Situation monitoring of urban areas using social media data streams

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WEILER, Andreas, Michael GROSSNIKLAUS, Marc H. SCHOLL, 2016. Situation monitoring of urban areas using social media data streams. In: Information Systems. 57, pp. 129-141. ISSN 0306-4379. eISSN 1873-6076. Available under: doi: 10.1016/

@article{Weiler2016-04Situa-32343, title={Situation monitoring of urban areas using social media data streams}, year={2016}, doi={10.1016/}, volume={57}, issn={0306-4379}, journal={Information Systems}, pages={129--141}, author={Weiler, Andreas and Grossniklaus, Michael and Scholl, Marc H.} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:contributor>Scholl, Marc H.</dc:contributor> <dc:creator>Weiler, Andreas</dc:creator> <dcterms:available rdf:datatype="">2015-12-04T12:38:10Z</dcterms:available> <dc:creator>Grossniklaus, Michael</dc:creator> <dcterms:hasPart rdf:resource=""/> <dspace:hasBitstream rdf:resource=""/> <dc:language>eng</dc:language> <dc:contributor>Weiler, Andreas</dc:contributor> <dc:rights>terms-of-use</dc:rights> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:issued>2016-04</dcterms:issued> <dspace:isPartOfCollection rdf:resource=""/> <bibo:uri rdf:resource=""/> <dc:date rdf:datatype="">2015-12-04T12:38:10Z</dc:date> <dcterms:rights rdf:resource=""/> <dc:contributor>Grossniklaus, Michael</dc:contributor> <dcterms:title>Situation monitoring of urban areas using social media data streams</dcterms:title> <dcterms:abstract xml:lang="eng">The continuous growth of social networks and the active use of social media services result in massive amounts of user-generated data. Our goal is to leverage social media users as “social sensors” in order to increase the situational awareness within and about urban areas. In addition to the well-known challenges of event and topic detection and tracking, this task involves a spatial and temporal dimension. In this paper, we present a visualization that supports analysts in monitoring events/topics and emotions both in time and in space. The visualization uses a clock-face metaphor to encode temporal and spatial relationships, a color map to reflect emotion, and tag clouds for events and topics. A hierarchy of these clock-faces supports drilling down to finer levels of granularity as well as rolling up the vast and fast flow of information. In order to showcase these functionalities of our visualization, we discuss several case studies that use the live data stream of the Twitter microblogging service. Finally, we demonstrate the usefulness and usability of the visualization in a user study that we conducted.</dcterms:abstract> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Scholl, Marc H.</dc:creator> <dcterms:isPartOf rdf:resource=""/> </rdf:Description> </rdf:RDF>

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