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THANKYOUTOOURSPONSORS

TheInstituteforSocialResearch(ISR)istheworld’slargestacademicsocialsciencesurveyandresearchorganization.TheISRisaleaderindevelopingandapplyingnewsocialsciencemethods,andcommittedtoeducatingthenextgenerationofsocialscientists.Itconsistsoffiveseparatebutinterdependentresearchcentersthatspanmorethan20academicdisciplinesinthesocialsciences.Inadditiontoworld-leadingsurveystudies,ISRresearchscientistsdevelopandtestsurveymethodologies;conductexperimentalstudies;maintainanddistributetheworld’slargestarchiveofcomputerizedsocialsciencedata;andtrainandeducateresearchersandstudentsfromaroundtheworld.

TheCenterforPoliticalStudies(CPS)attheInstituteforSocialResearchisrecognizedaroundtheworldasaleaderinthequantitativestudyofpoliticsandaninnovatorinresearchmethodsandtraining.Itsscholarsinvestigatetheinteractionsamongpublicopinion,politicalaction,politicalprocesses,elections,institutions,politicalparties,andindividuals–themesunitedbyaconcernfordemocraticprocess.CPSproducesdatasetsusedaroundtheworld,infrastructureforthesocialsciences,andofferstrainingprogramsforstudentswhogoontoworkinacademiaandthroughouttheprivatesectorandgovernment.

TheMichiganInstituteforDataScience(MIDAS)isahighlyreputedcenterofexcellenceinarangeofareasrelatedtoDataScienceandArtificialIntelligence.ItservesasthefocalpointofDataScienceattheUniversityofMichigan(U-M),andservesacommunityofmorethan260datasciencefacultymembersinterestedincollaboration.Theirexpertiseencompassestheoreticalfoundationsofdatascience,awiderangeofdatasciencemethodology,anditsapplicationsinalmostallresearchdomainsatU-M.

TheSocialScienceandSocialMediaCollaborative(S3MC)incorporatesthreeparallelprojectsinsurveymethodology,politicalcommunication,andparentingresearchtoidentifyopportunitiesforblendingsocialscienceandsocialmediadatatoimprovesocialscienceinference.

#ParentingisaRackhamInterdisciplinaryWorkshop(RIW)thatprovidesaforumforfacultyandgraduatestudentstoaddressissuesrelatedtoparentingresearch.Theworkshopaimstoprovideanintellectuallystimulatingenvironmentthatpromotescrossdisciplinarydialogue,informationsharing,andcollaborationbetweenfacultyandgraduatestudentsstudyingparenting.Theworkshopalsoaimstodiscoverandapplyvariousmethodologiestothestudyofparenting.

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PRELIMINARYPROGRAM

8:30a.m. REGISTRATION/MORNINGCOFFEE

8:45a.m. WELCOMEANDOPENINGREMARKSJuleKrüger,#UMTweetCon2019Organizer(CenterforPoliticalStudies,InstituteforSocialResearch)

9:00a.m. PANEL1:ISSUEFRAMING,JOURNALISTNETWORKS,ANDTWITTERINTHECLASSROOMDiscussant:WalterMebaneJr.(DepartmentofPoliticalScience,andStatistics)

• “FramingAthleteActivism:DifferencebetweenTraditionalMediaandSocialMedia”WencheWang(SchoolofKinesiology),Stacy-LynnSant(SchoolofKinesiology)

• “ProfessionalNetworksamongPoliticalJournalists”MichaelTraugott(CenterforPoliticalStudies,ISR)

• “UsingTwittertoTeachDentalTreatmentPlanning”VidyaRamaswamy(SchoolofDentistry),Dr.RomeshNalliah(SchoolofDentistry),Dr.MarkFitzgerald(SchoolofDentistry),CeliaAlcumbrack-McDaniel,(SchoolofDentistry)

10:30a.m. COFFEEBREAK

11:00a.m. PANEL2:NEIGHBORHOODHEALTH,COMMUTERSINDC,ANDCONSUMER

EXPERIENCESDiscussant:MargaretHicken(SurveyResearchCenter/PopulationStudiesCenter,ISR)

