Figure 1.
Sentiment of #MERS (top) #메르스 tweets (bottom)
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doi: 10.3934/bdia.2017013
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What can we learn about the Middle East Respiratory Syndrome (MERS) outbreak from tweets?
| 1. | School of Nursing, Columbia University Medical Center, New York, NY, 10032, USA |
| 2. | Department of Mathematics, UCLA, Los Angeles, CA, 90095, USA |
| 3. | Digital Library, UCLA, Los Angeles, CA, 90095, USA |
| 4. | Department of Nursing, Hoseo University, Asan, South Korea |
Middle East Respiratory Syndrome (MERS, 메르스 in Korean) is an emerging deadly viral respiratory disease with no treatment. This study applied a triangulation approach of quantitative structure and content mining techniques while incorporating qualitative approaches guided by domain experts, to understand #MERS and #메르스 tweets. This study sought to gain insights about culturally-appropriate nursing activities for an emerging global acute disease management.
Keywords:
Middle East respiratory syndrome,
qualitative approaches,
triangulation,
global acute disease management,
tweets.
Mathematics Subject Classification:
Primary:97R50;Secondary:97R71.
Citation:
Sunmoo Yoon, Da Kuang, Peter Broadwell, Haeyoung Lee, Michelle Odlum.
What can we learn about the Middle East Respiratory Syndrome (MERS) outbreak from tweets?. Big Data & Information Analytics, doi: 10.3934/bdia.2017013
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show all references
References:
| [1] |
D. M. Blei, A. Y. Ng and M. I. Jordan, Latent dirichlet allocation, Journal of machine Learning research, 3 (2003), 993-1022. Google Scholar |
| [2] |
P. S. Dodds, K. D. Harris, I. M. Kloumann, C. A. Bliss and C. M. Danforth, Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter, PloS one, 6 (2011), e26752.
doi: 10.1371/journal.pone.0026752. |
| [3] |
J. Fawcett and C. H. Ellenbecker,
A proposed conceptual model of nursing and population health, Nursing outlook, 63 (2015), 288-298.
doi: 10.1016/j.outlook.2015.01.009. |
| [4] |
D. Kuang and H. Park, Fast rank-2 nonnegative matrix factorization for hierarchical document clustering, in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2013,739-747.
doi: 10.1145/2487575.2487606. |
| [5] |
M. Lui and T. Baldwin, Cross-domain feature selection for language identification, in Proceedings of 5th International Joint Conference on Natural Language Processing, Citeseer, 2011. Google Scholar |
| [6] |
S. Yoon and S. Bakken, Methods of knowledge discovery in tweets, in NI 2012: Proceedings of the 11th International Congress on Nursing Informatics, vol. 2012, American Medical Informatics Association, 2012. Google Scholar |
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