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Soc.Sci.2022,11,395.https://doi.org/10.3390/socsci11090395www.mdpi.com/journal/socsci
Article
UsingSocialMediatoMonitorConflict‐RelatedMigration:
AReviewofImplicationsforA.I.Forecasting
HamidAkinUnver
DepartmentofInternationalRelations,ÖzyeğinUniversity,Istanbul34337,Turkey
Abstract:Followingthelarge‐scale2015–2016migrationcrisisthatshookEurope,deployingbig
dataandsocialmediaharvestingmethodsbecamegraduallypopularinmassforcedmigration
monitoring.Thesemethodshavefocusedonproducing‘real‐time’inferencesandpredictionson
individualandsocialbehavioral,preferential,andcognitivepatternsofhumanmobility.Although
thevolumeofsuchdatahasimprovedrapidlyduetosocialmediaandremotesensingtechnologies,
theyhavealsoproducedbiased,flawed,orotherwiseinvasiveresultsthatmademigrants’lives
moredifficultintransit.Thisreviewarticleexplorestherecentdebateontheuseofsocialmedia
datatotrainmachinelearningclassifiersandmodifythresholdstohelpalgorithmicsystemsmoni‐
torandpredictviolenceandforcedmigration.Ultimately,itidentifiesanddissectsfiveprevalent
explanationsintheliteratureonlimitationsfortheuseofsuchdataforA.I.forecasting,namely
‘policy‐engineeringmismatch’,‘accessibility/comprehensibility’,‘legal/legislativelegitimacy’,‘poor
datacleaning’,and‘difficultyoftroubleshooting’.Fromthisreview,thearticlesuggestsanonymiza‐
tion,distributedresponsibility,and‘righttoreasonableinferences’debatesaspotentialsolutions
andnextresearchstepstoremedytheseproblems.
Keywords:conflict;forcedmigration;artificialintelligence;eventdata;bigdataethics
1.Introduction:OpportunitiesandPitfallsofExtractingInformationfromViolent
andMigration‐ProneRegions
Masshumandisplacementhasoftenbeenaby‐productoforganizedviolence.Some
ofthelargestforcedmigrationeventsinworldhistoryhavebeentriggeredbyanthropo‐
genicdisasters,andinternationalorintrastateconflictsstillremainamongthemostim‐
mediatecausesofrefugeecrises(Lozano‐Graciaetal.2010;Schon2019;Selbyand
Hoffmann2012).Sincecivilwarsareespeciallyfoughtnearpopulatedareastoestablish
controloverpopulationcenters,theyareparticularlypronetogeneratingmassdisplace‐
ment(Steele2009;Lichtenheld2020).Indeed,frequentexchangesofterritoryasaresult
ofconflict—suchasinUkraine,Syria,SouthSudan,Myanmar,DRCongo,Somalia,Cen‐
tralAfricanRepublic,Afghanistan,andIraq—generateanoverwhelmingmajorityofthe
worldrefugeepopulation.Bothinhumanhistoryandtoday,armedconflictsremainone
ofthemostsignificantpredictorsofforceddisplacementand,often,thesizeofforced
migration.
Violenteventsleadtothemassuprootingofnoncombatantsfortworeasons.First,
theimmediatelife‐threateningpotentialofconflictsleadstothedepartureofentirevil‐
lages,towns,andsometimescities,fleeingintosaferregions(BasuandPearlman2017).
Often,large‐scalepopulationdisplacementgetstriggeredbecauseciviliansexpectharsh
treatmentortargetingbytheconquerorsandleavetoescapefromsuchfate(Conteand
Migali2019).Second,theaftershocksofaconflictcreatesignificantinfrastructureandsus‐
tenanceproblemsthatleadtothemassdepartureofcivilianstogainaccesstoessential
resourcesandservices(Humphrey2013;Rajabalietal.2009).Quitefrequently,secondary
effectssuchasthedestructionofhousing,sanitationandelectricalinfrastructure,absence
Citation:Unver,HamidAkin.2022.
UsingSocialMediatoMonitor
Conflict‐RelatedMigration:A
ReviewofImplicationsforA.I.
Forecasting.SocialSciences11:395.
https://doi.org/10.3390/socsci11090395
AcademicEditor:CarlosArcila
Calderón,TubaBircan
andNigelParton
Received:19May2022
Accepted:22August2022
Published:1September2022
Publisher’sNote:MDPIstaysneu‐
tralwithregardtojurisdictional
claimsinpublishedmapsandinstitu‐
tionalaffiliations.
Copyright:©2022bytheauthors.
Submittedforpossibleopenaccess
publicationunderthetermsandcon‐
ditionsoftheCreativeCommonsAt‐
tribution(CCBY)license(https://cre‐
ativecommons.org/licenses/by/4.0/).
Soc.Sci.2022,11,3952of15
oflawandorder,anddisruptionsinfoodsuppliesgenerateescalatinghardshipsinapop‐
ulationcenter,creatingsustained,low‐intensitymigration(SowersandWeinthal2021;
Crush2013).Thisisnottosuggestthatarmedviolenceautomaticallygeneratesforced
migration(sometimescivilianseitherchoosetostayintheclashzoneforvariousreasons,
orhavemobilityproblemsthatpreventsuchprospects),butonecouldsafelyassumethat
organizedviolencetendstocreatepoverty,grievance,andthreatstimulithatusuallycon‐
tributestolocals’assessmentsofstayingorleaving(Tellez2022;Epstein2010).
Violentorganizedconflictisnotsolelyanindependentvariableinthestudyofdis‐
placement.Often,climateandseasonaladversities(likebadharvest),inadditiontonatu‐
raldisasters,maytriggerforcedmigration,andareexacerbatedbypre‐existingornewly
emergingformsofconflict(AshandObradovich2020).Insuchcases,althoughviolence
doesnotnecessarilyinitiatemigrationevents,itnonethelessaffectsthetempo,duration,
anddirectionofmigrationflows(Burkeetal.2015).Suchdisastersandclimate‐related
effectsalsocontributetoincreasedviolenceduetodwindlingaccesstoresources,gener‐
atingadditionalcompetitionoverwater,food,andmedicalsupplies.Dormantgrievances
re‐emergeduetoawidearrayofdisplacementtriggers,andethnic/religiousgroupsmay
targeteachotherduringmigration,ortheymaybetargetedbyoutsidehostilearmedac‐
torswhileonthemove(Salehyan2007).