• “UsingTwittertoStudyNeighborhoodCharacteristicsRelatedtoHealthBehaviors”V.G.VinodVydiswaran(DepartmentofLearningHealthSciences),TiffanyVeinot(SchoolofInformation),DanielRomero(SchoolofInformation),DeahanYu(SchoolofInformation),BradleyE.Iott(SchoolofInformation),VeronicaJ.Berrocal(SchoolofPublicHealth),PhilippaJ.Clarke(SurveyResearchCenter,ISR),RobertGoodspeed(TaubmanCollegeofArchitectureandUrbanPlanning)

• “CouplingTwitterwithGISoperations:TheBenefitsandCostsofExtendingSentimentAnalysistoTravelModeandGeography”GregRybarczyk(DepartmentofGeography,Planning,andEnvironment),SyagnikBanerjee(SchoolofManagement,Flint)

• “DevelopingAmbientRetailInsightsfromOngoingServiceEncounterswithGeo-SocialData”SyagnikBanerjee(SchoolofManagement,Flint),KarthikSridhar(TheCityUniversityofNewYork),AmitPoddar(SalisburyUniversity),NandaKumar(TheCityUniversityofNewYork)

12:30p.m. LUNCHANDPOSTERSESSION Discussants:SyagnikBanerjee(SchoolofManagement,Flint),VeronicaJ.Berrocal(Schoolof

PublicHealth),RobertGoodspeed(TaubmanCollegeofArchitectureandUrbanPlanning),DanielRomero(SchoolofInformation),MichaelTraugott(CenterforPoliticalStudies,ISR),V.G.VinodVydiswaran(DepartmentofLearningHealthSciences)

(CONTINUEDONNEXTPAGE)

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PRELIMINARYPROGRAM,CONTINUED

2:00p.m. PANEL3:ELECTIONEXPERIENCES,SOCIALMOVEMENTS,ANDTHEPREDICTIONOFHUMANACTIVITIESDiscussant:MikeCafarella(ComputerScienceandEngineering)

• “WhatYouSayYouSeeisWhoYouAre:ObservingElectionIncidentsintheUnitedStatesviaTwitter”WalterMebaneJr.(DepartmentofPoliticalScience,andStatistics),PatrickWu(DepartmentofPoliticalScience),LoganWoods(DepartmentofPoliticalScience),AlejandroPineda(DepartmentofPoliticalScience),BlakeMiller(DartmouthCollege),JosephKlaver(DepartmentofPoliticalScience),PrestonDue(ComputerScienceandEngineering),AdamRauh(MassachusettsInstituteofTechnology)

• “TheRoleofSocialMovementOrganizationsintheAgeof#MeToo”PatrickPark(RossSchoolofBusiness),JoseUribe(RossSchoolofBusiness)

• “PredictingHumanActivitiesfromUser-GeneratedContent”SteveWilson(CollegeofEngineering),RadaMihalcea(ElectricalEngineeringandComputerScience)

3:30p.m. KEYNOTEADDRESS,AWARDCEREMONY,COFFEEBREAKKeynoteSpeaker:MikeCafarella(ComputerScienceandEngineering)

4:00p.m. CONCURRENTSESSIONS• Participatory&NetworkingSession:“DevelopingTwitterdataresearchatU-M”

This60-minutesessionisforU-MscholarscurrentlyengagedinTwitterdataresearchtomeet,network,andexploreopportunitiesforresearch,collaboration,grants,andfutureeventson#UMTwitterResearch.

• Workshop:“IntroductiontotheTwitterAPI”,AlexCao(CSCAR),1450ISRThompsonThis60-minuteworkshopisdesignedfor#UMTweetCon2019attendeesinterestedingettingstartedwiththeirownTwitterdataresearch.ItintroducesworkflowsforusingtheTwitterAPIforprogrammersandnon-programmersalike.Fornon-programmers,we'llbeusingthePostmansoftware.Forprogrammers,we'lldemonstratethesameprocessinPython.WewillalsodiscusssomeofthelimitationsoftheTwitterAPIandcoverhowtoparsetheJSONdataretrievedfromit.

