sustainability
Article
HowtoDetectOnlineHatetowardsMigrantsandRefugees? DevelopingandEvaluatingaClassifierofRacistand XenophobicHateSpeechUsingShallowandDeepLearning
CarlosArcila-Calderón 1,* ,JavierJ.Amores 1 ,PatriciaSánchez-Holgado 1 ,LazarosVrysis 2 , NikolaosVryzas 2 andMartínOllerAlonso 3
1 FacultaddeCienciasSociales,CampusUnamuno,UniversityofSalamanca,37007Salamanca,Spain
2 MultidisciplinaryMedia&MediatedCommunicationResearchGroup(M3C), AristotleUniversityofThessaloniki,54124Thessaloniki,Greece
3 DepartmentofSocialandPoliticalSciences,Università degliStudidiMilano,20122Milano,Italy
* Correspondence:carcila@usal.es
Citation: Arcila-Calderón,C.; Amores,J.J.;Sánchez-Holgado,P.; Vrysis,L.;Vryzas,N.;OllerAlonso, M.HowtoDetectOnlineHate towardsMigrantsandRefugees? DevelopingandEvaluatinga ClassifierofRacistandXenophobic HateSpeechUsingShallowandDeep Learning. Sustainability 2022, 14, 13094.https://doi.org/10.3390/ su142013094
AcademicEditors:StefanoRuggieri andAlessiaPassanisi
Received:19September2022
Accepted:11October2022
Published:13October2022
Publisher’sNote: MDPIstaysneutral withregardtojurisdictionalclaimsin publishedmapsandinstitutionalaffiliations.
Copyright: ©2022bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticleisanopenaccessarticle distributedunderthetermsand conditionsoftheCreativeCommons Attribution(CCBY)license(https:// creativecommons.org/licenses/by/ 4.0/).
Abstract: Hatespeechspreadingonlineisamatterofgrowingconcernsincesocialmediaallowsfor itsrapid,uncontrolled,andmassivedissemination.Forthisreason,severalresearchersarealready workingonthedevelopmentofprototypesthatallowforthedetectionofcyberhateautomatically andonalargescale.However,mostofthemaredevelopedtodetecthateonlyinEnglish,andvery fewfocusspecificallyonracismandxenophobia,thecategoryofdiscriminationinwhichthemost hatecrimesarerecordedeachyear.Inaddition,adhocdatasetsmanuallygeneratedbyseveral trainedcodersarerarelyusedinthedevelopmentoftheseprototypessincealmostallresearchers usealreadyavailabledatasets.Theobjectiveofthisresearchistoovercomethelimitationsofthose previousworksbydevelopingandevaluatingclassificationmodelscapableofdetectingracistand/or xenophobichatespeechbeingspreadonline,firstinSpanish,andlaterinGreekandItalian.Inthe developmentoftheseprototypes,threedifferentiatedmachinelearningstrategiesaretested.First, varioustraditionalshallowlearningalgorithmsareused.Second,deeplearningisused,specifically, anadhocdevelopedRNNmodel.Finally,aBERT-basedmodelisdevelopedinwhichtransformers andneuralnetworksareused.Theresultsconfirmthatdeeplearningstrategiesperformbetterin detectinganti-immigrationhatespeechonline.Itisforthisreasonthatthedeeparchitectureswerethe onesfinallyimprovedandtestedforhatespeechdetectioninGreekandItalianandinmultisource. Theresultsofthisstudyrepresentanadvanceinthescientificliteratureinthisfieldofresearch,since uptonow,noonlineanti-immigrationhatedetectorshadbeentestedintheselanguagesandusing thistypeofdeeparchitecture.
Keywords: hatespeech;racism;xenophobia;migration;socialmedia;deeplearning
1.Introduction
Violentspeechisnotanexclusivecommunicationaldysfunctionofourcontemporary societies,butitistoday,whenitseemsmoreworryingthaneverduetoitsmassivediffusion ondigitalplatforms.Theinternetandinformationandcommunicationtechnologieshave todayallowedonlinehatespeechtoincreaseunabated.Inthisnewcontext,socialmediahas becometheforuminwhichthistypeofmessagespreadsmorequicklyanduncontrollably, asevidencedbythelatestreportspublishedbytheAnti-DefamationLeague[1,2].This growthinonlinehatespeechalsocoincideswithanunstoppableincreaseinregistered hatecrimesinEurope[3],whichcouldevidencethecorrelationbetweenbothphenomena pointedoutbyMüllerandSchwarz[4].Moreover,ifthisconnectionisso,sincemostofthe hatecrimescommittedinEuropeareduetoracistand/orxenophobicreasons(according tothedatacollectedbytheOSCE’shatecrimereporting),wecouldaffirmthatmostof theincreasinghatespeechthatisspreadonlineisbasedonthistypeofdiscrimination
,13094.https://doi.org/10.3390/su142013094https://www.mdpi.com/journal/sustainability
andisaimedmainlytowardsmigrants,refugees,andasylumseekerswhocometoorare withinEuropeanborders.Inthissameline,recentworksdevelopedbytheauthorshave evidencedanegativetrendintherepresentationofmigrantsandrefugeesthatisspread bythemainmediaofMediterraneancountries[5]andinWesternEurope[6],whichcould bealsorelatedtotheincreaseinracistandxenophobichate.Otherstudiesalsoindicate thephenomenonofderesponsibilisation[7]ofhatespeechspreadingonline,especiallyby youngeraudienceswhofeelthattheirpublichatelanguageshouldnotbetakenseriously.