Sincearmedconflictiseither(orboth)anindependentandinterveningvariablein
migrationresearch,reliefagenciesandgovernmentshavebeenexploringwaystoquan‐
tifyandlogeventdatatoproducemoreinformedanalysesaboutbothformsofinterlinked
crises.Fromtheneedtoestablishmoredetailedandrobustmechanismstomonitorand
forecastforcedmigrationandtopreventviolence,arosemacro‐leveleventdatasets
(Lubecketal.2003;Nam2006;Eck2012).Thesedatasetsinitiallyloggedviolenceinbinary
forms,andinadyadicfashion,butintandem,anumberofhighlygranularandmulti‐
directionaldatasetshavebegunappearinginscientificspace(someexamplesare
Chojnackietal.2012;Weidmann2013;DemarestandLanger2018).Inthelastdecade,
thesedatasetshavebeenusednotjustforexplainingconflictandmigration,butalsoto
forecastthem(BlairandSambanis2020;Carammiaetal.2022).
Theneedtoforecastorganizedviolenceanduseitinturntopredictforcedmigration
haslongbeenapriorityforstateactorsandinternationalorganizations.Priortothepro‐
liferationofthescientificandprivateeventdatarevolution,informationaboutconflicts
andmigrationwasthuslargelysuppliedbyformalmilitaryobservers.Themainproblem
withthesemilitaryreportswasthattheywerepronetocensorshipandreportingbias,as
statestendedtomisrepresentfieldeventsinlinewiththeirnationalinterests(Banerjee
andMacKay2020).Fromthemid‐20thcenturyonwards,theonsetofhigh‐circulation
newspapers,radio,andtelevisionchangedthenatureofwarreportingandextractingin‐
formationaboutconflictzonesbytheriseofwarreportersandaidworkersasanim‐
portantadditionalsourceofconflicteventdata.Withoutaformalmilitarychainofcom‐
mandconstraints,warreportersandaidagencieshavebegungeneratingamorediverse
arrayoffieldinformationforbothmilitaryandnon‐militarypurposes,whichwasawel‐
comeaddition,sincetheysufferedfromfewernationalinterestcensorshipconstraints.
However,ultimately,theybegansufferingfromotherformsofobservationbias.Report‐
ershadeditorialconstraints(Knightley2002),whereasaidworkerswereincreasinglycon‐
strainedbytheirsuperiorswhenfieldeventsdidnotconformtoeithereditorialor
agency/donorinterests(Bunceetal.2019).Gradually,moreacademic/scientificprojects
tookpacebythe1970s,suchasDavidSinger’s‘CorrelatesofWarProject’,whichbrought
local,national,andinternationalnewsreportstogethertogeneratearicheraccountoffield
eventsthroughtraditionalmediareports(Eck2012).
However,evenwhenusedaspartofscientificdatasets,traditionalmediadatahasits
biaslimitations:Primarily,bothlocalandinternationalnewsoutletshaveeditorialinter‐
estsandpoliticalprerogativesthatoftenmurktheirreportingefforts(Ravi2005).Inmost
cases,mediagroupschoosetoornottoreporteventsbasedontheirpre‐existingpolitical
tiesoreditorialbiases.Newspaperoutletshavevaryingdegreesofautonomyand
Soc.Sci.2022,11,3953of15
editorialexposuretogovernmentalpressures,renderingtheindependenceofmedia‐
basedeventreportshighlyskewed(LeeandMaslog2005).Second,mediadatatendsto
focuson‘bigevents’thateitheraffectalargenumberofpeople,orawideswatheofterri‐
toryinordertomaximizeitsreadershippotential(BaumandZhukov2015).Thisleaves
out‘smallerevents’thatarestillfieldeventsbutareomittedduetoreadershiporsales
considerations.Third,mediagroupstendtoover‐focusoneventsthatconcerntheirhost
countryandleaveoutpotentiallyimportanteventsthatdonotconcernthemdirectly
(Weidmann2016).These‘irrelevantevents’canoftenbediscarded—again,forhome
countryreadershipconsiderations—butmayotherwisecontainimportantdetailstoinfer
theinnerworkingsofconflicts.Finally,asstipulatedintheHerman–ChomskyPropa‐
gandaModel(ChomskyandHerman2002;MullenandKlaehn2010),therearefivefilters
throughwhicheventshavetogothroughinordertoappearinthenews.Theseare(a)
mediaownershipinterests,(b)advertiserinterests,(c)censorshipornudgesfromthegov‐
ernment,(d)threatsagainstestablishedpower(corporateorstate),and(e)limitingthe
mobilizingeffectofdissent.Thesefiltersformsignificantbarriersagainsttheutilization
ofmainstream,‘established’medianewsstreamsandasillustratedinthebombingofSer‐
biain1999,theyresultinthesignificantoverrepresentationofviolenceconductedbyUS
adversaries.
Largelyduetotheselimitationsof‘classical’crisismediaeventdatacollectionproto‐
cols,theadventofsocialmediawasheraldedasanimportantnovelty.Themostcritical
noveltysocialmediabroughtwasits‘disintermediated’nature.Disintermediationisthe
reductionorremovalofintermediariesinasystem(inthiscase,newsandcommunication)
andwithinthecontextofdigitalcommunication,itdefinesthereducedimportanceof
traditionalnewsintermediaries—editors,reporters,censors—thatserveasthemiddle‐
menbetweenthenewssourcesandnewsconsumers(Sampedroetal.2022).Socialmedia
enablesaviolenteventoramigrationmovementontheground,tobedocumentedby
participantsthemselves,orpasserbycivilians,throughvideos,images,andtextsinreal
time,withoutanyintermediaries,andverylittle(andobscure)contentmoderation,di‐
rectlyintothenewsfeedsofdigitalusersacrosstheworld(Eriksson2018).Socialmedia
platformshavebothoverlappingandnichecapabilities.Twitterexcelsinshort‐text/short‐
mediaformat,whereasFacebookcanbeusedforgallery‐styleextendeddocumentation.
Instagramoffersamorevisualplatformexperience,whereasTikTok’scomparativead‐
vantageisshort‐term‘mood’videos(Jaidka2022).WhileTwitterdatahassofarbeenused
extensivelyforcrisisresearchduetoitsgranularAPIsystem,researchersareincreasingly
experimentingwithothersocialmediaplatformsforcrisiseventdata.
