NDLI: Investigating the Relationship between Social Media Content and Real-time Observations for Urban Air Quality and Public Health

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Investigating the Relationship between Social Media Content and Real-time Observations for Urban Air Quality and Public Health

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Investigating the Relationship between Social Media Content and Real-time Observations for Urban Air Quality and Public Health

Content Provider	ACM Digital Library
Author	Riga, Marina Karatzas, Kostas
Abstract	The rapid rise of Web 2.0 technologies and the popularity of social media, together with the broad use of low cost smart devices, changed dramatically the way users receive information, but also gave them the ability to become significant contributors of disseminated data. The challenge now is to benefit from large volumes of data and collective intelligence, so as to detect what people think or discuss in virtual communities, at the time that an event happens or the information is spread. Our domain of interest is the Urban Air Quality (UAQ) and public health. We want to promote the potential use of social media as a real-time source of "sensing" the environmental load or the existing environmental condition that affects directly humans' quality of life. With the use of the Self-Organizing Map (SOM), we analyze posts gathered from Twitter and we identify existing UAQ conditions, based on users' reports. Clusters of tweets with similar topics of discussion are formed. We additionally investigate the relations between citizens' reports and the corresponding, in time and location, actual observations of specific environmental characteristics. With a thorough investigation of SOM visualizations, we conclude that there is a positive correlation between personal observations and official data, highlighting thus the agreement among soft sensors' (users) and hard sensors' (monitoring sites) measurements.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450325387
DOI	10.1145/2611040.2611093
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-06-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text mining Participatory sensing Self-organizing maps Social media Air quality Twitter Clustering Computational intelligence
Content Type	Text
Resource Type	Article

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