Open AccessApplying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza
Author(s) -
Christopher Allen,
Ming–Hsiang Tsou,
Anoshé A Aslam,
Angel,
Jean Mark Gawron
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0157734
Subject(s) - social media , outbreak , data science , geographic information system , computer science , public health surveillance , geography , process (computing) , covid-19 , data mining , public health , cartography , world wide web , medicine , virology , infectious disease (medical specialty) , pathology , operating system , disease
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.