An Effective Approach to Track Levels of Influenza-A (H1N1) Pandemic in India Using Twitter

Procedia Computer Science

Volumn 70, Issue , 2015, Pages 801-807

  Jain, Vinay Kumar ^a   Kumar, Shishir ^a  

^a JAYPEE UNIVERSITY OF ENGINEERING AND TECHNOLOGY   (India)

Author keywords

H1N1; Influenza; Na ve Bayes; Random Forest; SVM; Swine flu; Twitter

Indexed keywords

DECISION TREES; ENVIRONMENTAL PROTECTION; HEALTH RISKS; PUBLIC HEALTH; SOCIAL NETWORKING (ONLINE);

H1N1; INFLUENZA; RANDOM FORESTS; SWINE FLU; TWITTER;

DISEASES;

EID: 84962687034     PISSN: None     EISSN: 18770509     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2015.10.120     Document Type: Conference Paper

References (15)

1. Emarketer. http://www.emarketer.com;2015.
2. Preventive Measures for Swine Flu.http://pib.nic.in/newsite/PrintRelease.aspx?relid=115710;2015.
3. Chew C M. Pandemics in the age of twitter: A content analysis of the 2009 h1n1 outbreak. Master's thesis, University of Toronto;2010.
4. Signorini A. Social web information monitoring for health; 2009.
5. Hu X., Lei Tang, and Huan Liu. Enhancing accessibility of microblogging messages using semantic knowledge. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, New York, USA, ACM; 2011,p. 2465-2468.
6. Vasileios Lampos and Nello Cristianini.Tracking the flu pandemic by monitoring the social web. In 2nd IAPR Workshop on Cognitive Information Processing (CIP 2010),IEEE Press,;2010, p. 411-416.
7. Chunara R, Andrews JR, Brownstein J S. Social and news media enable estimation of epidemiological patterns early in the 2010 haitian cholera outbreak. Am J Trop Med Hyg;86,(1),2012, p.39-45.
8. Tang X and Yang C C.Identifying Influential Users in an Online Healthcare Social Network, IEEE, ISI 2010,Vancouver, BC, Canada; 2010,p. 43-48.
9. Aramaki E, Maskawa S, Morita M.Twitter catches the u: detecting inuenza epidemics using Twitter.In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics;2011,p. 1568-1576.
10. Lee C, Kwak H, Park H, and Moon S. Finding influentials based on the temporal order of information adoption in twitter. In Proceedings of the 19th International Conference on World Wide Web (WWW'10); 2010, p.591-600.
11. Stewart A and Diaz E. Epidemic intelligence: for the crowd, by the crowd.In Proceedings of the 12th international conference on Web Engineering, ICWE'12,Berlin, Heidelberg,Springer-Verlag;2012, p.504-505.
12. Hansen L K,Arvidsson A, Nielsen F A, Colleoni E and Etter M. Good friends, bad news - affect and virality in twitter.Future Information Technology, Communications in Computer and Information Science;2011, p. 34-43.
13. Glik D. Risk communication for public health emergencies. Annual Reviews of Public Health;2007,p.33-54.
14. TFIDF.http://www.tfidf.com.
15. Blei D M,Ng A Y,Jordan M I. Latent Dirichlet Allocation,Journal of Machine Learning Research; 3,2003,p. 993-1022.