Withthesepremises,someresearchershaveunderstoodthatisurgenttoexplorenew methodsfordetectingandpreventingonlinehateatthegloballevel,butalsoinregional contexts,whereonlineandofflinehatehasnotstoppedincreasingeither.Forthisreason, inrecentyears,diversepublicandprivateinstitutionshavebeenmakinggreateffortstotry todetectandcounterhatespeechonline,althoughmostlyinageneralwayandnotdealing specificallywithracistandxenophobichate.Inaddition,thelargeamountofinformation offeredondigitalplatformstodaymakesitmoredifficultthanevertomonitor,detect,and combatthesehatefulcontents.This,inturn,meansthatvictimsofonlinehatemightbe increasing,somethingthat,intheSpanishcase,thelatestRaxenreports[8,9]show,even thoughmostincidentsmightnotberecorded.Inthissituation,itisimportanttotryto developnewmethodologicalstrategiesthatallowustomonitortheseviolentspeechesthat spreadonsocialplatforms,payingspecialattentiontoracistandxenophobichate.Taking thisintoaccount,itissurprisingthat,althoughtherearealreadyseveralresearcherswho areaddressingthisprobleminEnglish,therearestillsofewresearcherswhoaredoingitin otherspeakingcontexts,focusingspecificallyonanti-immigrationhateonline,thecategory thatinternationallyworriesthemost[10,11].
Withthesepremises,theaimofthisworkistogenerateandtestadetectorofracistand xenophobiconlinehatespeech.Thenoveltiesofthisworkcomparedtothepreviousones areseveral.Inthefirstplace,forthedevelopmentoftheprototypes,wewillgenerateadhoc datasetswithmessagesextractedfromsocialmedia,manuallyannotatedbyseveraltrained coders.Second,inadditiontoshallowlearningalgorithms,deeparchitectureswillalsobe usedinthedevelopmentoftheprototypes.Ontheonehand,anadhocdevelopedRNN modelisdeveloped,andontheotherhand,aBERT-basedmodelisdeveloped,inwhich transformersandneuralnetworkswillbeused.Finally,althoughtheprototypeswillbe trainedandtestedfirstintheSpanishlanguage,theoneswiththebestperformancewilllater beimprovedandretrainedfollowingthesamestrategysothattheyarecapableofdetecting anti-immigrationhatespeechalsoinGreekandItalian(Mediterraneancountriesthatare themaingatewayformostimmigrantsarrivinginEurope)andinmultiplesources.These detectorswillidentifyincreasesinthistypeofcyberhateanddeveloptailoredprograms tocombatandcounterit,butalsowillacquireempiricalknowledgeabouttheseviolent speeches,aboutthegroupstowhichtheyareaddressed,aboutthesourcesorprofiles propagatinghate,andlastly,abouthowthesetypesofmessagescouldbetriggeringhate crimesinthephysicalenvironment.Inshort,thisprototypewillallowustounderstandthe spreadofonlinehateaimedtowardsdisplacedpeopleandtodevisestrategiestocounteract andpreventitspossibleeffects,includingphysicalhatecrimes.
2.DefiningOnlineHateSpeech
Hatespeechisnotanewconcern;in1997Calvertalreadypointedtothistypeof discourseasaproblemtoanalyse,understand,andcombatwithcommunicationalapproaches,necessarilyinvolvingallelementsofthecommunicationtransmissionmodels. However,hatespeechhasbecomeabiggerconcerntodayduetotherapidgrowthof digitalmedia,especiallysocialmedia,inwhichformerreadersandaudienceshavebecome prosumers[12],withmoreandmorefollowerstowhomtheycanlaunchtheirmessages andcontent.Inaddition,nowadays,itseemsthatthemoresensationalistthiscontentis, themorefollowersitgets.Withoutadoubt,inthisnewdimensionofimmediacyand freedom,itismucheasierforhatefulmessagestospreadquicklyandwithoutanykind ofcontrol.Forthisreason,hatespeechhasnotstoppedincreasinginrecentyears,and
thatiswhyithasbecomesomethingsocomplexanddifficulttodetectandcombat,as wellasnecessaryandurgent.Thisiswhatthepresentworktriestosolvethroughanovel computationalstrategythatseekstoautomaticallydetectlatenthateinthemessagesspread throughTwitter,primarilyinSpanishand,secondarily,inGreekandItalian.However, beforetacklinganyonlinehatespeechdetectionstrategy,itfirstneedstobedefined.
Thus,fromatheoreticalapproach,hatespeechisunderstoodasthepromotionof messagesthatimplyrejection,contempt,humiliation,harassment,discrediting,andstigmatizationofpeopleorsocialgroupsbasedonattributes,suchasnationalityorcolourof skin.Thus,forspeechtobeconsideredhateful,oneofthemainconditionsisthatthe discriminatorymessageisdirectedtowardsoneofthevulnerablegroupstypifiedinthe Europeanframework,ortowardsanindividualwhocanbeidentifiedaspartofoneof thosecollectives,whoserejectionismotivatedbytheirapparentbelongingtothegroup. Inthissense,theCouncilofEurope[13]addsthatforspeechtobeunderstoodasahate crime,itmustpropagate,incite,promote,orjustifyracism,xenophobia,anti-Semitism,and otherformsofintolerance.TheEuropeanCommissionagainstRacismandIntolerance,in itsGeneralRecommendationNo.15[14],alsospecifiesthathatredcanbemotivatedby reasonsofrace,colour,ancestry,national,orethnicoriginamongmanyothercharacteristics orpersonalconditions.Ascanbeseen,theofficialdefinitionsofhatespeechpayspecial attentiontoracistand/orxenophobicdiscriminationasthemaincauseofalltypesof rejectionandhate.Foritspart,theMinistryoftheInteriorofSpain,initslatestevaluation reportonhatecrimesinSpain[15],collectedatotalof11categoriesofdiscrimination intowhichcrimescommittedagainstvulnerableaudiencescanbeclassified,whereracism and/orxenophobiaarethefirst,inwhichmorecrimesareregisteredeveryyear.