Thedownsidesofsocialmediadatahavepreciselybeenby‐productsofthesamedis‐
intermediationthatalsoservesasitsstrength.Duetotheabsenceofverificationandfact‐
checkingfilterssuchasinvestigativereportersordeskeditors,socialmediahasbeenrife
withdisinformationandredundancy(Gohdes2018;Zeitzoff2018).Additionally,ithas
beenfallingpreytoanotherformofavailabilitybias,wherebysocialmediafielddatacan
onlybeproducedwheresmartphones,internetaccessorcellphonetowersarepresent.
Thisisanimportantfactorbecauseinwaranddisasterzoneswhereallthreecanoftenbe
missing,socialmediaascrisiseventdatacanbeverydifficulttoproduce.Anumberof
recentstudiesdemonstratethisclearlyascellphonecoverageandinfrastructurehasa
strongimpactonthevolumeandreliabilityofsocialmediadatacomingfromdifficult‐to‐
accessregions(PierskallaandHollenbach2013).
Theremainderofthispaperwillfocusonthefollowingissues:
‐ Advantagesanddisadvantagesofdifferentformsofeventdatathatfeedcurrently
deployedA.I.monitoring/forecastingmodels.
‐ Adiscussionofwhysocialmediaisbecomingmorepopularasfielddatathatisbeing
usedtotrainforecastingmodels.
‐ EthicalconsiderationsinusingsocialmediadatatotrainA.I.conflict/migrationfore‐
castingclassifiers.
Soc.Sci.2022,11,3954of15
2.The‘AchillesHeel’ofForecasting:DataReliability
Forecastingcriticalandresource‐intensiveeventslikecrises,migration,violence,and
warshavelongbeenindemandamonggovernment,military,andinternationalorgani‐
zationcircles.Buildingpredictionandearlywarningsystemsallowsgovernmentsand
agenciestoprepareforrelief,aid,andlawenforcementplanning,andbuildresilience
againstmajorshocks.Giventhelimitedresourcesofemergencyresponseandreliefinsti‐
tutions,forecastingcanbeanimportantcostoptimizationprocess,allowingsuchagencies
tobereadyduringtime‐sensitiveepisodesthatrequiresubstantialresources.
Forecastingisalsoviewedwithwavesofsuspicioninsocialsciences.Mostdirect
criticismisthattheverybasisofforecasting:Collatingpriorinstancesofcasestoinferthe
timingandtypeoffutureevents,hasbeenviewedasunrealistic,orunabletoproperly
capturesocialuncertainty(Dowding2021).Whilemoretransformativeandlarge‐scaleof
suchevents—‘blackswans’(Ahmadetal.2021)—haveindeedbeendifficulttoforecast,
proponentsofthisapproachviewedforecastingasnonethelessusefulinestimatingthe
futuretrajectoryofexistingevents.Thisimpliesthat,foranyforecast,atleasttwotypes
ofinputarerequired:historicaldata,whichcanbeintheformofevent,population,meas‐
urementorsurvey;andamodel,whichwillformthefoundationoftheestimatorthatwill
determinethewayhistoricaldatawillbeprocessedtoproducetheoutput,namely,pre‐
diction(Efendietal.2018).
Eventdatahasbeenanimportantinputinviolenceandmigrationforecasting.Given
thefactthatsnowmeltsduringspringtime,insurgencyandcounter‐insurgencyopera‐
tionsrecordanuptickinmountainousareasduringtheseperiods.Monsoonseasonswit‐
nessheavyrainfall,whichrestrictsthemovementofarmorinmuddyregions,resulting
inreducedoffensivesbystatemilitaries.Massmigrationawayfromtheconflictareasis
expectedwhenthemonsoonseasonends,thesoildries,andheavyarmorcanonceagain
moveintocombatnearpopulationcenters.Heavyartilleryshellingoftenindiscriminately
targetscivilianpopulationcenters,andthereforethemovementofmobileartilleryunits
closertosuchpopulationcenterstendstogeneratemassexodus.Inwinter,heavysnowfall
andfrostrendercombatoperationsinruggedareasmoredifficult,socombatantsprepare
forspringoffensivesinordertopursuetheirtacticalobjectivesaroundtheseareas.As
forcedmigrationoftenhappensaroundtheseseasonalvariances,battlerepertoires,and
violencepatterns,theseindependentvariablesprovideresearcherswithsomedegreeof
confidencethatpastinstancesofeventscanbeharnessedtopredictunrealizedoutcomes.
Itisimportanttoemphasizethatconflictsandmigrationarespatio‐temporallymulti‐de‐
pendenteventsandstandardlinearforecastsoftenfailtoaddressthenuancesofsuch
variance(Christiansenetal.2021).
Givenitshigh‐levelgranularityanddatavolumeadvantagescomparedtomoretra‐
ditionalformsofcrisisinformation,socialmediadataisbeingincreasinglyleveragedto
monitor,aswellasforecast,conflictsandmigrationevents.Itisalsobeingusedaspartof
thebroadersuiteofadditionaldatainputstotrainartificialintelligence(A.I.)classifiers
thatarebeingdeployedtomonitorandforecastmigration(Alexanderetal.2022;
Willekens2018;Ningetal.2019;Salah2022;BircanandKorkmaz2021).Givenbiaslimi‐
tationsoftraditionalmilitaryobserver,warreporter,classicalmedia,andaidworker‐
basedfielddata,conflictandmigrationforecastingbigdataandA.I.approachesarebe‐
comingincreasinglyinterestedinharvestingsocialmediaasanadditionallayeroffield
reportseitherinproducingnewdatasets,ortocomplementexistingones.Socialmedia
datacanincreasetherobustnessofpredictionmodelsandforecastingconfidenceasit
providesresearcherswithagreatervolumeoffielddatafromcrisisevents,andifcleaned
forredundancyanddisinformationsufficiently,theycanstrengthenthevalidityofclaims
inferredthroughforecasts(Kaufholdetal.2020;Shanetal.2019).
