# American Institute of Mathematical Sciences

doi: 10.3934/bdia.2017013

## 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

* Corresponding author: Sunmoo Yoon, RN, PhD, Associate Research Scientist, Columbia University, sy2102@columbia.edu

Published  November 2017

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.

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
##### References:
 [1] D. M. Blei, A. Y. Ng and M. I. Jordan, Latent dirichlet allocation, Journal of machine Learning research, 3 (2003), 993-1022. [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. [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.

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##### References:
 [1] D. M. Blei, A. Y. Ng and M. I. Jordan, Latent dirichlet allocation, Journal of machine Learning research, 3 (2003), 993-1022. [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. [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.
Sentiment of #MERS (top) #메르스 tweets (bottom)
Content of #MERS and #메르스 tweets
Communication network of #MERS & #메르스 tweets
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