ABSTRACT
Twitter is a unique social media channel, in the sense that users discuss and talk about the most diverse topics, including their health conditions. In this paper we analyze how Dengue epidemic is reflected on Twitter and to what extent that information can be used for the sake of surveillance. Dengue is a mosquito-borne infectious disease that is a leading cause of illness and death in tropical and subtropical regions, including Brazil. We propose an active surveillance methodology that is based on four dimensions: volume, location, time and public perception. First we explore the public perception dimension by performing sentiment analysis. This analysis enables us to filter out content that is not relevant for the sake of Dengue surveillance. Then, we verify the high correlation between the number of cases reported by official statistics and the number of tweets posted during the same time period (i.e., R2 = 0.9578). A clustering approach was used in order to exploit the spatio-temporal dimension, and the quality of the clusters obtained becomes evident when they are compared to official data (i.e., RandIndex = 0.8914). As an application, we propose a Dengue surveillance system that shows the evolution of the dengue situation reported in tweets, which is implemented in www.observatorio.inweb.org.br/dengue/.
- H. Achrekar, A. Gandhe, R. Lazarus, S. Yu, and B. Liu. Predicting flu trends using twitter data. In International Workshop on Cyber-Physical Networking Systems, 2011.Google ScholarCross Ref
- R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In ACM SIGMOD International Conference on Management of Data, pages 207--216. ACM, 1993. Google ScholarDigital Library
- S. Asur and B. A. Huberman. Predicting the future with social media. CoRR, abs/1003.5699, 2010.Google Scholar
- D. Birant and A. Kut. St-dbscan: An algorithm for clustering spatial-temporal data. Data Knowl. Eng., 60:208--221, January 2007. Google ScholarDigital Library
- Centers for Disease Control and Prevention. http://www.cdc.gov/dengue/.Google Scholar
- M. Cha, H. Haddadi, F. Benevenuto, and K. P. Gummadi. Measuring user influence in twitter: The million follower fallacy. In International AAAI Conference on Weblogs and Social Media. AAAI Press, May 2010.Google Scholar
- L. Chen, H. Achrekar, B. Liu, and R. Lazarus. Vision: towards real time epidemic vigilance through online social networks. In ACM Workshop on Mobile Cloud Computing Services: Social Networks and Beyond, pages 1--5. ACM, 2010. Google ScholarDigital Library
- C. Chew and G. Eysenbach. Pandemics in the age of twitter: Content analysis of tweets during the 2009 h1n1 outbreak. PLoS ONE, 5(11):e14118, 11 2010.Google ScholarCross Ref
- DATASUS Dengue. http://bit.ly/dGtFst.Google Scholar
- Epidemiological report summary on Dengue. http://portal.saude.gov.br/portal/arquivos/pdf/informe_dengue_2011_janeiro_e_marco_13_04.pdf.Google Scholar
- M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In International Conference on Knowledge Discovery and Data Mining, pages 226--231. AAAI Press, 1996.Google ScholarDigital Library
- G. Eysenbach. Infodemiology:tracking flu-related searches on the web for syndromic surveillance. In AMIA Annu Symp Proc., pages 244--248, 2006.Google Scholar
- G. Eysenbach. Infodemiology and infoveillance: Framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the internet. J Med Internet Res., 11:e11, 2009.Google ScholarCross Ref
- J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski, and L. Brilliant. Detecting influenza epidemics using search engine query data. Nature, 457:1012--4, 2009.Google ScholarCross Ref
- S. Goel, J. M. Hofman, S. Lahaie, D. M. Pennock, and D. J. Watts. Predicting consumer behavior with web search. Proceedings of the National Academy of Sciences, 107(41):17486--17490, October 2010.Google ScholarCross Ref
- Google Geocoding API. http://code.google.com/intl/en/apis/maps/documentation/geocoding/.Google Scholar
- S. B. Halstead. Dengue. In Lancet, pages 1644--1652, 2007.Google Scholar
- A. Hulth, G. Rydevik, and A. Linde. Web queries as a source for syndromic surveillance. PLoS ONE, 4(2):e4378, 02 2009.Google ScholarCross Ref
- J. Kivinen and H. Mannila. The power of sampling in knowledge discovery. In ACM SIGACT- SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), pages 77--85. ACM, 1994. Google ScholarDigital Library
- V. Lampos and N. Cristianini. Tracking the flu pandemic by monitoring the social web. In Workshop on Cognitive Information Processing (CIP 2010), pages 411--416. IEEE Press, 2010.Google ScholarCross Ref
- V. Lampos, T. De Bie, and N. Cristianini. Flu detector: tracking epidemics on twitter. In European conference on Machine learning and knowledge discovery in databases, pages 599--602. Springer-Verlag, 2010. Google ScholarDigital Library
- P. M. Polgreen, Y. Chen, D. M. Pennock, F. D. Nelson, and R. A. Weinstein. Using internet searches for influenza surveillance. Clinical Infectious Diseases, 47:1443--1448, 2008.Google ScholarCross Ref
- S. Runge-Ranzinger, O. Horstick, M. Marx, and A. Kroeger. What does dengue disease surveillance contribute to predicting and detecting outbreaks and describing trends? Tropical Medicine International Health, 13(8):1022--1041, 2008.Google ScholarCross Ref
- T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In International conference on World wide web, pages 851--860. ACM, 2010. Google ScholarDigital Library
- A. Tumasjan, T. Sprenger, P. Sandner, and I. Welpe. Predicting elections with twitter: What 140 characters reveal about political sentiment. In International AAAI Conference on Weblogs and Social Media. AAAI Press, 2010.Google Scholar
- Twitter Streaming API. http://apiwiki.twitter.com/.Google Scholar
- A. Veloso, W. Meira Jr., and M. J. Zaki. Lazy associative classification. In International Conference on Data Mining, pages 645--654. IEEE Computer Society, 2006. Google ScholarDigital Library
- World Health Organization. http://www.who.int/tdr/diseases/default.htm.Google Scholar
Index Terms
- Dengue surveillance based on a computational model of spatio-temporal locality of Twitter
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