5:00p.m. RECEPTION

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POSTERSESSIONPARTICIPANTS

“BEYONDTHEEYE-CATCHERS:ALARGE-SCALESTUDYOFSOCIALMOVEMENTORGANIZATIONS'INVOLVEMENTINONLINEPROTESTS”

LiaBozarth(SchoolofInformation),CerenBudak(SchoolofInformation)

“ISTHISAJOKE?HUMORANALYSISONTWEETS”

SantiagoCastro(ElectricalEngineeringandComputerScience),LuisChiruzzo(UniversidaddelaRepública,Uruguay),AialaRosá(UniversidaddelaRepública,Uruguay),MathiasEtcheverry(UniversidaddelaRepública,Uruguay),DiegoGarat(UniversidaddelaRepública,Uruguay),JuanJoséPrada(UniversidaddelaRepública,Uruguay),RadaMihalcea(ElectricalEngineeringandComputerScience

“CHARACTERIZINGANDPREDICTINGINTERPERSONALRELATIONSHIPSINONLINESOCIALNETWORKS”

MinjeChoi(SchoolofInformation),DavidJurgens(SchoolofInformation),DanielRomero(SchoolofInformation),CerenBudak(SchoolofInformation),XinyiZheng(ComputerScienceandEngineering)

“SPANK,SMACK,&WHOOP”:AQUALITATIVEANALYSISOFSTAY-AT-HOMEMOTHERS’ANDSTAY-AT-HOMEFATHERS’SPANKINGTWEETS”

JoyceLee(SchoolofSocialWork),AndyC.Grogan-Kaylor(SchoolofSocialWork),ShawnaJ.Lee(SchoolofSocialWork),TawfiqAmmari(SchoolofInformation),AlexLu(SchoolofSocialWork),PamelaDavis-Kean(DepartmentofPsychology)

“GEOCODINGSTAY-AT-HOMEPARENTS’SELF-DEFINEDLOCATIONS”

AlexLu(SchoolofSocialWork),JoyceLee(SchoolofSocialWork)

“PSYCHOLOGICALRESPONSEOFCOMMUNITIESAFFECTEDBYNATURALDISASTERSINSOCIALMEDIA”

DanajaMaldeniya(SchoolofInformation)

“EVALUATINGSURVEYCONSENTTOSOCIALMEDIALINKAGE”

ColleenMcClain(PrograminSurveyMethodology),ZeinaMneimneh(SurveyResearchCenter),LisaSingh(GeorgetownUniversity),TrivelloreRaghunathan(SurveyResearchCenter)

“THESOCIALDYNAMICSOFACADEMICSONTWITTER”

GarrettPace(SchoolofSocialWorkandSociology)

“MACRO-ECONOMICCORRELATESOFTHEDISTRIBUTIONSOFNETWORKDIVERSITY”

PatrickPark(SchoolofBusiness),MinsuPark(CornellUniversity),MichaelMacy(CornellUniversity)

“THEGEOMETRYOFPARTISANSHIP:ESTIMATINGPARTISANASSOCIATIONSOFTWITTERUSERSUSINGWORDEMBEDDINGS”

PatrickWu(DepartmentofPoliticalScience),WalterMebaneJr.(DepartmentofPoliticalScience,andStatistics)

“NATURALLANGUAGEPROCESSINGAPPROACHESTODETECTMENTIONSOFDRUGSANDADVERSEDRUGEVENTSINTWEETS”

DeahanYu(SchoolofInformation),VGVinodVydiswaran(DepartmentofLearningHealthSciences)