Intheacademicsphere,someauthorssuchasMiró Llinares[16]havealsostudiedhate speechoffering,inadditiontoabroaddefinition,ataxonomywithdifferentlevelsofhate online.Thus,accordingtothisauthor,itispossibleandnecessarytodifferentiatebetween thetypethehatespeechthatcouldconstituteacrime,fromthespeechthat,evenexpressing rejectionandintoleranceofcertainvulnerablegroups,canbeframedwithinthemargins offreedomofexpression.Thesetypesofmessageswouldincludeslightinsults,criticism, andoffensestoindividualorcollectivesensitivity,whichinsomecases,couldbeanattack onpeople’sdignity,butnotahatecrime.Regardingillegalhatespeech,thesetypesof messageswouldincludeallthosethatarespreadinapublicandmassivecontextandthat moredirectlyandexplicitlyinciteviolence,intimidation,hostility,ordiscriminationagainst avulnerablegrouporanindividualbelongingtoavulnerablegroup—inthecaseofracist and/orxenophobichate,theywouldbemigrants,refugees,asylumseekers,andallkinds ofstigmatizedraces,ethnicities,andnationalities.Withthesepremises,thepresentwork coversallthetypifiedlevelsofhate,tryingtoextendhatespeechdetectionasmuchas possible,consideringthatthemostexplicithate(whichcouldbeconsideredacrime)is considerablyreducedinEuropeancontexts.However,itisexpectedthatinthetraining processandingeneratingthemachinelearningmodels,thefinalprototypewillberefined, anddetectionwillfinallybelimitedtothemostexplicitlevelsofhate.
3.DetectingRacistandXenophobicHateSpeechOnline
Inrecentyears,manyauthorshavestudiedhatespeechonlinefromverydifferent perspectives.ChettyandAlathur[17]analyseditfromthejurisprudentialbasis,concluding thatappropriatepoliticalmeasuresaswellastheactionsofsocialplatformsareessential toeffectivelycounteracthatespeech.Otherauthors,suchasElSheriefetal.[18],analysed itusingadata-basedlinguisticandpsycholinguisticperspective,offeringaframework ofunderstandingfromwhichtoidentifythehatethatisspreadonsocialmedia.Witha moreautomatedandmassivedetectionapproach,Mondal,Silva,andBenevenuto[19] proposedasystemformeasuringandmonitoringhatespeechpropagatedonthesocial networksTwitterandWhisperbasedonspecifickeywordsandexpressions,focusingonthe recognitionofthemaintargetstowhichhateisdirectedmassively.Fortheirpart,Malmasi andZampieri[20]aswellasSalminenetal.[21],aresomeofthefewauthorswhoproposed
methodstoautomaticallydetecthatespreadonsocialmediabasedonNLPandsupervised classificationtechniques.
However,alltheseworkshavesomethingincommon:theyalldealwithhatespeech fromagenericandinternationalpointofview;thatis,tryingtoidentifyhatespeechspread justinEnglish,motivatedbyallkindsofdiscriminatoryreasons,aimedtowardsalltypes ofvulnerableaudiences,andatanytimeandcontext,isanapproachthatistooambitious andcouldposeaproblemofinternalvalidity,especiallyinlarge-scalestrategies.Even theprototyperecentlydevelopedbySalminenetal.[21],oneofthemostinnovativeand advancedprototypesusingdeeplearningandincludingdetectioninvariousonlinesources, isbasedonthissametypeofapproach.Tryingtodetectonlinehateinageneralwaycanbe reductionistbyobviatingthecomplexityofhowhatespeechisspread,tryingtocoverthem allinasingleclassifiertrainedwithgeneralexamples.Thiscouldbealimitationbecause theresultingmodelsmaynotbeaseffective,reliable,and,paradoxically,generalizableas thosethataretrainedwithrealexamplesofaspecificcontext,aspecifictypeofhate,anda specificdiscriminatorycategory,separatinganddifferentiatingconcepts,characteristics, andlinguisticnuances.
Inthissense,itshouldbenotedthatontheinternationalscenetherearealreadysome examplesofstrategiesandtoolsfordetectingcyberhatethattakeintoaccountthedifferent levelsofhatespeech,aswellassomeofthedifferentcategoriesofprejudicethatcan motivateitorthedifferentvulnerablegroupswhomaybevictims.Wecanhighlightworks suchastheonedevelopedbyDavidsonetal.[22],whichdifferentiatesbetweenmessages thatexpressexplicithateandmessagesthatarejustoffensive,ortheonedevelopedby Badjatiyaetal.[23],whichaimstospecificallyidentifymessageswithracistorsexistcontent andalsousesdeepmodelling.However,mostofthecitedstudiesthatofferautomatichate speechdetectionmethodsbasedonmachinelearninghaveanotherlimitationincommon: theydonotuseadhocgeneratedtrainingcorpus.Mostoftheprototypesdevelopedsofar basetheirdetectiononpreviouslydevelopedlexicondictionaries,or,inthecaseofusinga corpusofexamplestotraintheclassificationalgorithms,theyusealreadyavailabledatasets developedinpreviousworks,suchastheprototypedevelopedbySalminenetal.[21]. Thisapproachalsoinfluencestheinternalvalidityoftheprototypeanditsfinalreliability. IntheSpanishcontext,oneofthefewstudiesthatattemptedtoaddressthedetectionof onlinehatespeechinSpanishistheonedevelopedbyPereiraKohatsuetal.[24].This prototypepresentsthesamelimitationsasmostofthosedevelopedinternationallysince italsoaddresseshatespeechinagenericwaywithoutdistinguishingaudiencesortypes. Inaddition,althoughPereiraKohatsudiddevelopanadhoctrainingcorpustogenerate predictivemodels,thiscorpuswasgeneratedbyasinglecoder,whichalsoposesaninternal validityproblemduetoitspotentialsubjectivity.Similarly,therearerecentprojectsthat havecreatedcorporaofhatefulspeechintheGreek[25,26]andItalianlanguages[27]for thetrainingofhatespeechdetectionmodels.
