Flu Gone Viral: Syndromic Surveillance of Flu on Twitter Using Temporal Topic Models

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn 2015-January, Issue January, 2014, Pages 755-760

  Chen, Liangzhe ^a   Hossain, K S M Tozammel ^a   Butler, Patrick ^a   Ramakrishnan, Naren ^a   Prakash, B Aditya ^a  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

Data Mining; Epidemiology; Social Media; Syndromic Surveillance; Topic Model; Twitter

Indexed keywords

DATA MINING; EPIDEMIOLOGY; FORECASTING; MONITORING; SOCIAL NETWORKING (ONLINE);

DISEASE SURVEILLANCE; EPIDEMIOLOGICAL MODELS; ESTIMATION OF TRENDS; MACHINE LEARNING TECHNIQUES; SOCIAL MEDIA; SYNDROMIC SURVEILLANCE; TOPIC MODELING; TWITTER;

LEARNING SYSTEMS;

EID: 84936930986     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2014.137     Document Type: Conference Paper

References (27)

1. H. Achrekar, A. Gandhe, R. Lazarus, S.-H. Yu, and B. Liu. Predicting Flu Trends using Twitter Data. In IEEE Conference on Computer Communications Workshops, pages 702-707. IEEE, 2011.
2. M. Andrews and G. Vigliocco. The Hidden Markov Topic Model: A Probabilistic Model of Semantic Representation. Topics in Cognitive Science, 2(1):101-113, 2010.
3. E. Beretta and Y. Takeuchi. Global Sta bility of an SIR Epidemic Model with Time Delays. The Journal of mathematical biology, 33(3):250-260, 1995.
4. D. Blei and J. Lafferty. Dynamic Topic Models. In In ICML, pages 113-120, 2006.
5. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet Allocation. The Journal of Machine Learning Research, 3:993-1022, 2003.
6. D. Butler. When Google got F lu Wrong. Nature, 494(7436):155-156, 2013.
7. P. Chakraborty, P. Khadivi, B. Lewis, A. Mahendiran, J. Chen, P. Butler, E. Nsoesie, S. Mekaru, J. Brownstein, M. Marathe, and N. Ramakrishnan. Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions. In SIAM International Conference on Data Mining, 2014.
8. N. Christakis and J. Fowler. Social Networ k Sensors for Early Detection of Contagious Outbreaks. PLoS ONE, (9), 09 2010.
9. R. Crane and D. Sornette. Robust Dynamic Classes Revealed by Measuring the Response Function of a Social System. In PNAS, 2008.
10. J. Ginsberg, M. Mohebbi, R. Patel, L. Brammer, M. Smolinski, and L. Brilliant. Detecting Influenza Epidemics using Search Engine Query Data. Nature, 457(7232):1012-1014, 2008.
11. A. Gruber, M. Rosen-Zvi, and Y. Weiss. Hidden Topic Markov Models. Artificial Intelligence and Statistics (AISTATS), 2007.
12. H. W. Hethcote. The mathematics o f infectious diseases. SIAM Review, 42, 2000.
13. L. Hong, D. Yin, J. Guo, and B. Davison. Tracking Trends: Incorporating Term Volume into Temporal Topic Models. In the 17th ACM SIGKDD, pages 484-492, 2011.
14. J. Jacquez and C. Simon. The Stochastic SI Model with Recruitment and Deaths I. Comparison with the Closed SIS Model. Mathematical Biosciences, 117(1):77-125, 1993.
15. A. Lamb, M. J. Paul, and M. Dredze. Separating fact from fear: Tracking flu infections on twitter. In North American Chapter of the Association for Computational Linguistics (NAACL), 2013.
16. V. Lampos, T. De Bie, and N. Cristianini. Flu detector: Tracking epidemics on twitter. In Proceedings of the 2010 European Conference on Machine Learning and Knowledge Discovery in Databases: Part III, ECML PKDD'10, pages 599-602, 2010.
17. D. Lazer, R. Kennedy, G. King, and A. Vespignani. The parable of google flu: Traps in big data analysis. Science, 343(6176):1203-1205, 2014.
18. K. Lee,A. Agrawal,A. Choudhary. Real-time disease surveillance using twitter data: Demonstration on flu and cancer In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD) 1474-1477. ACM, 2013.
19. M. Li and J. Muldowney. Global stability for the SEIR model in epidemiology. Mathematical Biosciences, 125(2):155-164, 1995.
20. Y. Matsubara, Y. Sakurai, B. A. Prakash, L. Li, and C. Faloutsos. Rise and fall patterns of information diffusion: model and implications. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, p ages 6-14, 2012.
21. PAHO. Epidemic disease database, pan american health organization. http://ais.paho.org/phip/viz/ed flu.asp, Dec. 2012.
22. M. Paul and M. Dredze. You Are What You Tweet: Analyzing Twitter for Public Health. In Fifth International AAAI Conference on Weblogs and Social Media (ICWSM 2011), 2011.
23. D. M. Romero, B. Meeder, and J. Kleinberg. Differences in the mechanics o f information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web, pages 695-704, 2011.
24. A. Sadilek, H. Kautz, and V. Silenzio. Predicting disease transmission from geo-tagged micro-blog data. In AAAI Conference on Artificial Intelligence, 2012.
25. X. Wang and A. McCallum. Topics Over Time: a non-Markov C ontinuous-time Model of Topical Trends. In the 12th ACM SIGKDD, pages 424-433, 2006.
26. J. Yang and J. Leskovec. Patterns of temporal variation in online media. In WSDM, pages 177-186, 2011.
27. J. Yang, J. McAuley, J. Leskovec, P. LePendu, and N. Shah. Finding progression stages in time-evolving event sequences. In the 23rd International Conference on World Wide Web, pages 783-794, 2014.