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A crowd sensing system identifying geotopics and community interests from user-generated content

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This paper presents a crowd sensing system (CSS) that captures geospatial social media topics and allows the review of results. Using Web-resources derived from social media platforms, the CSS uses a spatially-situated social network graph to harvest user-generated content from selected organizations and members of the public. This allows ‘passively’ contributed social media-based opinions, along with different variables, such as time, location, social interaction, service usage, and human activities to be examined and used to identify trending views and influential citizens. The data model and CSS are used for demonstration purposes to identify geotopics and community interests relevant to municipal affairs in the City of Toronto, Canada.
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Keywords: Social media; big data; data models; public opinion; public participation; smart city

Document Type: Research Article

Affiliations: 1: Department of Geography, McGill University, Montreal, QC, Canada 2: ESRI Canada, Toronto, ON, Canada

Publication date: August 3, 2019

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