Inthemigrationandconflictforecastingcontext,A.I.couldbedefinedas“agrowing
resourceofinteractive,autonomous,self‐learningagency,whichenablescomputational
artifactstoperformtasksthatotherwisewouldrequirehumanintelligencetobeexecuted
successfully”(TaddeoandFloridi2018).Amongtheseself‐learningtasksarethe
Soc.Sci.2022,11,3955of15
methodicalassessmentofalgorithmsanddatainputstoimprovetheirknowledgeofper‐
formancewithexperience(Flach2012).A.I.‐basedanalyticsandforecastingmethodshave
beenincreasinglyusedbyinternationalorganizationsandgovernmentstocontroland
managehumanmobility.Thesemethodsincludeidentityrecognition,automatedborder
monitoring,andanalysisofasylumapplications(Beduschi2021).Recently,thesemethods
alsobegantoharvestconflicteventdata(includingsocialmediadata)inordertotrain
A.I.modelstopredictmigration(Molnar2019a).Algorithmicmigrationmonitoringisbe‐
ingincreasinglyreliedonintheUnitedStates,UnitedKingdom,Canada,andmostEuro‐
peanUnionmembersstates,andformsthebasisofnext‐generationmigrationtechnology
investmentinmajorinternationalorganizations.
A.I.‐basedanalysisandforecastingsystemsrequiresignificantvolumesofdata—es‐
pecially‘bigdata’intheformofnotjustlargevolumeinformation,butalsohighgranu‐
larity,highvelocity,andhighcomplexityinformation.Whilethereisnotalwaysadirect
relationship,datasizeisoftenanimportantvariableinmoresophisticatedandaccurate
A.I.‐basedsystems.Tothatend,trainingmachinelearningclassifiersisadata‐hungryen‐
deavor,whichrequiresnotjustagreaternumberofevents,butmultiplereportsofthe
sameeventforgreaterrobustnesscheckanddatavalidity.Inaddition,greaterdatainput
canrenderdecisionandoutputchainsmoreefficientinfurtheriterationsbytriangulation,
asresultsaremorerobustresultsincrementallytrainfurtheriterationswithgreaterpreci‐
sion(Tarasyevetal.2018;Quinnetal.2018).
3.EthicsofSocialMediaasForecastingData
Forecastingsystemsthatarebuiltonmachinelearningprinciplescangrowincreas‐
inglymoreaccurateintheirpredictionsandcanultimatelymakeinferencesaboutlarge‐
scalehumanflowsduringconflictsornaturaldisasters.Thiscouldaidintheoptimization
ofreliefaidorrefugeecamppreparations,aswellasproperstaffingofmigrationpro‐
cessingcenters(Molnar2019b).
However,forecastingmigrationbroadly,inandofitself,maynotalwaysgenerate
relief‐orientedresults.Mentioningthesegeneralpitfallsareimportantpriortoconnecting
thisargumenttosocialmediadata.Countriescanclosebordersorengageindirectpre‐
ventativeactiontorenderrefugeepathwaysmoredangerous(PécoudanddeGuchteneire
2006;Vives2017).Rebelgroupsmayuseforecastresultspublishedonlinetocrackdown
onciviliansandcutofftheirescaperoutes(Bruce2001;LarsonandLewis2018).Anim‐
provedabilitytopredictmigrationmayalsocauselocalpopulationstofleeearlierandin
greaternumbers,giventhefactthatsuchpredictionsareoftensharedthroughsocialme‐
diaandcanbeseenbythelocalsthroughsmartphones(Dekkeretal.2018).This,inturn,
playsintothehandsofsmugglers,whomayprovidelesser‐knownpathwaysacrosscoun‐
triesformigrantsescapingstateprecautions,endangeringrefugees(Sanchez2017).These
prospectsgrowmoreproblematicasinsufficientlyoptimizedsystemsgetdeployedinde‐
cisionandanalyticsroles,andendupmisidentifying,miscalculating,andmisjudgingref‐
ugeesandtheiractions.Furthermore,cyber‐attacksanddataprotectionproblemsmay
leadtomigration‐relateddatasetstobestolen,leaked,andusedbymaliciousactors.Itis
importanttounderlinethattheEuropeanDataProtectionSupervisor(EDPS)initscon‐
sultationwiththeEuropeanAsylumSupportOffice(EASO)(D(2019)1961C2018‐1083)
hadconcludedthatsocialmediamonitoringtopredictmigrationpatternsisnotinline
withtheEUregulations.Yet,initsdisclosure,theEASOhasrevealedthatithasbeen
activelyharvestingsocialmediadatainordertosupporttheoperationsoftheDepartment
ofOperations,CountryofOriginInformation(COI),InformationandAnalysisUnit
(IAU),andtheDepartmentofAsylumSupport(DAS),amongothers.
Bringingtheargumentclosertothescopeofthisarticle,theuseofsocialmediaas
migrationforecastingdataontheotherhand,bringsinanevenlargersetofethicalcon‐
siderations.First,thescaleofmigrationdataavailablethroughsocialmediaandmobile
phonesignalsyieldsafalsesenseofsophisticationinanalyses,thatoftenleadsdecision‐
makerstotreatthesedatasourcesasrepresentative,orformattedsufficientlytotrain
Soc.Sci.2022,11,3956of15
machinelearningclassifiers(StewartandWilson2016).Anumberofimportantstudies
havetimeandagaindemonstratedthat,evenwithitsimmensevolume,socialmediaor
cellphonedatafromforcedmigrationscannotbetreatedasrepresentative,orarenotro‐
bustenoughintheircurrentformtoproperlytrainclassifiersthatleadtorealworlddeci‐
sions(SilverandAndrey2019).Thislackofrepresentativenessisamajorcontestationarea
betweenresearchersthatwarnagainstusingmostmedia‐baseddatasystemsontheirown
toguidedecisions,anddecision‐makersthataimtoleveragethesurfacesophisticationof
A.I.modelsbuiltonflaweddatatomarketthe‘validity’oftheirdecisions(Lachlanetal.
2016).Sincepredictiveandanalyticalmodelsarebasedoninferentialstatistics,theyoper‐
ateondegreesofprobability,andthusthe‘acceptable’thresholdfordecisionsbecomesa
politicalbenchmark,ratherthantechnical(Schroederetal.2013).Determiningwhich
thresholdis‘sufficient’tomakelife‐alteringinferencesfromdatausuallybecomesa
murkyprocessthatdecision‐makersfindtoocomplicatedtothinkabout,andgetsdele‐
gatedtoengineerswhohaveneitherthepoliticalorlegallegitimacy,norappropriate
trainingtomakesuchdecisions(Rahwan2018;Cunneenetal.2019).Giventhecomplexity
oftheprocess,importantdetailssuchasthevariablesandmeasurementsusedinbench‐
markingbecomesobscuredfrompublicdebateandcreatesaproblematicethicalgap.
