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Open AccessArticle

Understanding Public Opinions from Geosocial Media

by Shanqi Zhang 1,* and Rob Feick 2
Department of Geography and Environment Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
School of Planning, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Author to whom correspondence should be addressed.
Academic Editors: Songnian Li, Suzana Dragicevic, Xiaohua Tong and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(6), 74;
Received: 30 January 2016 / Revised: 9 May 2016 / Accepted: 13 May 2016 / Published: 24 May 2016
(This article belongs to the Special Issue Bridging the Gap between Geospatial Theory and Technology)
Increasingly, social media data are linked to locations through embedded GPS coordinates. Many local governments are showing interest in the potential to repurpose these firsthand geo-data to gauge spatial and temporal dynamics of public opinions in ways that complement information collected through traditional public engagement methods. Using these geosocial data is not without challenges since they are usually unstructured, vary in quality, and often require considerable effort to extract information that is relevant to local governments’ needs from large data volumes. Understanding local relevance requires development of both data processing methods and their use in empirical studies. This paper addresses this latter need through a case study that demonstrates how spatially-referenced Twitter data can shed light on citizens’ transportation and planning concerns. A web-based toolkit that integrates text processing methods is used to model Twitter data collected for the Region of Waterloo (Ontario, Canada) between March 2014 and July 2015 and assess citizens’ concerns related to the planning and construction of a new light rail transit line. The study suggests that geosocial media can help identify geographies of public perceptions concerning public facilities and services and have potential to complement other methods of gauging public sentiment. View Full-Text
Keywords: geosocial media; topic modelling; text analysis; public sentiment; user-generated content geosocial media; topic modelling; text analysis; public sentiment; user-generated content
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Zhang, S.; Feick, R. Understanding Public Opinions from Geosocial Media. ISPRS Int. J. Geo-Inf. 2016, 5, 74.

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