Immediate and long-term effects of 2016 Zika Outbreak: A Twitter-based study

2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016

Volumn , Issue , 2016, Pages

  Khatua, Aparup ^a   Khatua, Apalak ^b  

^a UNIVERSITY OF CALCUTTA   (India)

^b XLRI XAVIER SCHOOL OF MANAGEMENT   (India)

Author keywords

Hierarchical Clustering; Immediate effects; Long term effects; Twitter; Zika Outbreak

Indexed keywords

DATA MINING; VIRUSES;

HIER-ARCHICAL CLUSTERING; IMMEDIATE EFFECTS; LONG-TERM EFFECTS; TWITTER; ZIKA OUTBREAK;

SOCIAL NETWORKING (ONLINE);

EID: 85006340272     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/HealthCom.2016.7749496     Document Type: Conference Paper

References (21)

1. Achrekar, H., Gandhe, A., Lazarus, R., Yu, S. H., & Liu, B. (2011, April). Predicting flu trends using twitter data. In Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on (pp. 702-707). IEEE.
2. Alicino, C., Bragazzi, N. L., Faccio, V., Amicizia, D., Panatto, D., Gasparini, R., Icardi, G., & Orsi, A. (2015). Assessing Ebola-related web search behavior: insights and implications from an analytical study of Google Trends-based query volumes. Infectious diseases of poverty, 4(1), 1.
3. Chan, E. H., Sahai, V., Conrad, C., & Brownstein, J. S. (2011). Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Negl Trop Dis, 5(5), e1206.
4. Chew, C., & Eysenbach, G. (2010). Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PloS one, 5(11), e14118.
5. Culotta, A. (2010, July). Towards detecting influenza epidemics by analyzing Twitter messages. In Proceedings of the first workshop on social media analytics (pp. 115-122). ACM.
6. Farris, J. S. (1969). On the cophenetic correlation coefficient. Systematic Biology, 18(3), 279-285.
7. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014.
8. Ji, X., Chun, S. A., & Geller, J. (2012, April). Epidemic outbreak and spread detection system based on twitter data. In International Conference on Health Information Science (pp. 152-163). Springer Berlin Heidelberg.
9. Jin, F., Wang, W., Zhao, L., Dougherty, E., Cao, Y., Lu, C. T., & Ramakrishnan, N. (2014). Misinformation propagation in the age of twitter. Computer, 47(12), 90-94.
10. Kalyanam, J., Velupillai, S., Doan, S., Conway, M., & Lanckriet, G. (2015). Facts and Fabrications about Ebola: A Twitter Based Study. In Proceedings of the KDD'15 Conference.
11. Lampos, V., De Bie, T., & Cristianini, N. (2010, September). Flu detectortracking epidemics on Twitter. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 599-602). Springer Berlin Heidelberg.
12. Lazard, A. J., Scheinfeld, E., Bernhardt, J. M., Wilcox, G. B., & Suran, M. (2015). Detecting themes of public concern: A text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat. American journal of infection control, 43(10), 1109-1111.
13. Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google flu: traps in big data analysis. Science, 343(6176), 1203-1205.
14. Lu, Y., Hu, X., Wang, F., Kumar, S., Liu, H., & Maciejewski, R. (2015, May). Visualizing social media sentiment in disaster scenarios. In Proceedings of the 24th International Conference on World Wide Web (pp. 1211-1215). ACM.
15. Odlum, M., & Yoon, S. (2015). What can we learn about the Ebola outbreak from tweets?. American journal of infection control, 43(6), 563-571.
16. Saraçli, S., Doǧan, N., & Doǧan, I. (2013). Comparison of hierarchical cluster analysis methods by cophenetic correlation. Journal of Inequalities and Applications, 2013(1), 1-8.
17. Sokal, R. R., & Rohlf, F. J. (1962). The comparison of dendrograms by objective methods. Taxon, 33-40.
18. Tal Galili (2015). dendextend: an R package for visualizing, adjusting, and comparing trees of hierarchical clustering. Bioinformatics, btv428
19. Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301), 236-244.
20. Xu, D., Liu, Y., Zhang, M., Ma, S., Cui, A., & Ru, L. (2011, July). Predicting epidemic tendency through search behavior analysis. In IJCAI Proceedings-International Joint Conference on Artificial Intelligence (Vol. 22, No. 3, p. 2361).
21. Yom-Tov, E. (2015, May). Ebola data from the Internet: An opportunity for syndromic surveillance or a news event?. In Proceedings of the 5th International Conference on Digital Health 2015 (pp. 115-119). ACM.