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ABSTRACTSANDPANELISTBIOS

PANEL1

“FRAMINGATHLETEACTIVISM:DIFFERENCEBETWEENTRADITIONALMEDIAANDSOCIALMEDIA”WencheWang(SchoolofKinesiology),Stacy-LynnSant(SchoolofKinesiology)Abstract:DuringaNationalFootballLeague(NFL)pre-seasongameinAugust2016,SanFrancisco49erquarterbackColinKaepernickwentunnoticedashesatonthesidelineinsilentprotest.Inthemonthsthatfollowed,Kapernick’sprotestgainedpopularityamonghispeers,withseveralplayerskneelingforthenationalanthemtodrawattentiontotheissueofthedisproportionateshootingofAfrican-Americanmalesbypolice.TheseprotestsbecameamajornewsstoryintheUnitedStates(US)andsparkedheateddebatesaboutathletes’righttofreespeech,patriotism,andracialinjustice.Thecurrentstudyemploysthetheoreticalframeworkofmediaframingtoexplorehowtheseprotestswereconstructed,structured,anddevelopedinmainstreamnewsandsport-specificmedia,andinturn,howthenewsmedia’sframingoftheissue(may)influencepublicperception.Specifically,thisstudyseeksto:(i)identifythegenericandissue-specificframeswhichdominatemediadiscoursesurroundingthisissue;(ii)highlightthedifferencesbetweenframingintraditionalmedia(printandbroadcast)andsocialmediaand(iii)testhowtheframingofissuesintraditionalmediainfluencessocialmediausers’perceptionoftheanthemprotests.Dr.WencheWangiscurrentlyanAssistantProfessorofSportManagementintheUniversityofMichiganSchoolofKinesiology.PriortohertimeatU-M,Dr.WangwasapostdoctoralassociateintheInformaticsInstitute&DepartmentofEconomicsattheUniversityofFlorida,whereshealsoearnedherPh.D.inEconomics.Shehaspublishedinmultiplejournalsandbooks,includingInformation&Management,ReviewofIndustrialOrganization,OptimizationMethodsandApplications,andTheCambridgeHandbookofAntitrust,IntellectualProperty,andHighTech.Dr.WanghaspresentedatprominentconferencessuchastheEuropeanSportEconomicAssociationConferenceandtheINFORMSAnalyticsConference.Sheiscurrentlyresearchingsupplierencroachmentandtradecredit,informationdisclosureontheinternet,andtheNCAAcartel.

“PROFESSIONALNETWORKSAMONGPOLITICALJOURNALISTS”MichaelTraugott(CenterforPoliticalStudies,ISR)Abstract:ThispresentationreportsonanalysesofTwitternetworksamongpoliticaljournalistsandcommentatorswhocoveredthe2016presidentialcampaign.ItcomparestheiractivityandthecontentoftheircommunicationaroundthefirstRepublicanprimarydebateinAugust2015andthegeneralelectioncampaigninFall2016,aswellasthenatureoftheirnetworkedrelationships.

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MichaelTraugottisapoliticalscientistwhostudiescampaignsandelections,votingbehavior,politicalcommunication,theuseofpollstoconstructnews,andsurveymethodology.Hehaspublishedextensivelyinalloftheseareas.HeisactiveintheAmericanAssociationforPublicOpinionResearch(AAPOR)andtheWorldAssociationforPublicOpinionResearch(WAPOR),servingaspresidentofbothorganizations.In2010,hereceivedtheAAPORAwardforExceptionallyDistinguishedService.HealsoservedaspresidentoftheMidwestAssociationforPublicOpinionResearch(MAPOR).HeisafrequentresourceforjournalistsinterestedindiscussingAmericanpoliticalcampaignsandgovernmentoperations.

“USINGTWITTERTOTEACHDENTALTREATMENTPLANNING”VidyaRamaswamy(SchoolofDentistry),Dr.RomeshNalliah(SchoolofDentistry),Dr.MarkFitzgerald(SchoolofDentistry),CeliaAlcumbrack-McDaniel,(SchoolofDentistry)Abstract:Twitterwasusedin3classsessionstogeneratequestionsandconversationinaDDSyear1courseabouttreatmentplanning.Theinstructorposedquestionsduringclassaswellasafterclass.Duringclass,Twitterofferedstudentsachancetoseetheintheimmediate,livefeedhowtheirpeersweresynthesizingthesameinformationdifferently.Afterclass,Twitterofferedstudentstheopportunitytoreflectandrespondtocontentpresentedinclass.ResponsesonTwitterwereusedtostreamlinediscussioninclass.Studentparticipationseemedtosuggeststudentengagement.Therewere117postswith65usersinaclassof109students.VidyaRamaswamyisanAssociateDirectorofCurriculumandProgramEvaluationattheUniversityofMichiganSchoolofDentistry.

