Biomedical Engineering Reference
In-Depth Information
4. Chen, L., Achrekar, H., Liu, B., Lazarus, R.: Vision: towards real time epidemic vigilance
through online social networks: introducing sneft - social network enabled flu trends. In:
ACM Mobile Cloud Computing and Services, San Francisco, California (June 2010)
5. Culotta, A.: Detecting influenza outbreaks by analyzing twitter messages. In: Knowledge
Discovery and Data Mining Workshop on Social Media Analytics (2010)
6. Espino, J., Hogan, W., Wagner, M.: Telephone triage: A timely data source for surveillance
of influenza-like diseases. In: AMIA: Annual Symposium Proceedings (2003)
7. Ferguson, N.M., Cummings, D.A., Cauchemez, S., Fraser, C., Riley, S., Meeyai, A., Iam-
sirithaworn, S., Burke, D.S.: Strategies for containing an emerging influenza pandemic in
southeast asia. Nature 437, 209-214 (2005)
8. Gauvin, W., Ribeiro, B., Towsley, D., Liu, B., Wang, J.: Measurement and gender-specific
analysis of user publishing characteristics on myspace. IEEE Networks (September 2010)
9. Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: De-
tecting influenza epidemics using search engine query data. Nature 457, 1012-1014 (2009)
10. Jansen, B., Zhang, M., Sobel, K., Chowdury, A.: Twitter power:tweets as electronic word of
mouth. Journal of the American Society for Information Science and Technology 60(1532),
2169-2188 (2009)
11. Jordans, F.: WHO working on formulas to model swine flu spread (2009),
http://www.physorg.com/news165686771.html
12. Lazarus, R., Kleinman, K., Dashevsky, I., Adams, C., Kludt, P., DeMaria Jr., A., Platt, R.:
Use of automated ambulatory-care encounter records for detection of acute illness clusters,
including potential bioterrorism events (2002),
http://www.cdc.gov/ncidod/EID/vol8no8/02-0239.html
13. Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news
cycle. In: International Conference on Knowledge Discovery and Data Mining, Paris, France,
vol. 495(978) (2009)
14. Longini, I., Nizam, A., Xu, S., Ungchusak, K., Hanshaoworakul, W., Cummings, D., Hallo-
ran, M.: Containing pandemic influenza at the source. Science 309(5737), 1083-1087 (2005)
15. Magruder, S.: Evaluation of over-the-counter pharmaceutical sales as a possible early warn-
ing indicator of human disease. Johns Hopkins University APL Technical Digest (2003)
16. Mislove, A.: Pulse of the nation: U.S. mood throughout the day inferred from twitter (2010),
http://www.ccs.neu.edu/home/amislove/twittermood/
17. Motoyama, M., Meeder, B., Levchenko, K., Voelker, G.M., Savage, S.: Measuring online
service availability using twitter. In: Workshop on Online Social Networks, Boston, Mas-
sachusetts, USA (2010)
18. Paul, M., Dredze, M.: You are what you tweet:analyzing twitter for public health. Association
for the Advancement of Artificial Intelligence (2011)
19. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detec-
tion by social sensors. In: 19th International Conference on World Wide Web, Raleigh, North
Carolina, USA (2010)
20. Signorini, A., Segre, A.M., Polgreen, P.M.: The use of twitter to track levels of disease ac-
tivity and public concern in the U.S. during the influenza a h1n1 pandemic. PLoS ONE 6(5)
(May 2011)
21. Sitaram, A., Huberman, B.A.: Predicting the future with social media. Social Computing
Lab, HP Labs, Palo Alto, California, USA (2010)
22. Webb, S., Caverlee, J.: A large-scale study of myspace: Observations and implications for
online social networks. Association for the Advancement for Artificial Intelligence (2008)
 
Search WWH ::




Custom Search