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Allen C, Tsou MH, Aslam A, et al. Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza. PLoS One. 2016;11(7):e0157734doi: 10.1371/journal.pone.0157734.
Allen, C., Tsou, M. H., Aslam, A., Nagel, A., & Gawron, J. M. (2016). Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza. PloS one, 11(7), e0157734. https://doi.org/10.1371/journal.pone.0157734
Allen, Chris, et al. "Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza." PloS one vol. 11,7 (2016): e0157734. doi: https://doi.org/10.1371/journal.pone.0157734
Allen C, Tsou MH, Aslam A, Nagel A, Gawron JM. Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza. PLoS One. 2016 Jul 25;11(7):e0157734. doi: 10.1371/journal.pone.0157734. eCollection 2016. PMID: 27455108; PMCID: PMC4959719.
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PLoS One. 2016 Jul 25;11(7):e0157734. doi: 10.1371/journal.pone.0157734. eCollection 2016.

Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza.

PloS one

Chris Allen, Ming-Hsiang Tsou, Anoshe Aslam, Anna Nagel, Jean-Mark Gawron

Affiliations

  1. Department of Geography, San Diego State University, San Diego, California, United States of America.
  2. Graduate School of Public Health, San Diego State University, San Diego, California, United States of America.
  3. Department of Linguistics, San Diego State University, San Diego, California, United States of America.

PMID: 27455108 PMCID: PMC4959719 DOI: 10.1371/journal.pone.0157734

Abstract

Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013-2014 flu season. The results of this procedure are compared with national, regional, and local flu outbreak reports, revealing a statistically significant correlation between the two data sources. The main contribution of this paper is to introduce a comprehensive data mining process that enhances previous attempts to accurately identify tweets related to influenza. Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages.

References

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