PANEL2

“USINGTWITTERTOSTUDYNEIGHBORHOODCHARACTERISTICSRELATEDTOHEALTHBEHAVIORS”V.G.VinodVydiswaran(DepartmentofLearningHealthSciences),TiffanyVeinot(SchoolofInformation),DanielRomero(SchoolofInformation),DeahanYu(SchoolofInformation),BradleyE.Iott(SchoolofInformation),VeronicaJ.Berrocal(SchoolofPublicHealth),PhilippaJ.Clarke(SurveyResearchCenter,ISR),RobertGoodspeed(TaubmanCollegeofArchitectureandUrbanPlanning)Abstract:Initiativestoreduceneighborhood-basedhealthdisparitiesrequireaccesstomeaningful,timely,andlocalinformationregardinghealthbehavioranditsdeterminants.Inaseriesofresearchstudies,weexaminethevalidityofTwitterasasourceofinformationforneighborhood-levelanalysisofattitudesandpatternsrelatedtodietarybehaviorsandphysicalactivity.Inrecentworks,weappliedmachinelearningtechniquestoidentifydiet-relatedcontentsharedonTwitterinmetropolitanDetroit.Wethenanalyzedthe“healthiness”quotientoffoodsmentionedinthesetweetsaswellastheirsentimentandattitudestowardsfood.Weassociateddiet-relatedtweetmeasureswithcensustract

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characteristicsandhealthoutcomesandanalyzedthekeywordsdrivingthedifferencesinfoodhealthinessbetweenthemostandleastaffluenttracts.Ouranalysesrevealedsignificantcorrelationsbetweenhealthinessandsentimentinfood-relatedtweets,andtract-levelmeasuresofaffluence,disadvantage,race,age,fastfoodrestaurantdensity,andmortalityfromconditionsassociatedwithobesity.Discussionsoffoodshighinsaturatedfat(e.g.,pizza,bacon,fries)weremorecommonfrequentinless-affluenttracts.Food-relateddiscussionreferredtoactivities(eating,drinking,cooking),locationswherefoodwasconsumed,andbothpositive(affection,cravings,enjoyment)andnegativeattitudes(dislike,personalstruggles,complaints).OurfindingsdemonstratethefeasibilityofusingTwittertounderstandneighborhoodcharacteristicsregardingattitudestowardsfoodandhighlightthepotentialtostudyneighborhoodhealthdisparitiesandinformlocalhealthdisparityreductionefforts.Inourongoingwork,weextendthisworktostudytweetsrelatedtophysicalactivity–capturingbothsedentaryandactivelifestyles-andalcoholconsumption.V.G.VinodVydiswaranisanAssistantProfessorintheDepartmentofLearningHealthSciencesandtheSchoolofInformationattheUniversityofMichigan.Hisresearchinterestsareprimarilyinclinicalandconsumernaturallanguageprocessing,informationtrustworthiness,large-scaletextminingandanalysis,andmedicalinformationscience.Hiscurrentresearchfocusesonminingandanalyzinghealthinformationfrommultiplesources,includingscientificliterature,communityhealthforums,andsocialandinformationnetworks.,withaspecialinterestinanalyzingonlinemedicaltextualinformationtoinfercredibilityofsourcesandtheclaimstheymake.Dr.VydiswaranisaReviewEditoroftheJournaloftheAssociationforInformationScienceandTechnology(JASIST)andaGuestEditoronthejournalsupplementonBigDataAnalyticsforHealthoftheBiomedicalEngineeringandComputationalBiology(BECB).HereceivedhisPh.D.inComputerSciencefromtheUniversityofIllinoisatUrbana-ChampaignandMasterofTechnologyfromtheIndianInstituteofTechnologyBombay,India.

“COUPLINGTWITTERWITHGISOPERATIONS:THEBENEFITSANDCOSTSOFEXTENDINGSENTIMENTANALYSISTOTRAVELMODEANDGEOGRAPHY”GregRybarczyk(DepartmentofGeography,Planning,andEnvironment),SyagnikBanerjee(SchoolofManagement,Flint)Abstract:Growinguseofgeosocialmediadatahasopenedupnewopportunitiesinthefieldsofurbanplanning,publichealth,andtransportstudies:thegreatamountofdatacomingfromTwitterisanexample.Itprovideseasilyavailable,oftengeo-referenced,marginallycostly,datasetsofferingnewinsightsonindividualmobilityandcollectivelife.Thiscurrentstudyfocusedonanalyzingmode-choicesentiment(i.e,valence)patternsandlinkagesacrossspaceandtimeinWashington,D.C.,USA-acityrenownedforitsmulti-modaloptions.Themethodsweusedconsistedof:exploratoryspatialtemporaldataanalysis(ESTDA),globalregression,andspatialregressionmodels.Afteradjustingforspatialerrorassociatedwithsocioeconomic,environmental,weather,andtemporalfactors,spatialautoregressionmodelsprovedsuperiorandshowedthatwaterandpedestriantravelwereuniversallyassociatedwithpositivevalences.Bicyclingalsofavorablyinfluencedvalence,albeitonlyinD.C.Anoteworthyfindingwasthenegativeinfluencetemperatureandhumidityhadonvalence.Theoutcomesfromthisresearchshouldbeconsideredwhenadditionalevidenceisneededtoelevatecommutersentimentvaluesinpracticeandpolicy,especiallyinregardstoactivetransportation.

