Topical event detection on Twitter
Uloženo v:
| Název: | Topical event detection on Twitter |
|---|---|
| Autoři: | Lishan Cui, Xiuzhen Zhang, Xiangmin Zhou, Flora Salim |
| Rok vydání: | 2016 |
| Témata: | Database systems, Burst detection, Dynamic topic modelling, Event detection, Topic mutation |
| Popis: | Event detection on Twitter has attracted active research. Although existing work considers the semantic topic structure of documents for event detection, the topic dynamics and the semantic consistency are under-investigated. In this paper, we study the problem of topical event detection in tweet streams. We define topical events as the bursty occurrences of semantically consistent topics. We decompose the problem of topical event detection into two components: (1) We address the issue of the semantic incoherence of the evolution of topics. We propose to improve topic modelling to filter out semantically inconsistent dynamic topics. (2) We propose to perform burst detection on the time series of dynamic topics to detect bursty occurrences. We apply our proposed techniques to the real world application by detecting topical events in public transport tweets. Experiments demonstrate that our approach can detect the newsworthy events with high success rate. |
| Druh dokumentu: | conference object |
| Jazyk: | unknown |
| Relation: | 10779/rmit.27384603.v1; https://figshare.com/articles/conference_contribution/Topical_event_detection_on_Twitter/27384603 |
| Dostupnost: | https://figshare.com/articles/conference_contribution/Topical_event_detection_on_Twitter/27384603 |
| Rights: | All rights reserved |
| Přístupové číslo: | edsbas.AC64FB7C |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | FullText | Text: Availability: 0 CustomLinks: – Url: https://figshare.com/articles/conference_contribution/Topical_event_detection_on_Twitter/27384603# Name: EDS - BASE (s4221598) Category: fullText Text: View record from BASE – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Cui%20L Name: ISI Category: fullText Text: Nájsť tento článok vo Web of Science Icon: https://imagesrvr.epnet.com/ls/20docs.gif MouseOverText: Nájsť tento článok vo Web of Science |
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| Items | – Name: Title Label: Title Group: Ti Data: Topical event detection on Twitter – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lishan+Cui%22">Lishan Cui</searchLink><br /><searchLink fieldCode="AR" term="%22Xiuzhen+Zhang%22">Xiuzhen Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Xiangmin+Zhou%22">Xiangmin Zhou</searchLink><br /><searchLink fieldCode="AR" term="%22Flora+Salim%22">Flora Salim</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2016 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Database+systems%22">Database systems</searchLink><br /><searchLink fieldCode="DE" term="%22Burst+detection%22">Burst detection</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+topic+modelling%22">Dynamic topic modelling</searchLink><br /><searchLink fieldCode="DE" term="%22Event+detection%22">Event detection</searchLink><br /><searchLink fieldCode="DE" term="%22Topic+mutation%22">Topic mutation</searchLink> – Name: Abstract Label: Description Group: Ab Data: Event detection on Twitter has attracted active research. Although existing work considers the semantic topic structure of documents for event detection, the topic dynamics and the semantic consistency are under-investigated. In this paper, we study the problem of topical event detection in tweet streams. We define topical events as the bursty occurrences of semantically consistent topics. We decompose the problem of topical event detection into two components: (1) We address the issue of the semantic incoherence of the evolution of topics. We propose to improve topic modelling to filter out semantically inconsistent dynamic topics. (2) We propose to perform burst detection on the time series of dynamic topics to detect bursty occurrences. We apply our proposed techniques to the real world application by detecting topical events in public transport tweets. Experiments demonstrate that our approach can detect the newsworthy events with high success rate. – Name: TypeDocument Label: Document Type Group: TypDoc Data: conference object – Name: Language Label: Language Group: Lang Data: unknown – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: 10779/rmit.27384603.v1; https://figshare.com/articles/conference_contribution/Topical_event_detection_on_Twitter/27384603 – Name: URL Label: Availability Group: URL Data: https://figshare.com/articles/conference_contribution/Topical_event_detection_on_Twitter/27384603 – Name: Copyright Label: Rights Group: Cpyrght Data: All rights reserved – Name: AN Label: Accession Number Group: ID Data: edsbas.AC64FB7C |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: unknown Subjects: – SubjectFull: Database systems Type: general – SubjectFull: Burst detection Type: general – SubjectFull: Dynamic topic modelling Type: general – SubjectFull: Event detection Type: general – SubjectFull: Topic mutation Type: general Titles: – TitleFull: Topical event detection on Twitter Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lishan Cui – PersonEntity: Name: NameFull: Xiuzhen Zhang – PersonEntity: Name: NameFull: Xiangmin Zhou – PersonEntity: Name: NameFull: Flora Salim IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2016 Identifiers: – Type: issn-locals Value: edsbas |
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