Analysing Twitter and web queries for flu trend prediction

Theoretical Biology and Medical Modelling

Volumn 11, Issue SUPPL.1, 2014, Pages

  Santos, José Carlos ^a   Matos, Sérgio ^a  

^a UNIVERSITY OF AVEIRO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN; INFLUENZA, HUMAN; INTERNET; PORTUGAL; RECEIVER OPERATING CHARACTERISTIC; SEARCH ENGINE; STATISTICAL MODEL; STATISTICS;

HUMANS; INFLUENZA, HUMAN; INTERNET; LINEAR MODELS; PORTUGAL; ROC CURVE; SEARCH ENGINE; STATISTICS AS TOPIC;

EID: 84900339672     PISSN: None     EISSN: 17424682     Source Type: Journal    
DOI: 10.1186/1742-4682-11-S1-S6     Document Type: Article

References (21)

1. You are what you tweet: Analyzing Twitter for public health. Paul M, Dredze M, Proceedings of the 5th International AAAI Conference on Weblogs and Social Media 2011 265 272
2. Dissemination of health information through social networks: Twitter and antibiotics. Scanfeld D, Scanfeld V, Larson EL, American Journal of Infection Control 2010 38 3 182 188 10.1016/j.ajic.2009.11.004 20347636
3. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak. Chew C, Eysenbach G, PLoS ONE 2010 5 11 14118. 10.1371/journal.pone.0014118 21124761
4. Twitter. http://www.twitter.com
5. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. Eysenbach G, J Med Internet Res 2009 11 1 11 10.2196/jmir.1157 19329408
6. Scoping review on search queries and social media for disease surveillance: a chronology of innovation. Bernardo TM, Rajic A, Young I, Robiadek K, Pham MT, Funk JA, J Med Internet Res 2013 15 7 147. 10.2196/jmir.2740 23896182
7. Comparison of web-based biosecurity intelligence systems: Biocaster, epispider and healthmap. Lyon A, Nunn M, Grossel G, Burgman M, Transboundary and emerging diseases 2012 59 3 223 232 10.1111/j.1865-1682.2011.01258.x 22182229
8. The use of twitter to track levels of disease activity and public concern in the u.s. during the influenza a h1n1 pandemic. Signorini A, Segre AM, Polgreen PM, PLoS ONE 2011 6 5 19467 10.1371/journal.pone.0019467 21573238
9. Social and news media enable estimation of epidemiological patterns early in the 2010 haitian cholera outbreak. Chunara R, Andrews JR, Brownstein JS, Am J Trop Med Hyg 2012 86 1 39 45 10.4269/ajtmh.2012.11-0597 22232449
10. Twitter catches the flu: detecting influenza epidemics using Twitter. Aramaki E, Maskawa S, Morita M, Proceedings of the Conference on Empirical Methods in Natural Language Processing Association for Computational Linguistics 2011 1568 1576
11. Tracking the flu pandemic by monitoring the social web. Lampos V, Cristianini N, Proceedings of the 2nd International Workshop on Cognitive Information Processing (CIP) 2010 411 416
12. Towards detecting influenza epidemics by analyzing Twitter messages. Culotta A, Proceedings of the First Workshop on Social Media Analytics ACM 2010 115 122
13. Detecting influenza outbreaks by analyzing Twitter messages. Culotta A, 2010 arXiv:1007.4748 [cs.IR]
14. Real-Time Disease Surveillance using Twitter Data: Demonstration on Flu and Cancer. Lee K, Agrawal A, Choudhary A, Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD): 11-14 August 2013 Chicago, IL. ACM 2013 1474 1477
15. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. Eysenbach G, AMIA Annual Symposium Proceedings: 11-15 November 2006 Washington, DC. AMIA 2006 244 248 17238340
16. Detecting influenza epidemics using search engine query data. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L, Nature 2009 457 1012 1014 10.1038/nature07634 19020500
17. Influenzanet project. http://www.influenzanet.eu
18. sapo.pt. http://www.sapo.pt
19. Loper E, Bird S, Klein E, Natural Language Processing with Python O'Reilly Media Inc 2009
20. Making large Scale SVM Learning Practical. Joachims T, Advances in Kernel Methods-Support Vector Learning MIT-Press, Schölkopf B, Burges C, Smola A, 1999
21. Scikit-learn: Machine Learning in Python. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E., Journal of Machine Learning Research 2011 12 2825 2830