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GregRybarczykisanAssociateProfessorofGeographyandGeographicInformationSystems(GIS)attheUniversityofMichigan-Flint.HeisalsoafacultyaffiliateattheUniversityofMichiganDataScienceInstitute(MIDAS).Hisresearchiscenteredonexaminingaccessibility,urbandesign,andpublichealthoutcomesusinginnovativeGISmethodsandempiricalmodels.Hisworkwithintheseareashasbeenexpansive,spanningapproachesinvolvingactivetransportation;naturalisticmodelstoassesscyclingstress;Agent-BasedModeling(ABM);travel-modesentimentanalysisusingBigData;travel-demandmodeling;neighborhoodcrimeandpublichealthoutcomes;urbaninfrastructureresiliency;mentalhealth-transportation-urbanformrelations;intermodaltravelpotentialsinlegacycities;andgeovisualizingfoodinsecuritypatterns.Hehasalsoworkedinprivateindustry,governmentunits,andnon-profitorganizationsasaGISanalyst,planner,andconsultant.

“DEVELOPINGAMBIENTRETAILINSIGHTSFROMONGOINGSERVICEENCOUNTERSWITHGEO-SOCIALDATA”SyagnikBanerjee(SchoolofManagement,Flint),KarthikSridhar(TheCityUniversityofNewYork),AmitPoddar(SalisburyUniversity),NandaKumar(TheCityUniversityofNewYork)Abstract:Increaseduseoflocationbasedsocialnetworkshasledtouserssharingtheirlocationandsentimentsaboutongoingconsumerexperiencefromretailbusinesspremises.Wedevelopanempiricalframeworkthatretailerscaneffectivelyusetounderstandtheantecedentsimpactingsentimentsaboutongoingconsumptionexperiencesandthoseimpactingthedecisiontosharesuchsentiments,namely,(a)theconsumer’ssocialreputationandengagement,(b)consumer’sprivacysettingsand(c)locationspecificcontextualandhyper-contextualfactors.Critically,weexamineprivacyconcernsalongtwodistinctdimensions-LocationPrivacy(LP)andContentVisibilityPrivacy(CVP)–thatoughttobeexaminedconcurrentlybutdistinctly.Whiletheaforementionedfactorsimpactconsumer’sdecisiontoshareandthesentimentbeingshared,aconsumer’sprivacysettingscancreateendogeneityissuesbyinfluencingtheconsumer’sreputationandengagementmetrics.Wealleviatesuchconcernsbyformulatingadoublehurdleframeworkondecisiontoshareandevaluationbeingsharedwhilesimultaneouslyaccountingforendogeneityamongpredictorvariables.Weapplythemodeltodatasharedfromrestaurantscomprisingoflocationbasedsocialcheck-insandsentimentexpressedthroughmicro-bloggingcontentwhichwesupplementusingmultipledatasourceswithconsumersocialreputationandengagementmetrics,andrestaurantspecificratingsdataandcrowdedness.Wefindempiricalevidenceofhyper-contextualfactorsandprivacyfactorsdrivingconsumptionsharingandsentimentsmorethanothers.TheresultspointtodifferenteffectsofLPandCVPonconsumersharingpropensityandassociatedevaluations.AlleviatingCVPconcernsforconsumerswhoareindifferenttosharingcanleadtomaximumimprovementintheirconsumptionevaluation.AlleviatingLPconcernscanmaximizeconsumptionevaluationforconsumersmorecertainabouttheirsharingintent.Dr.SyagnikBanerjeeisanAssociateProfessorofMarketingatUM-FlintandanAffiliateProfessoratMichiganInstituteofDataSciences(MIDAS).HereceivedhisPh.D.inBusiness(Marketing)fromtheUniversityofRhode