Althoughalgorithmicdecision‐makingstructurescanoftenbeusefulwithsufficient
oversightandcontrolmechanisms,itisdifficulttoassertthatbigdatamigrationforecast‐
ingprotocolshavesufficientsafeguardsinplace,renderingtheverycoreoftheprocess
detachedfrom,andinaccessibletotheverypeopleitdealswith(Zednik2021).Ultimately,
A.I.predictiveandforecastinganalyticsoperateoninferencesbasedonanumberofcor‐
relations,buttowhatextentthesecorrelationsrepresentcausalmechanismsisnotalways
straightforward(Milanoetal.2021).Giventhedatasizeandspeedofsocialmedia‐based
information,establishingflexibleandadjustablecausalmechanismsbecomesverydiffi‐
cult,whichoftenforcesengineerstoofferchoicealternativesfordecision‐makersbased
solelyoncorrelations(BuhmannandFieseler2021).Incaseswheredecisionmakersdonot
havethesufficientbackgroundoradvisorsupporttodigdeeperintothequestioningof
suchcausalmechanisms,aswellastheirdirectionandweight,bigdatamigrationfore‐
castinganddecisionproceduresbecomeopaqueandpoorlycalibrated.
ThisisinlinewithMolnar(2021),wheretheadventofCOVID‐19pandemichasgen‐
eratedgreaterrelianceonbiosurveillance(virus‐targetingrobots,phonetracking,A.I.‐en‐
abledthermalcameras),whichconnectstoTendayiAchiume’s(2021)claimthatsurveil‐
lancetechnologiesarereinforcingspatialracism.Byusingpandemic‐relatedmotives,bor‐
dercontrolagenciesarerelyingincreasinglyonautomatedforecastingandprediction
modelstokeeprefugeesandmigrantswithinconfinedspaces—camps,borderwallareas,
orsegregatedprocessingcenterswithincities.ThesearewhatJasondeLeon(2015)calls
as‘landofopengraves’,whereauthoritiesconfinerefugeemovementandsettlementinto
dangerousareaswhereself‐sustenanceisoftendifficult.Althoughmoretraditionalbor‐
derprotectiontacticsseeksimilaroutcomes,newertechnologiesrenderautomatedpush‐
backdecisionslessaccountablebyexportingtheauthorityofdecisionstoambivalent
modelsbuiltonmurkytrainingdata.AsunderlinedinMolnar(2021),theexistingtrack
recordofautomatedtechnologiesonraceandgender,openupsthepathforthedeploy‐
mentofsimilarmethodsonmigrationsurveillance.
Second,algorithms(asopposedtohumansupervisedinferencemethods)areincreas‐
inglybeingusedinaself‐learningfashionduringcrisisforecastingandanalysis.Most
conflictandmigration‐relatedsocialmediadataarebeingusedtotrainmachinelearning
classifiers,whichthen‘learn’fromthislimiteddataandextractfutureinstancesofdata
collectionprotocols(Wachteretal.2017).Automateddatacollectionandanalysisparam‐
etersarethusdoublydetachedfromhumanagency,control,andlegalresponsibility:not
onlydoesitperforminanautomatedfashion,butitgrowsincreasinglylessinfluencedby
theoriginalhumancontroloveritthatformeditsprimarylegalandethicalbasis
(Mittelstadtetal.2016).Whilethisproblemremainsintactevenforpoliticallyandlegally
well‐controlleddataprotectionmechanismsliketheGDPR,formigrationandrefugee
Soc.Sci.2022,11,3957of15
forecasting,itisfurtherproblematicallyhiddenfromvulnerablepopulations,whocannot
seeorchallengetheinferencemechanismthatleadstoaparticulardecisionthatconcerns
theirlives.Sincethespecificprocessesbywhichthecollecteddataareusedtogenerate
likelihoodsofanoutcomearemurky,civiliansareexposednotonlytodecisionsmadeby
inconclusiveevidence,butfromalegalstandpoint,cannotreliablychallengesuchdeci‐
sionsbecauseparametersthatcreatesuchdecisionsareabstractedfromhumanagencyby
degrees(iterations)ofself‐trainingprocessesthatgeneratemachinelearningclassifiers
(Zarsky2013).
Indiscussingtheethicsandlegalityofmigrationanalyticsandforecastingprotocols,
twokeyconceptsformthecoreofthedebate.Thesearetransparencyofthedatacollection,
modeling,anddecisionchain;andtheexplainability/comprehensibilityofthetechnicalities
thatliewithinthislink.Often,theseprotocolsandclassifiers,aswellasthedatastreams
thatareintegratedintothem,areacquiredsecondhandfromsecondorthird‐partysup‐
pliersaspartof‘integratedsolutions’thatcontainpresetclassifiersanddecisionthresh‐
olds(Renda2019).Whenusingsuchsecond‐handsolutions,stateinstitutionsorinterna‐
tionalorganizationsriskbeingchallengedontheaccessibilityandcomprehensibility
grounds,sincequiteoftentheseinstitutionsthemselvesdonothavetheengineeringca‐
pacitytounderstandthedetailedanalyticsprotocolsthemselves(Mittelstadtetal.2016).
Sinceinstitutionshavelowtechnicalcapacitytoalterthesethresholds,theyquiteoften
operatewith‘onesizefitsall’parameters,thatfrequentlyproduceinaccurateforecasting
andanalytics.Whilethereisanongoingdebateonwhetherthisaccessibilitygapisinten‐
tionalornot,thereisnonethelesssuchalegalandethicalgapthatremainsinplacewith
suchsolutions.
Third,therichestaspectofsocialmediadata—thatitisdiverse,high‐volume,and
representingabroadrangeofviewsandperspectivesfromfieldevents—hasthedanger
ofrunningcountertotheveryfundamentalbasisofA.I:itregularlymodifiesandalters
itsdetection,datacollection,processing,andmeasurementapproachesinaself‐learning
fashion(VallorandBekey2017).Thisrenderslegalandjudicialoversightandlegitimacy
amovingtarget,asitbringsinacriticalquestion:Whichparameter,threshold,andconfi‐
denceintervalrangewillbethebasisoflegal,ethicalandpoliticalresponsibility?If,for
example,aparliamentoracourtapprovesthedeploymentofaparticularalgorithmic
structuretobedeployedduringemergencies,migrationcrisesandinmonitoringviolent
conflictatt=0,andifthealgorithmsadjustitsfundamentalentityrecognition,statistical
inference,andconfidenceintervaloptimizationparametersast+1,t+2…howwillthis
approvaltranslateoverneweriterationsofthealgorithm?Willthealgorithmbeapproved
‘asis’,orcanitbediscussedanddeliberatedwithinaspecificparameteroscillationrange,
expectingitsfutureself‐learningalterations?
