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. 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

Chris Allen  1 , Ming-Hsiang Tsou  1 , Anoshe Aslam  2 , Anna Nagel  2 , Jean-Mark Gawron  3
Affiliations

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

Chris Allen et al. PLoS One. .

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. National ILI compared to the aggregated tweet rates for all study cities.
Fig 2
Fig 2. Local sentinel-provided ILI compared to the tweet rate for Fort Worth (a), Nashville (b), Cleveland (c), and Boston (d).
Fig 3
Fig 3. Map showing the correlation rank for each region.
See this image and copyright information in PMC

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