@article{allen_applying_2016,
	title = {Applying {GIS} and {Machine} {Learning} {Methods} to {Twitter} {Data} for {Multiscale} {Surveillance} of {Influenza}},
	volume = {11},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0157734},
	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.},
	language = {en},
	number = {7},
	urldate = {2020-02-27},
	journal = {PLOS ONE},
	author = {Allen, Chris and Tsou, Ming-Hsiang and Aslam, Anoshe and Nagel, Anna and Gawron, Jean-Mark},
	month = jul,
	year = {2016},
	note = {00000 
Publisher: Public Library of Science},
	keywords = {Geographic information systems, Influenza, Machine learning, Machine learning algorithms, Public and occupational health, Social media, Support vector machines, Twitter},
	pages = {e0157734},
}

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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.","language":"en","number":"7","urldate":"2020-02-27","journal":"PLOS ONE","author":[{"propositions":[],"lastnames":["Allen"],"firstnames":["Chris"],"suffixes":[]},{"propositions":[],"lastnames":["Tsou"],"firstnames":["Ming-Hsiang"],"suffixes":[]},{"propositions":[],"lastnames":["Aslam"],"firstnames":["Anoshe"],"suffixes":[]},{"propositions":[],"lastnames":["Nagel"],"firstnames":["Anna"],"suffixes":[]},{"propositions":[],"lastnames":["Gawron"],"firstnames":["Jean-Mark"],"suffixes":[]}],"month":"July","year":"2016","note":"00000 Publisher: Public Library of Science","keywords":"Geographic information systems, Influenza, Machine learning, Machine learning algorithms, Public and occupational health, Social media, Support vector machines, Twitter","pages":"e0157734","bibtex":"@article{allen_applying_2016,\n\ttitle = {Applying {GIS} and {Machine} {Learning} {Methods} to {Twitter} {Data} for {Multiscale} {Surveillance} of {Influenza}},\n\tvolume = {11},\n\tissn = {1932-6203},\n\turl = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0157734},\n\tdoi = {10.1371/journal.pone.0157734},\n\tabstract = {Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. 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Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2020-02-27},\n\tjournal = {PLOS ONE},\n\tauthor = {Allen, Chris and Tsou, Ming-Hsiang and Aslam, Anoshe and Nagel, Anna and Gawron, Jean-Mark},\n\tmonth = jul,\n\tyear = {2016},\n\tnote = {00000 \nPublisher: Public Library of Science},\n\tkeywords = {Geographic information systems, Influenza, Machine learning, Machine learning algorithms, Public and occupational health, Social media, Support vector machines, Twitter},\n\tpages = {e0157734},\n}\n\n","author_short":["Allen, C.","Tsou, M.","Aslam, A.","Nagel, A.","Gawron, J."],"key":"allen_applying_2016","id":"allen_applying_2016","bibbaseid":"allen-tsou-aslam-nagel-gawron-applyinggisandmachinelearningmethodstotwitterdataformultiscalesurveillanceofinfluenza-2016","role":"author","urls":{"Paper":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0157734"},"keyword":["Geographic information systems","Influenza","Machine learning","Machine learning algorithms","Public and occupational health","Social media","Support vector machines","Twitter"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/fortinm","dataSources":["TLSfqfvpu3sQJ89pA"],"keywords":["geographic information systems","influenza","machine learning","machine learning algorithms","public and occupational health","social media","support vector machines","twitter"],"search_terms":["applying","gis","machine","learning","methods","twitter","data","multiscale","surveillance","influenza","allen","tsou","aslam","nagel","gawron"],"title":"Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza","year":2016}