Giventhewealthofsocialmediadataanditsdramaticpeaksandplateausduring
keyevents,usingsuchdataasmachinelearningtraininginputwilllikelycausesignificant
episodicchangestohowitcollectsandmodelsemergencyinformation.Thefundamental
questionthusbecomes:areparliaments,governments,andcourtsequippedtodealwith
thequestionofwhethertheirt=0approvalofanalgorithmwillbevalidornotatt+1
andbeyond?Ifthealgorithmdecidestocollectdatabeyonditsinitiallegalandpolitical
confinestooptimizeitsrobustness,wherewillthelegalandelectoralresponsibilitylie?In
politicaldiscourse,thesealgorithmsaregenerallyconstructedas‘semi‐supervised’sug‐
gestingthattherewillalwaysbeanengineertooptimize/overseetheseparameter
changes,recentscholarshippositsthatthisisnotalwaysthecaseafteranalgorithmis
legallyandpoliticallyapproved(Shneiderman2016;Castets‐Renard2019;Elkin‐Koren
2020).Lawyers,bureaucrats,andpoliticians,inturn,usuallydonothavetheknowledge
oradvisoryassistancetomakesuchassessmentsbeyondthemedium‐termaswell,sug‐
gestingthatself‐learningalgorithmsthatarevettedpriortotheirapprovalmaysteerfur‐
therawayfromtheirinitialparameters,andbecometoolargetoretrospectivelytrouble‐
shoot,astimeprogressesanddatasizeandvariabilityincreases.
Soc.Sci.2022,11,3958of15
Fourth,theuseofsocialmediadataasoneoftheinputsofclassifiertrainingrisksa
highdegreeofdatacleaningandwranglingproblems.Often,datastreamsthatharvest
socialmediaplatformsandfeeditintotrainingandanalyticsdashboardshaveless‐than‐
optimaldatacleaningpractices,whichleadtotheinclusionofpotentiallyredundant,mis‐
leading,andsometimesincoherentinformationintothetrainingchain(Chuetal.2016;
Jainetal.2020;Pavlyshenko2019).Whenaclassifieristrainedbasedon—forexample—a
socialmediadatapoolthatincludesheavybotcampaign,high‐volumedisinformation,or
misleadinginformation,itisgoingtogeneratesignificantlymismatchedinferencescom‐
paredtothegroundreality.Toillustrate,anattackerduringacivilwarcanspreadsocial
mediadisinformationaboutthepresenceof‘potentialjihaditerrorists’amongthefleeing
civilianpopulace,whichmaybepickedupbyautomatedmigrationcrisisdataextraction
protocolsandgenerateaflawedinferenceabouttherefugees,generatingdisproportion‐
atelypreventativeorharshtreatmentbystatesecurityservices,refugeecampadministra‐
tions,orevenbythelocalsofthetownsandvillagesalongthemigrationpath.Sincea
propercleaningandformattingofsocialmediadatatakestime,currentA.I.protocolshave
ahighlikelihoodofgeneratingnoisyandflaweddecisionsduringcriseswithtimecon‐
straints.
Thisincreasestherisksofdiscriminationinbigdatamigrationresearch.WhileRomei
andRuggieri(2014)suggestthatacontrolleddistortionoftrainingdata,introductionof
anti‐discriminationinputsintotheclassifiers,post‐processingofclassifiersforasecond
roundcheck,andmodifyingfairness‐relatedparametersinfurtheriterations,thisprotocol
becomestrickywithsocialmediadatathatrendersthesesafeguardsextremelytime‐con‐
suming.Giventhefactthatrefugeecrisesareextensivelysecuritizedintraditionaland
socialmediadata,narrativesofmigrationtendtobehighlysecuritizedaswell(Colombo
2018).Usingsocialmediadirectlyasatextinputalsoinjectsthisdiscursivediscrimination
intoanalyticalandforecastingchains,creatingautomaticbiasesaboutmasshumanmo‐
bility.Althoughtechnicallysocialmediatextdatacanbepost‐processedtomitigateorat
leastsoftentheseunderlyingdiscursivebiases,itisbothverytimeconsuming,andalso
highlycontext‐specificgiventhepeculiaritiesofforeignlanguages,slang,andsatire/sar‐
casmdynamics(BatrincaandTreleaven2015).WhiletheseparametersarewellsetinEng‐
lish,muchmoreworkisrequiredtoconductthiscleaningworkinotherlanguages—es‐
peciallyinlanguagesinwhichdatacleaningengineersarenotproficient.Thisissuenot
onlycreatesageneralizedunfairnesssinceanalgorithmactivelybeingtrainedonsocial
mediadatawillcreateimmediatebiasesagainstrefugees,butitalsoproducesasecond
setofbiasandunfairnessduetothealgorithm’stextcorpusvariancesacrossdifferent
languages.
Fifth,andconnectedtothethird,thesheersizeoftheA.I.forecastingandanalytics
datainfrastructures,rendersreverseengineeringofmistakesverydifficultandtimecon‐
suming(Sejnowski2020).Oncesuchdecisionsleadtoflawedoutcomes—especiallythat
resultinharmagainstrefugees—returningtotrainingdataandtryingtounderstand
whichcomponentcausedtheflawedoutcomebecomesadauntingtaskwithsocialmedia
data.Sincesuchtroubleshootingandfixingworkrequiresextensivestaffing,time,and
financialresources,decidingtoengageinsuchenterpriseitselfbecomesapoliticaland
bureaucraticdecision(Agrawaletal.2018).Evenifsuchdecisionistaken,tryingtofind
theneedleintheproverbialdatahaystacknecessitatesredirectingresourcesfromdaily
monitoring,analytics,anddecision‐producingprotocols.Suchdiminishingreturnsren‐
derstheentiretyofthetroubleshootingprocessdeterringandpotentiallyunsustainable,
causinganalyticsandpredictionteamstogoaheadwithinsufficientlytrainedclassifiers
thatcontinuetoproduceflawedresults—andworse:learnfromtheseflawedpriors
(CoglianeseandLehr2016).Whensuchcasespersist,itbecomesnearlyimpossibletofind
whichdatastreamcausedtheflawedinterpretation,whichengineeringdepartmentwas
responsibleforit,andmostimportantly:whoistobeheldlegallyaccountableforsuch
flawedinterpretations.Thiscreatesnotjustanethicalproblemwherebythesourceof
harmagainstmigrants/refugeescannotbeidentifiedreliably,butitalsoposesasignificant
Soc.Sci.2022,11,3959of15
legalproblemwhereresponsibilitycannotbetraced,andbindingrevisionscannotbeap‐
pliedtoproperindividuals,teams,andinferencemechanisms.Notonlythatthismecha‐
nismputspeopleatriskintoharm,butitalsocreatesadigitallyauthoritarianconstruct
whichinvisiblyshutsthedoortocomplaints,informationrequestsandlegalreparation
options.