Fromamoretechnicalstandpoint,recurrentneuralnetworks(RNN)havebecome apopularchoiceforhate-speechdetectionandclassificationinshortmicro-blogging texts[28,29].Duwairi,Hayajneh,andQuwaider[30]investigatedtheabilityofconvolutionalneuralnetworks(CNN),CNN-LSTM(longshort-termmemory),andbidirectional LSTM-CNNmodelstodetecthatefulcontentfromsocialmediaintheArabiclanguage, withthelasttwoarchitecturescombiningCNNandRNNachievingthebestscores.Inthe workofAl-HassanandAl-Dossari[31],asupport-vectormachine(SVM)classifieriscomparedagainstLTSM,CNNwithLTSM,agatedrecurrentunit(GRU),andCNNwithGRU models.Alldeeplearningmodelsoutperformthebaseline,withthecombinedarchitecture achievingbetterperformanceinthiscaseaswell.Al-MakhadmehandTolba[32]propose anensembleofdeepclassifierscombinedwithanaturallanguageprocessing(NLP)-based semanticfeatureextractionlayer.PrasadandMishra[33]explorethefeasibilityofbidirectionalencoderrepresentationsfromtransformer(BERT)-basedmodelsformultilingual hateandabusivespeechdetection,oneofthemostadvancedtechniquesinthislinewhich hasalsobeentestedinthiswork.In[34],transferlearningisinvestigated,introducing
theBERT-basedtransformer,AraBERT,thatshowsanimprovedperformanceinAlgerian dialectalArabic.In[35],multitasklearning(MTL)isproposedfortheadaptationofa pretrainedhatespeechdetectionmodelintheArabiclanguageincross-corporatasks. Hatespeechdetectionmodelsareadaptedtothetargetdomain,andtheirperformance deterioratessignificantlywhenappliedindifferentdomains[36].Thisisalsoshownin theworkofBashar,Nayak,Luong,andBalasubramaniam[37],whotrainedmodelsfor hatespeechdetectioninthecontextoftheCOVID-19pandemic.Theimportanceofthe datasetsizeandqualityishighlightedalsobyKovács,Alonso,andSaini[38]inbothclassic machinelearninganddeeplearningapproaches.Textpreprocessingcanalsosignificantly improveperformance[39].
Consideringthesepremises,thegeneralobjectiveofthepresentworkistodevelop andvalidateamoreadvancedcomputationalstrategythatallowsforthedetectionof hatespeechonlinebasedonracismorxenophobiafollowingthelinesofresearchthatthe authorshavealreadybeendevelopingspecificallyintheSpanishcontexts,whichcould beconsideredaspilotstudiesonwhichthisprojectisbased[10,11,40,41].Inmostofthese studies,theauthorstreatracismandxenophobiaasasinglecategoryinthesamewaythat theMinistryoftheInteriorofSpaindoeswhenitrecordshatecrimes.Thisisbasedonthe factthatbothtypesofdiscriminationareparallelandpresentdifficultiestodifferentiate. AccordingtoauthorssuchasDíezNicolás[42]orCortina[43],onmanyoccasions,even withhelpofmeasurementtools,itistoodifficulttodistinguishbetweenoneandthe othertypeofprejudiceasthemainreasonforrejectionandhate,sinceinmostcases,the categoriesareconcatenated,intertwined,andoneisintrinsicallylinkedtotheother.For thisreason,theyareusuallystudiedtogether.
Inamoreparticularway,thepresentworkaimstosolveandovercomethelimitations ofpreviouslydevelopedprototypesbasedonaseriesofdifferentiatingelements.On theonehand,weexclusivelyfocusondetectinghatespeechmotivatedbyracismand xenophobia,whichallowsfortheelaborationofmorespecific,complete,andprecise corporatogeneratemorereliablepredictivemodels.Inthesameway,wegenerateour owndatasetsofrealtweets,atfirst,onlyinSpanish,butlater,alsoinGreekandItalian, totrainthepredictivemodels.Inthissense,sincethecreationofthesecorporarequires themanualannotationofpreviouslydownloadedandfilteredmessagesfromtheTwitter APIs,weposethefollowingresearchquestion:Whatfrequencyandpercentageofhate tweetsduetoracism/xenophobiaaredetectedthroughmanualannotationinasampleof previouslyfilteredtweetsaboutmigration?(RQ1).
Ontheotherhand,anotherinnovativeelementthatthisworkpresentsistheuseof deeplearninginthegenerationofpredictivemodelsthatwillallowfortheclassificationof hateinTwittermessagesautomaticallyandonalargescale.Specifically,recurrentneural networkswillbeused(andanadhocmodelandaBERT-basedmodel),analgorithmthat, apriori,shouldpresentsignificantadvantagesovertraditionalclassificationalgorithms, offeringbetteraperformance,especiallywhenappliedtotextclassifications,asisthe case.However,thereisnotenoughempiricalknowledgetoaffirmthatdeepmodelling willofferahigherreliabilitythanshallowalgorithms.Forthisreason,wealsoposethe followingquestions:Whichmachinelearningalgorithmpresentsthebestperformance whengeneratingapredictivemodelcapableofdetectinghatespeechspreadonTwitterin Spanish,basedonracist/xenophobicreasons(RQ2)?Doesdeepmodellingperformbetter thanshallowmodellingforgeneratingaprototypecapableofdetectingracist/xenophobic hatespeechonTwitterinSpanish(RQ2A)?
