., Avery, E. J., & Park, S. (2015) · Public Relations Review · doi:10.1016/j.pubrev.2015.02.001 · Cited by 299
Hope for Haiti: An analysis of Facebook and Twitter usage during the earthquake relief efforts
Muralidharan, S., Rasmussen, L., Patterson, D., & Shin, J. (2011) · Public Relations Review · doi:10.1016/j.pubrev.2011.01.010 · Cited by 263
Public engagement with nonprofit organizations on Facebook
Cho, M., Schweickart, T., & Haase, A. (2014) · Public Relations Review · doi:10.1016/j.pubrev.2014.01.008 · Cited by 263
Origins of power-law degree distribution in the heterogeneity of human activity in social networks
Muchnik, L., Pei, S., Parra, L. C., Reis, S. D. S., Andrade, J. S., Havlin, S., & Makse, H. A. (2013) · Scientific Reports · doi:10.1038/srep01783 · Cited by 236
Google Flu Trends: Correlation With Emergency Department Influenza Rates and Crowding Metrics
Dugas, A. F., Hsieh, Y., Levin, S. R., Pines, J. M., Mareiniss, D. P., Mohareb, A. M., Gaydos, C. A., Perl, T. M., & Rothman, R. E. (2012) · Clinical Infectious Diseases · doi:10.1093/cid/cir883 · Cited by 223
An overview of social network analysis
Oliveira, M., & da Gama, J. M. P. (2012) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1048 · Cited by 210
Public behavior response analysis in disaster events utilizing visual analytics of microblog data
Chae, J., Thom, D., Jang, Y., Kim, S., Ertl, T., & Ebert, D. S. (2013) · Computers & Graphics · doi:10.1016/j.cag.2013.10.008 · Cited by 182
#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media
Zhao, J., Cao, N., Wen, Z., Song, Y., Lin, Y., & Collins, C. M. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346922 · Cited by 178
Yoo, E., Rand, W. M., Eftekhar, M., & Rabinovich, E. (2016) · Journal of Operations Management · doi:10.1016/j.jom.2016.05.007 · Cited by 178
Emergent use of social media: A new age of opportunity for disaster resilience
Keim, M. K., & Eric Noji, M. (2011) · American Journal of Disaster Medicine · doi:10.5055/ajdm.2011.0044 · Cited by 171
A fast multi-scale method for drawing large graphs
Harel, D., & Koren, Y. (2000) · Working Conference on Advanced Visual Interfaces (AVI) · doi:10.1145/345513.345353 · Cited by 137
Liu, B. F., Fraustino, J. D., & Jin, Y. (2014) · Journal of Applied Communication Research · doi:10.1080/00909882.2014.982685 · Cited by 132
ScatterBlogs2: Real-Time Monitoring of Microblog Messages through User-Guided Filtering
Bosch, H., Thom, D., Heimerl, F., Puttmann, E., Koch, S., Kruger, R., Worner, M., & Ertl, T. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.186 · Cited by 128
Social network analysis: Measuring, mapping, and modeling collections of connections
Hansen, D. L., Shneiderman, B., Smith, M. A., & Himelboim, I. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817756-3.00003-0 · Cited by 126
Communicating flood risk: Looking back and forward at traditional and social media outlets
FELDMAN, D. L., Contreras, S. L., Karlin, B., Basolo, V., Matthew, R. A., Sanders, B. F., Houston, D., Cheung, W., Goodrich, K. A., Reyes, A., et al. (2015) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2015.12.004 · Cited by 109
Chen, S., Lin, L., & Yuan, X. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13211 · Cited by 107
Combining real and virtual volunteers through social media.
