Regional Level Influenza Study with Geo-Tagged Twitter Data
The rich data generated and read by millions of users on social media tells what is happening in the real world in a rapid and accurate fashion. In recent years many researchers have explored real-time streaming data from Twitter for a broad range of applications, including predicting stock markets and public health trend. In this paper we design, implement, and evaluate a prototype system to collect and analyze influenza statuses over different geographical locations with real-time tweet streams. We investigate the correlation between the Twitter flu counts and the official statistics from the Center for Disease Control and Prevention (CDC) and discover that real-time tweet streams capture the dynamics of influenza cases at both national and regional level and could potentially serve as an early warning system of influenza epidemics. Furthermore, we propose a dynamic mathematical model which can forecast Twitter flu counts with high accuracy.
KeywordsInfluenza Regional level Partial differential equation modeling Geo-tagged twitter stream
This project is supported by NSF grant CNS #1218212.
- 4.Broniatowski, D.A., Paul, M.J., and Dredze, M., National and local influenza surveillance through twitter: An analysis of the 2012-2013 influenza epidemic. PLoS one 8, 2013.Google Scholar
- 5.Achrekar, H.A., Gandhe, R., Lazarus, S. H.Y. u., and Liu, B.: Predicting Flu Trends using Twitter data, IEEE Conference on Computer Communications Workshop on Cyber-Physical Networking Systems (2011)Google Scholar
- 7.Dredze, M., Paul, M., Bergsma, S., and Tran, H., Carmen: A Twitter Geolocation System with Applications to Public Health. In: AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI) (2012)Google Scholar
- 9.Lamb, A., Paul, M.J., and Dredze, M.: Separating fact from fear: Tracking flu infections on twitter. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (2013)Google Scholar
- 10.Lee, K., Agrawal, A., and Choudhary, A.: Real-Time Disease Surveillance using Twitter Data: Demonstration on Flu and Cancer. Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2013)Google Scholar
- 11.Lee, K., Agrawal, A., and Choudhary, A.: Real-Time Digital Flu Surveillance using Twitter Data, The 2nd Workshop on Data Mining for Medicine and Healthcare (2013)Google Scholar
- 12.Signorini, A., Segre, A.M., and Polgreen, P.M., The Use of twitter to track levels of disease activity and public concern in the U.S. during the influenza a h1n1 pandemic. PLoS ONE 6(5), 2011.Google Scholar
- 14.Aramaki, E., Maskawa, S., and Morita, M. Twitter catches the flu: detecting influenza epidemics using Twitter. Proceedings of the Conference on Empirical Methods in Natural Language Processing Association for Computational Linguistics, 2011.Google Scholar
- 15.Lampos, V., and Cristianini, N., Tracking the flu pandemic by monitoring the social web. Proceedings of the 2nd International Workshop on Cognitive Information Processing (CIP), 2010.Google Scholar