Inaddition,thisworkincludesanexternalvalidationphaseasanotherinnovative elementinwhichthefirstdevelopedclassifieristestedwithanewsampleoftweets.This stagewillcheck,beyondtheinternalevaluationoftheprototype,howreliablethemodel withthebestevaluationmetricsiswhenitcomestodetectingnewmessagesinSpanish aboutmigrantsandrefugeespostedonTwitter.Moreover,regardingthisstageofvalidation, weposethefollowingresearchquestion:Willthebestperformingalgorithmreliablydetect hatespeechinanewsampleoftweetsaboutmigrationinSpanish(RQ3)?
Finally,theresearchalsoincludestheevaluationofthedetectorinotherlanguages andothersourcesaswell,notonlyTwitter.Specifically,themodelswiththehighest scorevalidatedinSpanishwillbetrainedinadditionallanguagesusingthesamemachine learningarchitecture,thatis,messagesinGreekandItalianpromotinghatespeechabout migrantsandrefugeesfoundonline.Therefore,onemoreresearchquestioncanbeposed: CanthebestperformingmachinelearningmodelsinSpanishberetrainedandappliedto otherlanguagesaswell,keepingthesamelevelofperformance(RQ4)?
4.Method
Asindicated,thedetectorofracistandxenophobiconlinehatespeechhasbeen developedfollowingalarge-scaledetectionstrategybasedontheintensivecomputationof dataundertheSupercomputingCentreofCastillayLeón,ScayleusingNLPandmachine learning.Forthis,themethodologicalworkwasdevelopedover4stages:theinitial explorationandtheoreticalapproach,thegenerationofthedatasets,thegenerationof thepredictivemodels,theexternalvalidationoftheprototype,andtheadaptationofthis prototypetoGreekandItaliananditsevaluationonothersources.
4.1.TheoreticalPhase
Inthisphase,wecarriedoutanin-depthqualitativeexplorationofhatespeechthat spreadsonsocialmediasuchasTwitterand,specifically,thatwhichismotivatedbyracist and/orxenophobicreasons.Aliteraturereviewrelatedtothisfieldofstudywasalso carriedout,whichservedasatheoreticalapproach.Inaddition,weidentifiedprofiles andhashtagsonTwitterthroughwhichagreaternumberofmessagescontainingracist andxenophobichatearepublished.ExploringthesepotentialsourcesofhateonTwitter helpedustobetterunderstandandnarrowdownthedifferentwaysinwhichracistand xenophobichateisexpressed,aswellasthedifferentcontextsinwhichitspreads,the mostcommonvictims,andthemostcommonlyusedtermsandexpressions.This,inturn, helpedustosubsequentlygeneratethelinguisticfiltersthatwouldallowustodownload thefirstsampleofpotentialracisthatetweetsformanualclassificationinordertogenerate theadhocdataset.
4.2.DatasetGenerationPhase
Aftertheexploratoryphase,thedatasethadtobecreatedfromrealandvalidated examplesofshortmessagescontainingthetypeofhatetodetect.Theobjectivewasthat thedevelopeddatasetcouldbeusedasacorpustotrainthehatespeechdetectors.The generationofthedatasetwascarriedoutinaseriesofsub-phasesthatareexplainedbelow.
4.2.1.DefinitionandTypologyofHateSpeechtoDetect
Firstly,criteriawereestablishedtodefinethetypeofspeechtobedetectedtogenerate acustomizeddataset.Inaccordancewiththepossibilitiesthathadbeenidentifiedinthe previousqualitativeexplorationandtakingintoaccountboththedefinitionsprovided bythedifferentauthorsandinstitutionsandtheEuropeanlegalframeworkitself,the definitionofhatespeechwasbroadened,encompassingthedifferentmeaningsandtypes offeredbyacademia,publicinstitutions,andtheSpanishpenalcode,aswellasthethree levelsofonlinehateprovidedbyMiró Llinares[16].Thus,alltypesofhatefulspeeches wereincludedforthegenerationofthedataset,fromthemostexplicitandviolenttothe mostsubtle,sinceinthepreviousphase,asmallminorityofdirectlyracist/xenophobic hatehadbeendetected,evenintheprofilesmostpolarized.Sincetheintentionwasto beabletodetectasmanymessagesaspossiblewithracistand/orxenophobiccontent,it wasnecessarytocovermoretypesofhatemessages,includingthemostimplicitones.In addition,inthevalidationprocessofthemanualclassification,whichwouldbecarriedout followingthebasisofcontentanalysis,andinthesubsequenttrainingofthemodels,itwas expectedthattheresultswouldberefined,leavingonlytheclearerexamplesandfiltering andrejectingthemostdoubtfulorambiguousfornothavingintercoderagreement.For
thisreason,itwasalsointerestingtocoverthewidestpossiblerangeoftypesofhate.On theotherhand,whatwouldbeconsideredhatespeechduetoracistand/orxenophobic discriminationwasalsodefined,compilingallthederogatoryterms,expressions,and targetscollectedintheexploratoryphase.
4.2.2.ElaborationofDictionaryFiltersandDownloadingtheFirstSampleofTweets
Subsequently,adictionaryoftermsandcombinationsofwordswerecreatedtoserve asafilterforaninitialdownloadofpotentialtweetswithracistand/orxenophobichate. Todothis,westartedfromthequalitativeexplorationofTwitteraccounts,profiles,and hashtagsthroughwhichagreaternumberofracistandxenophobicmessagesarespread inSpain.Thus,Tweetswerelocatedusingkeywordsidentifiedintheexploratoryphase inwhichpotentialvictimsofthiskindofhatewerementioned.Theyaremainlyforced migrants,refugees,andasylumseekers,butalsoregularimmigrantsandalltypesofnonwesternethnicitiesandforeigncultures,racializedpeople,sub-SaharanAfricans,gypsies, Latinos,Asians,Muslims,etc.
