Event identification for local areas using social media streaming data

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WEILER, Andreas, Marc H. SCHOLL, Franz WANNER, Christian ROHRDANTZ, 2013. Event identification for local areas using social media streaming data. the ACM SIGMOD Workshop. New York, New York, 22. Jun 2013 - 27. Jun 2013. In: Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks - DBSocial '13. the ACM SIGMOD Workshop. New York, New York, 22. Jun 2013 - 27. Jun 2013. New York, New York, USA:ACM Press, pp. 1-6. ISBN 978-1-4503-2191-4

@inproceedings{Weiler2013Event-24342, title={Event identification for local areas using social media streaming data}, year={2013}, doi={10.1145/2484702.2484703}, isbn={978-1-4503-2191-4}, address={New York, New York, USA}, publisher={ACM Press}, booktitle={Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks - DBSocial '13}, pages={1--6}, author={Weiler, Andreas and Scholl, Marc H. and Wanner, Franz and Rohrdantz, Christian} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/24342"> <dc:creator>Rohrdantz, Christian</dc:creator> <dc:contributor>Wanner, Franz</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-08-28T14:48:59Z</dc:date> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/24342"/> <dc:creator>Wanner, Franz</dc:creator> <dc:contributor>Rohrdantz, Christian</dc:contributor> <dcterms:abstract xml:lang="eng">Unprecedented success and active usage of social media services result in massive amounts of user-generated data. An increasing interest in the contained information from social media data leads to more and more sophisticated analysis and visualization applications. Because of the fast pace and distribution of news in social media data it is an appropriate source to identify events in the data and directly display their occurrence to analysts or other users. This paper presents a method for event identification in local areas using the Twitter data stream. We implement and use a combined log-likelihood ratio approach for the geographic and time dimension of real-life Twitter data in predefined areas of the world to detect events occurring in the message contents. We present a case study with two interesting scenarios to show the usefulness of our approach.</dcterms:abstract> <dc:language>eng</dc:language> <dcterms:title>Event identification for local areas using social media streaming data</dcterms:title> <dc:creator>Scholl, Marc H.</dc:creator> <dcterms:bibliographicCitation>Proceedings of the 3rd ACM SIGMOD Workshop on Databases and Social Networks : DBSocial 2013; New York, NY, USA, June 23 2013 / Kristen LeFevre, Ashwin Machanavajjhala, Adam Silberstein (Conference Chairs). - New York, NY : ACM, 2013. - S. 1-6. - ISBN 978-1-4503-2191-4</dcterms:bibliographicCitation> <dc:creator>Weiler, Andreas</dc:creator> <dc:contributor>Weiler, Andreas</dc:contributor> <dc:contributor>Scholl, Marc H.</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-08-28T14:48:59Z</dcterms:available> <dcterms:issued>2013</dcterms:issued> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dc:rights>deposit-license</dc:rights> </rdf:Description> </rdf:RDF>

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