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Island,MBA(Marketing)fromInternationalManagementInstitute,NewDelhi,andhisBachelorinScience(Economics)fromPresidencyCollege,Kolkata.Hejoinedacademiawithfouryearsofindustryexperienceinconsumerpackagedgoods,logistics,tracking,andtelecommunications.Hehasbeenapioneerintheresearchoftheimpactofmobiledevicesindigitalmarketing,consumerinsightsandbehavior.Hisresearchcoversareasincludingmobileadvertising,Geoadvertising,AdvertisingTransparencyanddevelopingTrustworthydatascienceapplicationsformarketingandpublicpolicy.HehaspublishedinJournalofBusinessResearch,JournalofResearchinInteractiveMarketing,JournalofMarketingAnalyticsandInformationSocietyamongmanyothers.HeauthoredM-PoweringMarketinginaMobileWorld,abookcoveringthedynamicsoftechnology,business,andsocietyforacademicsaswellaspractitioners.HehasdevelopedthefirstacademiccourseonmobileinteractivemarketingandoffereditforNorthwesternUniversityin2013,beenspeakersattheMobileUniversitySummit,andbeenacollaboratorwithHeartlandMobileCouncil(Chicago)fordevelopingacertificationprogramformobilemarketers.HehashelpedtheUMFlintSchoolofManagementwintheCommunityOneAwardsfromMMBDCthroughhisteachingandtheOakGovchallengewithAT&Tthroughhisresearchefforts.HeisamemberoftheCollaborativeNeuro-marketingGroupatNorthwesternUniversity,MIDASatUMAnnArborandalsoservesasreviewerandEditorialBoardMemberforseveralinternationallyreputedjournalsincludingEuropeanJournalofMarketingandJournalofResearchinInteractiveMarketing.

PANEL3

“WHATYOUSAYYOUSEEISWHOYOUARE:OBSERVINGELECTIONINCIDENTSINTHEUNITEDSTATESVIATWITTER”WalterMebaneJr.(DepartmentofPoliticalScience,andStatistics),PatrickWu(DepartmentofPoliticalScience),LoganWoods(DepartmentofPoliticalScience),AlejandroPineda(DepartmentofPoliticalScience),BlakeMiller(DartmouthCollege),JosephKlaver(DepartmentofPoliticalScience),PrestonDue(ComputerScienceandEngineering),AdamRauh(MassachusettsInstituteofTechnology)Abstract:UsingTwitterdatafromOctober1throughNovember8,2016,weshowthatpartisanbiasesappearinindividuals'reportssupposedlyoftheirownexperiences.BasedonsixmillionoriginalTweetsgatheredusingTwitter'sSTREAMAPI,weusesupervisedmachinelearningtoidentify315,180observationsof``incidents,''whereanincidentisareportedlypersonalinvolvementwiththeelectoralprocesssuchasareportedexperiencewithalinetovote(long,shortornone),orsuccessorfailureregisteringorvoting.TheseincidentTweetsweresentby215,230distinctTwitterusers.Weusewordembeddingscomputedusingthetextoftheusers'self-descriptionstodiscernhowmucheachuserassociateswithTrump-Donald-realdonaldtrump-MAGA-RepublicanorwithClinton-Hillary-hillaryclinton-strongertogether-Democrat.CosinesimilaritiesderivedfromthewordembeddingsdifferentiallypredicthowfrequentlyusersretweetorfavoriteTweetssentfromaccountsthatwereretweetedbyatleast90%ofeachparty'srespectivemembersofCongress(plusafewotheraccounts).Cosinesimilarityhomophilywithin279millionclosedtriadsofincident-TweetingusersidentifiedusingTwitter``friends''and``followers''isofthesameorderofmagnitudeasthehomophilybetweenincident-tweetingusersandsendersofTweetstowhichanincidentTweetisareply.Incident-tweetinguserswithdifferentpartisanassociationstendtoreportdifferenttypesofincidents.WeusethelocationsreportedforTweetsorthatusersreportforthemselvestoestablishthattheobservablereportingdifferencesprobablystemfrombiasesandnotfromdifferencesin