Secondly,basedonthesefirstexamplesofmessageswithhatespeechextractedfrom potentialhateperpetratoraccountsonTwitter,wemadethefinalselectionofthesearch words[44]tocreatethefinalfilterdictionarythatwouldbeusedforthedownload.Specifically,alistofwords,roots,orwordcombinationsthatcouldberepresentativeorindicative ofracistand/orxenophobichatewasdrawnup.Thisfilterdictionarywasdevelopedad hocwiththeaimofaccessingtweetsmostlikelytocontainthetypeofhatesought,thus optimizingthetagging,streamlining,andoptimizingofthedatasetcreationprocess.In thisway,thefilteredtweetsweredownloaded,whichwouldlaterbeclassifiedmanuallyto generatethetrainingcorpus.
WefinallycarriedoutthedownloadusingthedictionarygeneratedbetweenOctober andDecember2019.Althoughwedownloadedalargernumberoftweets,wefinally collectedasampleof24,000messagesforlatermanualclassification.
4.2.3.ManualPairClassificationandClean-UpoftheFinalDataset
Afterdownloading,weproceededtomanuallyclassifythepotentialanti-immigration hatetweets,forwhichtheDoccanoplatformwasused.Alltweetswereclassifiedbytwo binarytrainedjudgesashateandnon-hatemessages.Simultaneously,thetweetsthat werenotinterestingtoincludeinthedatasetwerediscarded,suchasthosefromother contexts,forexample.Thedatasetwasgeneratedonlywiththemessagesinwhichthere wasagreementbetweenbothcoders,discardingthosewithoutagreement.Withthisstep, weintendedtoensurethereliabilityandqualityoftheresultingdataset,thusovercoming thelimitationsofsomepreviouslydevelopedprototypes,e.g.,[24].Aftercompilingand cleaningthefinaldataset,itwasmadeupofatotalof3751racist/xenophobichatetweets (15.6%)and7892non-racist/xenophobichatetweets(32.9%).
4.3.GenerationofPredictiveModelsPhase
Inafinalphase,weusedthedatasetdevelopedtogeneratetheclassifiersthatwould laterallowustoidentifytheanti-immigrationhatemessagesspreadonTwitterinSpanish. Specifically,wegeneratedatotalofninepredictivemodels,sixofthemusingtraditional algorithms,anothermodelfromthevotesofthoseshallowmodels,andtwofinalmodels usingdeeplearning,specificallyrecurrentneuralnetworksandtransformers.
4.3.1.ShallowModelling
Forthedevelopmentoftheshallowmodels,thescikit-learnlibrariesandtheNatural LanguageToolkit(NLTK)wereused.Specifically,6modelsweregeneratedwiththefollowingconventionalalgorithms:originalNaïveBayes,NaïveBayesformultinomialmodels, NaïveBayesforBernoulli’smultivariatemodels,logisticregression,linearclassifierswith stochasticgradientdescenttraining,andasupportvectorclassifier.Inallofthem,weused thedefaultparametersettingsfromthescikit-learnlibrary[45]andbag-of-wordsasthe
textrepresentation.Inaddition,thedatasetwasrandomlydividedintotwosubsets,one with70%ofthemessagesfortraining,andanotherwith30%fortestingthemodels.After trainingtheshallowmodels,wefinallygeneratedafinalsummaryclassifierwhichbased itspredictiononthevotesoftheprevious6classifiers.Inthiscase,aconfidencethreshold of80%wasincludedsothesummarydetectorwouldchoosethecategorypredictedbyat least5ofthe6shallowclassifiers.
4.3.2.DeepModelling
Finally,adeeplearningarchitecturewasusedtogeneratethefinalprototype.Specifically,anadhocrecurrentneuralnetworkwasgeneratedusingembeddingsasthetext representation.Todothis,weusedtheKeraslibrarywithTensorFlowasthebackendto createasequentialmodelwithfourlayers.Theinputlayerwasusedtocreatetheembeddings,whichweretrainedusingthe10,000mostcommonwordsofthecreatedvocabulary plus1000out-of-vocabularybuckets,assuggestedbyGéron[46].Thus,theembedding matrixincludedonerowforeachofthese11,000wordsandonecolumnforeachofthe 6embeddingdimensions.Thesecondandthethirdwerehiddenlayersthatconsistedof GRUs(asimplifiedversionoftheconventionalLSTMcells)with128neuronseach[47]. Finally,theoutputlayerwasadenselayerwithoneneuronandusedthesigmoidactivation toestimatetheprobabilitythataparticularmessagecontainedracist/xenophobichate.We usedstandardlosswithbinarycrossentropyandanAdamoptimizertocompilethismodel. Subsequently,weimplementedthetrainingcorpususing10epochs,andweusedthetest setforvalidation(30steps).
Inaddition,wedevelopedafinaldeeplearningclassifierusingbidirectionalencoder representationsfromtransformers(BERT)[48],apre-trainedlargelanguagemodelthat uses177,854,978parametersandthatwasfine-tunedwithourannotatedhatefulandnonhatefulmessages.ThismodelwasgeneratedusinganAdamoptimizerwithlearning rate=3 × 10 5 andepsilon=1 × 10 8,sparsecategoricalcrossentropyforloss,and 3epochs.ThenumberofparametersofbothmodelscanbeconsultedinTable 1
Table1. Deeplearningalgorithms’complexityintermsofmodels’numberofparameters.
