Leveraging Temporal Markers to Detect Event from Microblogs

Int. J. Knowl. Soc. Res. Pub Date : 2017-07-01 DOI:10.4018/IJKSR.2017070104

Soumaya Cherichi, R. Faiz

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引用次数: 1

Abstract

Oneofthemarvelsofourtimeistheunprecedenteddevelopmentanduseoftechnologiesthatsupport socialinteraction.Socialmediatingtechnologieshaveengenderedradicallynewwaysofinformation andcommunication,particularlyduringevents;incaseofnaturaldisasterlikeearthquakestsunami andAmericanpresidentialelection.ThispaperisbasedondataobtainedfromTwitterbecauseof itspopularityandsheerdatavolume.Thiscontentcanbecombinedandprocessedtodetectevents, entitiesandpopularmoodstofeedvariousnewlarge-scaledata-analysisapplications.Onthedownside, thesecontentitemsareverynoisyandhighlyinformal,makingitdifficulttoextractsenseoutofthe stream.Takingtoaccountallthedifficulties,weproposeaneweventdetectionapproachcombining linguisticfeaturesandTwitterfeatures.Finally,wepresentoursystemthataims(1)detectnewevents, (2)torecognizetemporalmarkerspatternofanevent,(3)andtoclassifyimportanteventsaccording tothematicpertinence,authorpertinenceandtweetvolume. KEywoRDS Clustering, Event Detection, Microblogs, NLP, Patterns, Social Network Analysis, Temporal Markers, Twitter

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利用时间标记从微博中检测事件

Oneofthemarvelsofourtimeistheunprecedenteddevelopmentanduseoftechnologiesthatsupport socialinteraction。Socialmediatingtechnologieshaveengenderedradicallynewwaysofinformation andcommunication，particularlyduringevents;incaseofnaturaldisasterlikeearthquakestsunami andAmericanpresidentialelection。ThispaperisbasedondataobtainedfromTwitterbecauseof itspopularityandsheerdatavolume。Thiscontentcanbecombinedandprocessedtodetectevents, entitiesandpopularmoodstofeedvariousnewlarge-scaledata-analysisapplications。Onthedownside, thesecontentitemsareverynoisyandhighlyinformal，makingitdifficulttoextractsenseoutofthe流。Takingtoaccountallthedifficulties，weproposeaneweventdetectionapproachcombining linguisticfeaturesandTwitterfeatures.Finally，wepresentoursystemthataims(1)detectnewevents， (2)torecognizetemporalmarkerspatternofanevent， (3)andtoclassifyimportanteventsaccording tothematicpertinence，authorpertinenceandtweetvolume。关键词聚类，事件检测，微博，自然语言处理，模式，社会网络分析，时间标记，推特

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来源期刊

Int. J. Knowl. Soc. Res.

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