learning to detect vandalism in social content systems a study on wikipedia

ID:7284489

大?。?48.46 KB

頁(yè)數(shù):23頁(yè)

時(shí)間:2018-02-10

learning to detect vandalism in social content systems  a study on wikipedia_第1頁(yè)
learning to detect vandalism in social content systems  a study on wikipedia_第2頁(yè)
learning to detect vandalism in social content systems  a study on wikipedia_第3頁(yè)
learning to detect vandalism in social content systems  a study on wikipedia_第4頁(yè)
learning to detect vandalism in social content systems  a study on wikipedia_第5頁(yè)
資源描述:

《learning to detect vandalism in social content systems a study on wikipedia》由會(huì)員上傳分享,免費(fèi)在線閱讀,更多相關(guān)內(nèi)容在工程資料-天天文庫(kù)。

1、LearningtoDetectVandalisminSocialContentSystems:AStudyonWikipediaVandalismDetectioninWikipediaSaraJavanmardi,DavidW.McDonald,RichCaruana,SholehForouzan,andCristinaV.LopesAbstractAchallengefacingusergeneratedcontentsystemsisvandalism,i.e.editsthatdamagecontentquality.Thehighvisibilityandeasy

2、accesstosocialnetworksmakesthempopulartargetsforvandals.Detectingandremovingvandalismiscrit-icalfortheseusergeneratedcontentsystems.Becausevandalismcantakemanyforms,therearemanydifferentkindsoffeaturesthatarepotentiallyusefulforde-tectingit.Thecomplexnatureofvandalism,andthelargenumberofpot

3、entialfea-tures,makevandalismdetectiondif?cultandtimeconsumingforhumaneditors.Machinelearningtechniquesholdpromisefordevelopingaccurate,tunable,andmaintainablemodelsthatcanbeincorporatedintovandalismdetectiontools.Wedescribeamethodfortrainingclassi?ersforvandalismdetectionthatyieldsclassi-?

4、ersthataremoreaccurateonthePAN2010corpusthanotherspreviouslydevel-oped.Becauseofthehighturnaroundinsocialnetworksystems,itisimportantforvandalismdetectiontoolstoruninreal-time.Tothisaim,weusefeatureselectionto?ndtheminimalsetoffeaturesconsistentwithhighaccuracy.Inaddition,becausesomefeature

5、saremorecostlytocomputethanothers,weusecost-sensitivefeatureselectiontoreducethetotalcomputationalcostofexecutingourmodels.Inadditiontothefeaturespreviouslyusedforspamdetection,weintroducenewfeaturesbasedonuseractionhistories.Theuserhistoryfeaturescontributesigni?cantlytoclassi-?erperforman

6、ce.Theapproachweuseisgeneralandcaneasilybeappliedtootherusergeneratedcontentsystems.S.Javanmardi(B)UniversityofCalifornia,IrvineDonaldBrenHall5042,Irvine,CA92697-3440,USAe-mail:sjavanma@ics.uci.eduD.W.McDonaldTheInformationSchool,UniversityofWashington,Washington,WA,USAR.CaruanaMicrosoftRes

7、earch,Redmond,WA,USAS.Forouzan·C.V.LopesBrenSchoolofInformationandComputerSciences,UniversityofCalifornia,Irvine,CA,USAT.?zyeretal.(eds.),MiningSocialNetworksandSecurityInformatics,203LectureNotesinSocialNetworks,DOI10.1007/978-94-007-6359-3_11,?Springer

當(dāng)前文檔最多預(yù)覽五頁(yè),下載文檔查看全文

此文檔下載收益歸作者所有

當(dāng)前文檔最多預(yù)覽五頁(yè),下載文檔查看全文
溫馨提示:
1. 部分包含數(shù)學(xué)公式或PPT動(dòng)畫的文件,查看預(yù)覽時(shí)可能會(huì)顯示錯(cuò)亂或異常,文件下載后無(wú)此問(wèn)題,請(qǐng)放心下載。
2. 本文檔由用戶上傳,版權(quán)歸屬用戶,天天文庫(kù)負(fù)責(zé)整理代發(fā)布。如果您對(duì)本文檔版權(quán)有爭(zhēng)議請(qǐng)及時(shí)聯(lián)系客服。
3. 下載前請(qǐng)仔細(xì)閱讀文檔內(nèi)容,確認(rèn)文檔內(nèi)容符合您的需求后進(jìn)行下載,若出現(xiàn)內(nèi)容與標(biāo)題不符可向本站投訴處理。
4. 下載文檔時(shí)可能由于網(wǎng)絡(luò)波動(dòng)等原因無(wú)法下載或下載錯(cuò)誤,付費(fèi)完成后未能成功下載的用戶請(qǐng)聯(lián)系客服處理。
关闭