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

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

Early access 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:
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D. M. BleiA. 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.

show all references

References:
[1]

D. M. BleiA. 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.

Figure 1.  Sentiment of #MERS (top) #메르스 tweets (bottom)
Figure 2.  Content of #MERS and #메르스 tweets
Figure 3.  Communication network of #MERS & #메르스 tweets
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