Stability Evaluation of Event Detection Techniques for Twitter


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WEILER, Andreas, Joeran BEEL, Bela GIPP, Michael GROSSNIKLAUS, 2016. Stability Evaluation of Event Detection Techniques for Twitter. 15th International Symposium, IDA 2016. Stockholm, 13. Okt 2016 - 15. Okt 2016. In: BOSTRÖM, Henrik, ed. and others. Advances in Intelligent Data Analysis XV : 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings. Cham:Springer, pp. 368-380. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-46348-3. Available under: doi: 10.1007/978-3-319-46349-0_32

@inproceedings{Weiler2016-09-21Stabi-35785, title={Stability Evaluation of Event Detection Techniques for Twitter}, year={2016}, doi={10.1007/978-3-319-46349-0_32}, number={9897}, isbn={978-3-319-46348-3}, issn={0302-9743}, address={Cham}, publisher={Springer}, series={Lecture Notes in Computer Science}, booktitle={Advances in Intelligent Data Analysis XV : 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings}, pages={368--380}, editor={Boström, Henrik}, author={Weiler, Andreas and Beel, Joeran and Gipp, Bela and Grossniklaus, Michael} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:contributor>Beel, Joeran</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:contributor>Gipp, Bela</dc:contributor> <dc:contributor>Weiler, Andreas</dc:contributor> <dspace:hasBitstream rdf:resource=""/> <dcterms:title>Stability Evaluation of Event Detection Techniques for Twitter</dcterms:title> <dc:language>eng</dc:language> <dc:date rdf:datatype="">2016-10-31T09:33:03Z</dc:date> <dcterms:rights rdf:resource=""/> <dc:rights>terms-of-use</dc:rights> <bibo:uri rdf:resource=""/> <dcterms:abstract xml:lang="eng">Twitter continues to gain popularity as a source of up-to-date news and information. As a result, numerous event detection techniques have been proposed to cope with the steadily increasing rate and volume of social media data streams. Although most of these works conduct some evaluation of the proposed technique, comparing their effectiveness is a challenging task. In this paper, we examine the challenges to reproducing evaluation results for event detection techniques. We apply several event detection techniques and vary four parameters, namely time window (15 vs. 30 vs. 60 mins), stopwords (include vs. exclude), retweets (include vs. exclude), and the number of terms that define an event (1...5 terms). Our experiments use real-world Twitter streaming data and show that varying these parameters alone significantly influences the outcomes of the event detection techniques, sometimes in unforeseen ways. We conclude that even minor variations in event detection techniques may lead to major difficulties in reproducing experiments.</dcterms:abstract> <dspace:isPartOfCollection rdf:resource=""/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Beel, Joeran</dc:creator> <dc:creator>Weiler, Andreas</dc:creator> <dcterms:isPartOf rdf:resource=""/> <dc:creator>Gipp, Bela</dc:creator> <dcterms:issued>2016-09-21</dcterms:issued> <dcterms:available rdf:datatype="">2016-10-31T09:33:03Z</dcterms:available> <dc:contributor>Grossniklaus, Michael</dc:contributor> <dc:creator>Grossniklaus, Michael</dc:creator> <dcterms:hasPart rdf:resource=""/> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 31.10.2016 (Informationen über die Zugriffsstatistik)

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