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1、TypicalityinComputerMediatedDiscussions
2、AnAnalysiswithNeuralNetworks
3、MichaelR.BertholdandFaySudweeksAbstract
4、ProjectH,alargegroupofRafaeli[4,5],RogersandRafaeli[8]andinternationalresearchers,producedaRafaeliandSudweeks[6]arguethatthevariablehugeamountofdatafromcompute
5、rme-thataectstheinteractivenatureofcomputer-diateddiscussions.Thedataclassiedmediatedcommunication(CMC)isthetheor-severalthousandpostingsfrommorethaneticalconstructofinteractivity
6、theextentthirtynewsgroups.Oneapproachtoex-towhichmessagesinasequencerelatetoeachtractt
7、ypicalmessagesfromthisdatabaseother,andespeciallytheextenttowhichlaterispresentedinthispaper.Anautoassoci-messagesrecounttherelatednessofearliermes-ativeneuralnetworkwastrainedon3000sages.codedmessagesandthenusedtocon-Inthispaperweuseanautoassociativeneuralstructtypic
8、almessagesundercertainnetwork(ANN)toanalyseandexplorecom-speciedconditionsforseveralscenarios.municationdensity,theextenttowhichmes-Thispaperillustratesthearchitectureofsagesinathreadcohere.Thedatasetcom-theneuralnetworkthatwasusedandprises3000postingsto30newsgroupsc
9、lassiedexplainsthenecessarymodicationstoon46variablesorgroupsoffeatures.Inthethecodingformat.Inadditionseveralcontextofcategorisation,eachvariableequatestypicalitysets"producedbytheneuralwithareferencepointorfeaturewithinsomein-netareshownandtheirgenerationisex-for
10、mationsetting.WeproposethattheANNisplained.InconclusiontheANNisusedcapableofidentifyingthefeaturesofmessagestoexplorethetypesofmessagesthattyp-thattypicallyinitiateorcontributetolongericallyinitiateorcontributetolongerlast-lastingthreads.Ourndingssupportthecon-ingthr
11、eads.structofinteractivityasavariableofcommu-nicationsettings.WealsodemonstratethatanI.IntroductionANNisavaluablepreprocessortootherana-lyticalmethods.Ascomputernetworksexpandintohomesandorganisations,andhigh-speednetworkhighwaysII.TheDataprovideamediumforcommunicatio
12、nandcom-A.Preprocessingmunityformationonascalethathasneverbeenfeasiblebefore,newmoresarecreated.PeopleThedatasetwascreatedby