Thereareotherproblemswithbigdatamigrationandviolenceforecastingthatis
beyondthescopeofthispaper,whichfocusesonusingsocialmediaasclassifiertraining
data.Theseproblemsinclude(butarenotlimitedto)problemswiththeanalysisandmod‐
elingrobustnessaspectofalgorithms,i.e.,thefactthatthestatisticalparametersthatgen‐
eratedecisionsorhelpthealgorithmlearnsecondarytasksmaynotalwaysbecalibrated
wellenough,oraccurate.Sincethereisaknow‐howandformationaldisconnectbetween
engineerswhocalibratethesemodelsandthedecision‐makerswhoactonthefindingsof
suchmodels,inferencemodelaccuracybecomeslessofamathematicalortechnicalen‐
deavorandmoreofapoliticalandethicaloneinwhichconfidenceintervals,variable
weights,directions/strengthofcorrelations,andhowwithin‐sampleevidenceisusedto
generateout‐of‐sampleforecastsbecomebiased,andpotentiallydiscriminatory.
4.AvenuesforEthicalSocialMediaDataUseinMigrationandViolenceForecasting
Theaboveaccountmaysuggestthatthenumberofissuesthatcurrentlyobscurethe
useofsocialmediadataasareliableemergencyinformationfortrainingA.I.‐basedsys‐
temsandassistingintheirinferenceandforecastprotocolsaretooinsurmountable.This
doesnotimplythatsocialmediadatacannotbeusedforsuchpurposes,butanumberof
broaderethicalandlegaldecisionshavetobemadebeforemoretechnical/programming
optimizationworkcanbeundertaken.
Oneofthefirstpathwaystorendersocialmediadata‘moreuseable’inalgorithmic
formatwouldbetofindwaystoanonymizeandrendersuchdatauntraceablebacktoa
vulnerableperson,oragroup.Whilesomeanonymizationapproachesareavailablefor
accountswhosesocialmediapostsarepartoftrainingclassifiers,suchanonymizationbe‐
comesharderinvideoandimagedata,wherethecontentiscrucialforinference,andcan
oftencontaininformationaboutactualidentityofindividualsandgroupsthatareboth
vulnerableandindanger(Beigietal.2018;TownsendandWallace2017;Zhangetal.
2018).Anewanonymizationprotocolisnecessarytocreateadisconnectbetweenactual
identitiesandtheinferenceproducedasaresultofit,aswellasfurtherstudiesthatex‐
plorehowlackofanonymitycreatesunintendeddiscriminatoryinferencesbyalgorithms.
Thislogic,however,needstobedissectedwhenconsideringindividualversuscol‐
lectiveharm.Harmanddiscriminationtowardsone,orasmallnumberofmigrantsfits
intoadifferentanonymizationdebatecomparedtoharmanddiscriminationtowardsa
largerflowofmigration.Intheformercontext,anonymizationcanprovideaviablesolu‐
tion(asitisalreadydeployedassuchbytheEASOandUNHCR),althoughitstartsto
becomelessrelevanttothemigrantprotectiondebatewithinthecontextoflarge‐scale
flows.Thispointbecomesalargerpoliticalandideologicalissue(anti‐immigration),as
opposedtoatechnicalandhumanrights‐relateddebatebecausediscriminationofoneor
agroupofmigrantswithinthebasisofethnicity,religionandbiometricssitsinadifferent
corneroftheanonymizationdebatecomparedtoextractingdatafromamassexodus.In
thelattercontext,identity‐relatedvariablesformasecondaryconsiderationinlightofthe
moreimportantfirstconsideration:wherethemigrationflowhasoriginated,whereitis
headedto,andwhichpathwaysittransitsthrough.Thedifferencebetweenthetwoalso
containsthedifferencebetweenflawedstateresponses(a)bydenyingtherighttoclaim
asylumbyawholesalepreventionormigration,and(b)bytargetingone,oragroupof
peopleinadiscriminatoryfashionusingdatacollectedthroughsocialmedia.
Thesecondpathistobroadenthejudicial,bureaucratic,andparliamentarydiscus‐
sionsontheresponsibilityofA.I.systems.Whilethisdecisionwilllikelydifferacross
countriesandpoliticalsystems,countrieswillmostlikelyoptforasharedresponsibility
infrastructureforA.I.‐baseddecisionerrors,distributinglegalandpoliticalerrormargins
Soc.Sci.2022,11,39510of15
betweenengineers,systemmanagersandA.I.protocols.Inaflaweddecisionthatcreates
asignificantlevelofhumansuffering,thelegalattributionchainwilllikelyfollowthrough
(1)theinstitution,(2)thedirector,(3)thesub‐manager,(4)thechiefengineer,(5)thejunior
engineerthatruns,operates,andmaintainsthecodestructureofthealgorithm.Which
lineofthehierarchyistoblamewilllikelybeapoliticaldecisionthatdifferentcountries
willtakedifferentlybasedonpowerdynamicsbetweenvariousrelatedinstitutions,and
withintheinstitutionthathasundertakentheflaweddecision(Shah2018).Inrecentyears,
anewdebateemergedonthe‘moralagency’ofalgorithms—i.e.,whethernon‐humanac‐
torsofanalgorithmsuchassequence,selection,anditerationcanbeheldresponsibleina
courtoflaw(Véliz2021;Cunneenetal.2019).ThisdebateriskswhatWachteretal.define
as‘de‐responsibilizationofhumanactors’,whichinsimpletermsisto‘hidebehindthe
computer’andexportingtheresponsibilityofflaweddecisionstounprosecutableagents
(Löschetal.2017;Kirkpatrick2016).ThistrendrunsclosertoHannahArendt’sobserva‐
tionofhowinhumanandstructurallyhostileactionscandispassionatelybeundertaken
bybureaucraticnetworksandactorsthatarefollowingrulesandprotocolsthatcreate
automatediterationsofsufferingwithoutagencyontheirpart.
