Applying GIS and machine learning methods to twitter data for multiscale surveillance of influenza

PLoS ONE

Volumn 11, Issue 7, 2016, Pages

  Allen, Chris ^a   Tsou, Ming Hsiang ^a   Aslam, Anoshe ^a   Nagel, Anna ^a   Gawron, Jean Mark ^a  

^a SAN DIEGO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DATA MINING; DISEASE SURVEILLANCE; EPIDEMIC; GEOGRAPHIC DISTRIBUTION; GEOGRAPHIC INFORMATION SYSTEM; INFLUENZA; MACHINE LEARNING; SOCIAL MEDIA; STATISTICAL SIGNIFICANCE; SUPPORT VECTOR MACHINE; UNITED STATES; HEALTH SURVEY; HUMAN; INFLUENZA, HUMAN; MEDICAL GEOGRAPHY;

DISEASE OUTBREAKS; GEOGRAPHIC INFORMATION SYSTEMS; GEOGRAPHY, MEDICAL; HUMANS; INFLUENZA, HUMAN; MACHINE LEARNING; PUBLIC HEALTH SURVEILLANCE; SOCIAL MEDIA; UNITED STATES;

EID: 84980028545     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0157734     Document Type: Article

References (15)

1. Dukic VM, Hedibert FL, Polson N. Tracking Flu Epidemics Using Google Flu Trends and Particle Learning. Social Science Research Network. 2009 Nov 25.
2. Dugas AF, Mehdi J, Yulia G, Levin S, Torcaso F, Igusa T, et al. Influenza Forecasting with Google Flu Trends. PLoS ONE. 2013; 8(2): e56176. doi: 10.1371/journal.pone.0056176 PMID: 23457520
3. Olson D, Konty K, Paladini M, Viboud C, Simonsen L. Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales. PLoS Computational Biology. 2013; 9(10): e1003256. doi: 10.1371/journal.pcbi.1003256 PMID: 24146603
4. Butler D. When Google got Flu Wrong. Nature. 2013 Feb 14; 494(7436): 155-6. doi: 10.1038/494155a PMID: 23407515
5. Newsam S. Crowdsourcing What Is Where: Community-Contributed Photos as Volunteered Geographic Information. IEEE Multimedia: Special Issue on Mining Community-Contributed Multimedia. 2010; 17 (4): 36-45.
6. Perreault, M, Ruths D. The Effect of Mobile Platforms on Twitter Content Generation. In Proc. of the International Conference on Social Media and Weblogs. 2011.
7. Golder SA, Macy MW. Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength across Diverse Cultures. Science. 2011; 333: 1878-1881. doi: 10.1126/science.1202775 PMID: 21960633
8. Nagel AC, Tsou MH, Spitzberg B, An L, Gawron JM, Gupta D, et al. The Complex Relationship of Real-space Events and Messages in Cyberspace: Case Study of Influenza and Pertussis Using Tweets. Journal of Medical Internet Research. 2013 Oct 26; 15(10): e237. doi: 10.2196/jmir.2705 PMID: 24158773
9. Lamb A, Paul MJ, Dredze M. Separating Fact from Fear: Tracking Flu Infections on Twitter. In Proc. of NAACL-HLT. 2013; 789-795.
10. Tsou M, Leitner M. Editorial: Visualization of Social Media: Seeing a Mirage or a Message? In Special Content Issue: Mapping Cyberspace and Social Media. Cartography and Geographic Information Science. 2013; 40(2): 55-60.
11. Tsou M, Yang J, Lusher D, Han S, Spitzberg B, Gawron JM, et al. Mapping Social Activities and Concepts with Social Media (Twitter) and Web Search Engines (Yahoo and Bing): A Case Study in 2012 U. S. Presidential Election. Cartography and Geographic Information Science. 2013. 40(4): 337-348.
12. Joachims T. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In Proc. of the European Conference on Machine Learning (ECML). Springer. 1998.
13. Salton G, Buckley C. Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management. 1988; 24(5): 513-23.
14. Aslam AA, Tsou MH, Spitzberg BH, An Li, Gawron JM, Gupta DK, et al. The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance. J Med Internet Res. 2014; 16(11): e250. doi: 10.2196/jmir.3532 PMID: 25406040
15. Petróczi A, Haugen K. "The Doping Self-Reporting Game: The Paradox of a 'false-Telling' Mechanism and Its Potential Research and Policy Implications." Sport Management Review 15, no. 4 (November 2012): 513-17. doi: 10.1016/j.smr.2012.04.002