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dc.contributor.authorDong, Xiaowen
dc.contributor.authorMavroeidis, Dimitrios
dc.contributor.authorCalabrese, Francesco
dc.contributor.authorFrossard, Pascal
dc.date.accessioned2016-07-01T17:05:34Z
dc.date.available2016-07-01T17:05:34Z
dc.date.issued2015-06
dc.date.submitted2014-04
dc.identifier.issn1384-5810
dc.identifier.issn1573-756X
dc.identifier.urihttp://hdl.handle.net/1721.1/103417
dc.description.abstractEvent detection has been one of the most important research topics in social media analysis. Most of the traditional approaches detect events based on fixed temporal and spatial resolutions, while in reality events of different scales usually occur simultaneously, namely, they span different intervals in time and space. In this paper, we propose a novel approach towards multiscale event detection using social media data, which takes into account different temporal and spatial scales of events in the data. Specifically, we explore the properties of the wavelet transform, which is a well-developed multiscale transform in signal processing, to enable automatic handling of the interaction between temporal and spatial scales. We then propose a novel algorithm to compute a data similarity graph at appropriate scales and detect events of different scales simultaneously by a single graph-based clustering process. Furthermore, we present spatiotemporal statistical analysis of the noisy information present in the data stream, which allows us to define a novel term-filtering procedure for the proposed event detection algorithm and helps us study its behavior using simulated noisy data. Experimental results on both synthetically generated data and real world data collected from Twitter demonstrate the meaningfulness and effectiveness of the proposed approach. Our framework further extends to numerous application domains that involve multiscale and multiresolution data analysis.en_US
dc.description.sponsorshipSwiss National Science Foundation (Mobility Fellowship)en_US
dc.publisherSpringer USen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10618-015-0421-2en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSpringer USen_US
dc.titleMultiscale event detection in social mediaen_US
dc.typeArticleen_US
dc.identifier.citationDong, Xiaowen, Dimitrios Mavroeidis, Francesco Calabrese, and Pascal Frossard. “Multiscale Event Detection in Social Media.” Data Min Knowl Disc 29, no. 5 (June 13, 2015): 1374–1405.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.departmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)en_US
dc.contributor.mitauthorDong, Xiaowenen_US
dc.relation.journalData Mining and Knowledge Discoveryen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2016-05-23T12:16:28Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.orderedauthorsDong, Xiaowen; Mavroeidis, Dimitrios; Calabrese, Francesco; Frossard, Pascalen_US
dspace.embargo.termsNen
dc.identifier.orcidhttps://orcid.org/0000-0002-1143-9786
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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