4.4.ExternalValidationPhase
Finally,oncethepredictivemodelshadbeengeneratedandevaluated,theclassifier withthebestperformancewasvalidatedwithnewsamples.Theobjectiveofthisstage wastotesthowaccurateandreliabletheclassifierwiththebestevaluationmetricsiswhen puttingitintopracticewithnewdata.Thisnewdatasetcontained10,285tweetsretrievedin NovemberandDecember2020.Themessagesweremanuallyclassifiedbytwonewhuman coders.Inthiscase,thesamplewascarefullyreviewedbeforethemanualannotation, eliminatingbeforestartingtotagallthetweetsthatwewantedtodiscardbecausethey camefromothercontextsorbelongedtoothercategoriesofprejudice,forexample.Thus, aftertheclassificationprocess,onlythetweetsthatdidnothaveanagreementwere rejected,withwhichitwaspossibletoconsiderablyincreasethevalidtweetswithinterjudgeagreement.Attheendoftheclassificationprocess,theintercoderreliabilitywas checkedagain,seekingfullagreement.Thus,thismanualclassificationresultedin83%of thetweetsannotatedwithagreement(n=8588),ofwhich2781weremessagesofracist and/orxenophobichate(27.04%ofthetotal,32.38%ofthosewithagreementtweets),and 5807weremessagesthatdidnotcontainracistand/orxenophobichate(56.46%ofthe total,67.62%ofthosewithagreement).Atotalof16.5%ofthesamplewasrejectedfor nothavinginter-judgeagreement(n=1697).Subsequently,theagreementofthemanual classificationwiththepredictionsofthedetectorwiththebestperformancewaschecked, andnewevaluationmetricswereextractedfromthedetectorwiththatnewsampletoknow
towhatextentithadcoincidedwiththehumanclassifiers,andthuscheckhowaccurate andreliablehadbeenthedetectionoftheclassifierwiththenewtweets.
4.5.EvaluationofthePrototypeonMultisourceContentandinOtherLanguages
Animportantobjectiveofthisresearchwastocreateadetectorthatcouldbecapable ofdetectinghatespeechinothersimilarcontextsandlanguages.Theabilityofthemodels withthebestperformancestoisolatehatespeechcomingfrommultipleonlinesources hadtobeinvestigated.SourcesincludeTwitter,YouTube,Facebook,webarticles,and commentsfromdigitalmedia,associationwebsites,andblogsinwhichthetypeofhate speechanalysedcouldbepotentiallyspread.Inadditiontothis,itisveryimportantfor theeffectivenessoftheproposedapproachtobevalidatedinotherlanguagesaswellby simplyretrainingthemodelinthenewtargetlanguages.Forevaluatingthesedesirable characteristics,wemadeuseofthePHARMdatasets[49].ThePreventingHateAgainst RefugeesandMigrants(PHARM)projectconcernsamulti-sourceplatformfortheanalysis ofunstructurednewsandsocialmediamessages.Inadditiontoawebinterfaceforscraping andanalysinghatespeech,PHARMoffersamultilingualdatasetcontainingmultisource racist/xenophobichatespeechrecords.ThesourcesincludewebsitesinGreek,Italian,and Spanish,aswellasTwitter,YouTube,andFacebook,andconcernnewsarticles,comments, tweets,Facebookposts,andYouTubecomments.Currently(September2022),thePHARM datasethasabout35krecords.Thisdatasethasbeenannotatedbyhumancoders,while theresultshavebeencheckedforacceptableinter-coderreliabilityfollowingthestrategy previouslydevelopedintheSpanishprototype.InadditiontothePHARMrepository,the finaldatasetswereaugmentedwithadditionaldataprovidedbythePHARMdevelopment team.Table 2 depictsthebasicdescriptivecharacteristicsoftheformeddatasets.
Table2. DatasetdetailsforthesupplementaryevaluationinSpanish,Greek,andItalian.
Aswillbefurtheranalysedinthefollowingsection,themostpromisingclassifiers fortheprototypehatespeechdetectorprovedtobethedeeplearningmodels.Therefore, thisarchitecturewasadoptedforthisexperimentalscenarioaswell,aftermakingsome slightmodifications.Themostnotablemodificationwastheadditionofinstanceweighting inthetrainingphase.Duetotheimbalancebetweenhatefulandnon-hatefulrecords,the initialclassifiertendedtodevelopabiastowardstheclassthatwasoverrepresented,the non-hatefulclass.Therestoftheparametersremainedthesame.Thenumberofunitsfor eachGRUlayerwasreducedto64,asthissetupofferedthesameperformancewithless computationalcost.
5.Results
Inthefirstplace,beforegivingwaytoansweringtheresearchquestionsraised,wewill exploretheresultsofthemanualannotationcarriedouttocreatethedatasets.Inthissense, thefirstthingtopointoutisthehighpercentageoftweetsthatwerefinallyrejected,which isgreaterthan50%.Thisindicatestheinitialcomplexitytofacethetaskofidentifyingthis typeofhatespeechreliablyandinaparticularlinguisticcontext.Ontheotherhand,itis observedthatthepercentageofhatetweetswithfullagreementisconsiderablyreduced despitehavingclassifiedpreviouslyfilteredmessages.Specifically,andrespondingtoRQ1, 15.6%ofracistand/orxenophobichatetweets(N=3751)werevalidated,comparedto 32.9%ofmessageslabelledasnon-racist/xenophobichate(N=7892).Thesepercentages showthatlinguisticfilterdictionaries,nomatterhowcompleteandcomplex,cannotbe
aneffectivemethodtoidentifythesetypesofhatemessages,somethingthatwasalready assumed.However,theyservedtooptimizetheprocess,sincewithoutthesefilters,the procedureoffindinghatespeechexamplesthroughoutTwitterwouldhavebeenendless.In addition,consideringthatthedatasetscanalwaysbeenrichedandupdatedwithnewdata, themostimportantthing,especiallyinthisinitialprototype,wastoestablishastrategyto generatecorporaofqualityratherthanquantity.