Reuter, C., Heger, O., & Pipek, V. (2013) · TUbilio (Technical University of Darmstadt) · Cited by 101
9th International Conference on Information Systems for Crisis Response and Management (ISCRAM)
Le, V. T., Bouraqadi, N., Stinckwich, S., & Doniec, A. (2012) · HAL Portal Artxiker (Hindustan Aeronautics Limited (India)) · Cited by 94
Silver, A., & Matthews, L. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1253762 · Cited by 93
Communicating Risk and Uncertainty: Science, Technology, and Disasters at the Crossroads
Rodr´ıguez, H., ́ıaz, W. D., Santos, J. M., & Aguirre, B. E. (2007) · Handbooks of sociology and social research · doi:10.1007/978-0-387-32353-4_29 · Cited by 91
Social Resources and Community Resilience in the Wake of Superstorm Sandy
Cagney, K. A., Sterrett, D., Benz, J., & Tompson, T. N. (2016) · PLoS ONE · doi:10.1371/journal.pone.0160824 · Cited by 89
Analyzing Social Media Networks with NodeXL
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-64028-9 · Cited by 87
Joshi, A., & Aoki, M. (2013) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2013.09.004 · Cited by 85
Degree centrality and eigenvector centrality in twitter
Maharani, W., Adiwijaya, A., & Gozali, A. A. (2014) · International Conference on Telecommunication Systems, Services, and Applications · doi:10.1109/tssa.2014.7065911 · Cited by 76
Tweets, Google trends, and sovereign spreads in the GIIPS
Dergiades, T., Milas, C., & Panagiotidis, T. (2014) · Oxford Economic Papers · doi:10.1093/oep/gpu046 · Cited by 76
The capacity for self-governance and post-disaster resiliency
Grube, L. E., & Storr, V. H. (2013) · The Review of Austrian Economics · doi:10.1007/s11138-013-0210-3 · Cited by 71
Using social media to enhance emergency situation awareness
Yin, J., Karimi, S., Lampert, A., Cameron, M. A., Robinson, B., & Power, R. A. (2015) · Cited by 61
A comparative study of social network analysis tools
Combe, D., Largeron, C., Egyed‐Zsigmond, E., & Géry, M. (2010) · HAL · Cited by 46
Thom, D., Kruger, R., Ertl, T., Bechstedt, U., Platz, A., Zisgen, J., & Volland, B. (2015) · IEEE Pacific Visualization Symposium · doi:10.1109/pacificvis.2015.7156376 · Cited by 41
Tyshchuk, Y., Hui, C., Grabowski, M., & Wallace, W. A. (2012) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2012.536 · Cited by 40
A Volunteered Geographic Information Framework to Enable Bottom-Up Disaster Management Platforms
Poorazizi, M. E., Hunter, A. J. S., & Steiniger, S. (2015) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi4031389 · Cited by 28
Knox, J. A., Rackley, J. A., Black, A. W., Gensini, V. A., Butler, M., Dunn, C. B., Gallo, T., Hunter, M. R., Lindsey, L., Phan, M., et al. (2013) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-12-00036.1 · Cited by 25
Online Human Behaviors on Social Media during Disaster Responses
Kim, J., & Hastak, M. (2017) · Homeland security affairs · Cited by 14
Self-citationEvaluating Multi-attributes on Cause and Effect Relationship Visualization
Bae, J., Ventocilla, E., Riveiro, M., Helldin, T., & Falkman, G. (2017) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0006102300640074 · Cited by 12
Self-citationHugelius, K., Adolfsson, A., Gifford, M., & Örtenwall, P. (2017) · Europe PMC (PubMed Central) · doi:10.1371/currents.dis.f4a444e1f182776bdf567893761f86b8 · Cited by 11
Community detection algorithms
(2018) · Physics Subject Headings (PhySH) · doi:10.29172/180b51bc-73e1-45ee-90e6-a820e89e0da1 · Cited by 9
Cited by
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Zhang, C., Fan, C. H., Yao, W., Hu, X., & Mostafavi, A. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.004 · Cited by 301
“Williams et al. showed that among the respondents who use social media, only 29.3% have experience using social media in a disaster (Williams, Valero, & Kim, 2018).”
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Martínez-Rojas, M., Pardo-Ferreira, M. D. C., & Rubio-Romero, J. C. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.07.008 · Cited by 213
Pourebrahim, N., Sultana, S., Edwards, J., Gochanour, A., & Mohanty, S. D. (2019) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2019.101176 · Cited by 195
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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