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objectiverealexperiences.Reportsofpersonalexperiencesareinflectedbypartisanbiasesandcommunicatedthroughpartisancommunicationsilos.We'vealsocollecteddatafrom2018(65millionTweets)andareworkingtoimplementaTwitterElectionObservatorywithreal-timeclassificationandreportingfor2020.WalterMebaneJr.isaUniversityofMichiganprofessorofpoliticalscienceandstatisticsandanexpertondetectingelectoralfraud.Hehasauthorednumerousarticlesondetectingfraudulentresultsinelections,includingaseriesofnotesontheresultsoftheIranianpresidentialelection,2009.

“THEROLEOFSOCIALMOVEMENTORGANIZATIONSINTHEAGEOF#METOO”PatrickPark(RossSchoolofBusiness),JoseUribe(RossSchoolofBusiness)Abstract:Withtheadventofsocialmedia,wearewitnessingfundamentalshiftsinthedynamicsandorganizationofsocialmovementswheredecentralizedmassesareheavilyinvolvedinallaspectsofamovement,fromagendasetting,coordination,toresourcemobilization(Barberáetal.2015;TufekciandWilson2012).Inthemidstofthistectonicshift,theroleofsocialmovementorganizations(SMOs)hasbeenreceivingincreasingattention.AnunsettleddebateinthisliteratureistheextenttowhichSMOsare“adaptingtothenewclimatetheyfaceandhowtheyarereengineeringthevaluetheyreturntoparticipants”(Earl2014).The#MeToomovementoffersausefulcaseforinvestigatinghowSMOsadapttothenewdynamicsprecipitatedbyonlinesocialmovements.Usingdataontax-exemptorganizationsintheU.S.inFY2015,whichincluded149,275nonprofitorganizationsthatlistedawebsiteintheirIRS990filings,wedevelopacombinationofautomatedandmanualsearchstrategiestoidentifytheTwitterhandlesof45Korganizationstoinvestigatetheorganizationalcharacteristicsthatpredictthehazardofparticipatinginthe#MeToomovementthroughthegenerationoforiginaltweetsandretweets.Throughacombinationofnetworkanalysisof#MeTooretweetsandtopicmodelingoftweettext,wetesttheextenttowhichtheparticipatingorganizationseitherleadwithnewdiscourseoramplifypre-existingdiscoursethroughtheir#MeToohashtagtweets.PatrickParkisaResearchFellowatRossSchoolofBusiness,UniversityofMichigan.Hisresearchinterestsareprimarilyinsocialnetworks,computationalsocialscience,andagent-basedmodels.HereceivedhisPh.D.inSociologyfromCornellUniversity.

TITLE:“PREDICTINGHUMANACTIVITIESFROMUSER-GENERATEDCONTENT”SteveWilson(CollegeofEngineering),RadaMihalcea(ElectricalEngineeringandComputerScience)Abstract:Theactivitieswedoarelinkedtoourinterests,personality,politicalpreferences,anddecisionswemakeaboutthefuture.Inthispaper,weexplorethetaskofpredictinghumanactivitiesfromuser-generatedcontent.Wecollectadatasetcontaininginstancesofsocialmediauserswritingaboutarangeofeverydayactivities.Wethenuseastate-of-the-artsentenceembeddingframeworktailoredtorecognizethesemanticsofhumanactivitiesandperformanautomaticclusteringoftheseactivities.Weintroduceadeepneuralmodeltomakepredictionsaboutwhichclusterscontainactivitiesthatwereperformedbyagivenuserbasedonthetextoftheirprevious

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posts.Wealsoexplorethedegreetowhichincorporatinginferredusertraitsintoourmodelhelpswiththispredictiontask.SteveWilsonisaPostdoctoralResearchFellowattheUniversityofMichigan,CollegeofEngineering.HisresearchfocusesonthedevelopmentandapplicationofNaturalLanguageProcessingtechniquestowardComputationalSocialScienceProblems.ThisofteninvolvestheuseofMachineLearningandCrowdComputingmethods.Specifically,heislookingatwaystoautomaticallyinferpersonalvaluesfromtextandexplorehowvaluesrelatetoaperson'scultureandbehaviors.