Thethirdpathfollowsthe‘righttoreasonableinferences’debateanditsintroduction
intothedebateontheuseofsocialmediadataforbigdatamigrationandviolencefore‐
castingpurposes(WachterandMittelstadt2019;Veroneseetal.2019).InGDPRcontext,
thisrightpositsthatindividualswhosedataisharvestedforinferenceandprediction
tasksthatconcerntheirlives(‘high‐riskinferences’),havetherighttoaskforjustification
bytheappropriatedatamanagertodisclosewhethersaidinferencehasbeenproduced
throughjustifiableandexplainableprotocols.Whenusedaspartofadecisionoraction
thatconcernsmigrantsandrefugees,thealgorithmmanagersandengineershavetopro‐
duceapublicexplanationthatoutlinestherationaleforusingthedatasettoproducesaid
output,whethertheformofinferenceandmodelingistrulyappropriateforsaiddecision,
andwhethertheoutputissufficientlyrobustandstatisticallymeaningfulwithinthelegal
andlegislativeparameterssetbythatcountry’slawsandregulations.
Toconclude,high‐velocitysocialmediadatastreamscontainsignificantpotentialfor
emergencymonitoringandforecasting;however,manycontemporaryexamplesofsuch
attemptsremainethicallyandlegallyproblematic.Thisproblemisnotlimitedtotheuse
ofsocialmediadata,butconcernsseveralothercomponentsofbigdatamigrationand
conflictforecastingpractices,suchasflawedinferences,opaquedecisions,andpolicy‐en‐
gineeringmismatchbetweenthresholdsthatgenerateprofiling,monitoring,andscenario‐
building.Thispaperhasarguedthattheuseofsocialmediadatacanexacerbateexisting
problemswithexplainability,interpretability,andtransparencyofA.I.forecastingand
decisionsystemsthatdealwithviolenceandforcedmigration.Thispaperhasarguedthat
theuseofsocialmediadatacanexacerbateexistingproblemswithexplainability,inter‐
pretability,andtransparencyofA.I.forecastinganddecisionsystemsthatdealwithvio‐
lenceandforcedmigration.Largedatainjectionthroughtheinclusionofsocialmediadata
intoself‐learningA.I.monitoringsystemsgenerateafalsesenseofsophisticationthat
forcesdecision‐makerstodisregardthevalidity,representativeness,androbustnessof
suchsystems.Sincetheyareself‐learningsystems,learningclassifiersthatproducedeci‐
sionoptionsbecomedetachedfromthepeopletheyismonitoring,becomeunreachable
fromthemigrants’standpoint,andcanpotentiallyendangervulnerablepopulationsun‐
derduress.
Third,sincesocialmediadatacontainsrapidlychangingstances,discourses,andnar‐
ratives,A.I.systemsthatusesuchdataastraininginputriskssteeringoutsidetheconfines
oflegalandparliamentaryoversight.Whilecourtsandlegislatorsmayethicallyandle‐
gallyapproveaself‐learningalgorithminitially,weeksandmonthsafterthesealgorithms
startlearningontheirown,theyhaveagreatlikelihoodofstrayingintoanoversight‘gray
zone’.FurtherproblemsarisefromthefactthatmostA.I.migrationandconflictmonitor‐
ingsystemshavesuboptimaldatacleaningpractices.Thisrendersmodelsvulnerableto
botcampaignsanddisinformationandrisksbiasanddiscriminationagainstmigrant
Soc.Sci.2022,11,39511of15
populations.Finally,althoughsocialmediadatabringsimmensedatadiversitytohelp
teachmachinelearningclassifiers,itssheerscalerendersretrospectivetroubleshooting
verydifficult,especiallywhenthesesystemsmakeacostlyflaweddecisionandreverse‐
engineeringbecomesessentialtofindoutwhichdatastreamscausedtheproblem.Alt‐
houghitisstillearly,oncetheseissuesarediscussedandpotentiallyresolved,socialme‐
diadatacanbeusedethicallyandcleanlyinfutureA.I.migrationandconflictmonitoring
tasks.
AnimportantstepinthesedirectionsweretakenbytheEuropeanDataProtection
Supervisor(EDPS)initsconsultationwiththeEuropeanAsylumSupportOffice(EASO)
(D(2019)1961C2018‐1083),wheresocialmediamonitoringformigrationpredictionwas
deemedillegalwithintheparametersoftheEUlaw.However,itisimportanttokeepin
mindthattheEDPSconclusionisonlylimitedtoEASO’soperations,andassertsthat
trackingmigrationwithinthecontextofmigrantsmugglingandhumantraffickingmon‐
itoringarebeyondthescopeofEASO,andtherefore,leavesagrayareaforotherEUagen‐
ciesthatmaybemonitoringsocialmediaformigrationonthepretextthatsuchmonitor‐
ingisdoneto‘preventcrime’.
Still,however,thereissignificantdiscussionspaceastowhatextentEDPSjudgement
willhaveabindingeffectonotherEUnations’dailyborderprotectionpractices,orform
amodeltoemulatefortheborderprotectionagenciesoftherestoftheworld.Asthe
competitiontotrainmoreaccuratemodels—eitherinconflictormigrationprediction—
socialmediadatawillremainacontroversial,yetanincreasinglypopularchoice,asthe
debateoverhowbesttouseitethicallyandtomeetstrategicobjectiveswilllikelycontinue
inthefollowingyears.
Funding:ThisresearchwaspartiallyfundedbytheScientificandTechnologicalResearchCouncil
ofTurkey,ARDEB1001Program,GrantNumber120K986;andTheScienceAcademySocietyof
Turkey:2021‐BAGEPProgram.
InstitutionalReviewBoardStatement:Notapplicable.
InformedConsentStatement:Notapplicable.
DataAvailabilityStatement:Notapplicable.
ConflictsofInterest:Theauthordeclaresnoconflictofinterest
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