Regardingtheevaluationofthegeneratedmodels,threemetricswereused:accuracy, F1-score,andAUC-ROC.TheAccuracywascalculatedbyusingthetotalsumofcorrect predictionsacrossallclasses;theF1-scorewascalculatedasthearithmeticmeanoftheperclassF1-scores,whicharetheharmonicmeansoftheprecisionandrecallmetrics.Finally, theAUC-ROCunveilstheefficiencyoftheclassifiersatallthresholds.Therefore,both micro-andmacro-averagedmetricsarepresent(accuracyandF1-score,respectively),with thelatterbeinginsensitivetoapossibleimbalancebetweenclasses.Whenmacro-averaging, allclassesaretreatedasequal,unveilinglowscoresonclasseswithfewinstances.Ascan beseeninTable 3,classificationperformanceisatleastacceptableinmostcases,asscores higherthan0.75havebeenrecorded.ExceptforthesimpleNaïveBayesalgorithm,all modelsshowedsimilarperformances.Moreover,theaccuracyandAUC-ROCscoreswere higherfortheadhocRNNmodel,whichconfirmsthecomparativeadvantageofdeep learninginthistypeoftask.RespondingtoRQ2,wecanaffirmthatlogisticregressionand supportvectormachinesaretheshallowalgorithmsthatpresentthebestperformancein thiscase.RespondingtoRQ2A,wecanconfirmthatthedeeplearningapproachoffersthe highestperformance.TheresultsarealsovisualizedinFigure 1
Table3. Evaluationmetricsofthemodelsgeneratedwitheachofthealgorithms. ClassificationAlgorithmAccuracyF1-ScoreAUC
Figure1. Performanceratingsforalltestedalgorithms.
Next,theexternalvalidationphasewascarriedout.Atthisstage,theaimwasto assesshowcapablethedeeplearningclassifiergeneratedadhocisonnewdata,i.e.,new tweetsconcerningdifferenttemporalcontexts.Forthis,onlythetweetswithanagreement resultingfromthemanualclassificationofthenewsamplewereused(n=8588).Thus, afterrunningthemodelonthatsample,firstly,Krippendor’sAlphawasusedtocheck theintercoderreliabilitybetweenthemanuallycodedtweetswithagreementandthe predictionsofferedbytheprototype.Theresultofthisreliabilitypretestwas α =0.6, anacceptablefigure,butnottoohigh.Subsequently,theevaluationmetricsofthedeep modelwereextractedwhenbeingrunandtestedonthenewsample,withmorepromising results:Accuracy=0.85,F1-Score=0.74,andAUC-ROC=0.88.Takingthesemetricsinto accountandrespondingtoRQ3,wecanconfirmthattheclassificationprototypeshowsan acceptableperformancewhentestedwithnew,unseen,realdata.
Finally,theevaluationofthedeeplearningarchitectureonnewdatasetsandlanguages tookplace.Asindicated,atthisstage,notonlytheRNNmodeldevelopedadhocwas improvedandtested,butalsoanewdeeplearningmodelbasedonBERT(bidirectional encoderrepresentationsfromtransformers),amachinelearningtechniquebasedontransformerswhichissupposedlythemostadvancedlinguisticmodelfornaturallanguage processingandespeciallyforembeddingcontextualizedwords[48,50],wasimproved andtested.
Table 4 depictstheaccuracyandF1-scoremetricsforthetwodeepmodelsandthe Spanish,Greek,andItalianlanguages.Theresultsindicatethatthesemodelscanhavea goodperformanceindifferentlanguagesaswell,notonlyinSpanish,andinmultisource. BothmodelsachievehighaccuracyratingsconsistentlyintheSpanishlanguage(0.86,0.85, 0.87,and0.90inthefourtests,respectively)andshowsimilarperformancesfortheGreek andItalianlanguages,aswell.Itshouldbealsonotedthatthegapbetweenthemicro-and macro-averagedmetrics(accuracyandF1-score,respectively)becamesmaller,indicating thattheinstanceweightingtechniqueinthetrainingprocessledtoabetter-balanced classifierwithoutabiastowardstheclasswithmoreparadigms.Therefore,responding toRQ4,wecanconfirmthattheproposeddeeplearningarchitecturescanbeappliedto detectracistandxenophobichatespeechinotherlanguagesandotherplatformsaswell, retainingitsperformance.
Table4. EvaluationmetricsofthedeeplearningmodelstrainedonthePHARMdatasets.
6.Discussion
Thisworkhasgeneratedthefirstprototypescapableofdetectinganti-immigration onlinehatespeechautomaticallyandonalargescale.Theseclassifiersweretestedand validatedfirstlyformessagesspreadonTwitterinSpanish,andlaterimprovedandadapted fortheirapplicationonmoreonlinesources(YouTube,Facebook,andmediawebsites)and inmorelanguages(GreekandItalian).Forthis,wehavegeneratedadhocdatasetsthrough manualsortingtasks,andwehaveused,firstly,traditionalclassificationalgorithmsfor thegenerationoftheprimarymodels,andsecondly,twodifferentdeeplearningstrategies, aninnovationwithrespecttodetectors,developedbyotherauthors.Specifically,the
developmentofthedifferentclassifiershadbeenbasedonnaturallanguageprocessing andsupervisedmachinelearningtechniques.Regardingthemachinelearningalgorithms used,differentshallowanddeeparchitectureshavebeenputtothetest,fromthemost traditionalbasedonshallowmodelling,todeepmodelsbasedontransformers,comparing theirperformances.
Wehaveconfirmedthatdeepmodellingperformsconsiderablybetterthanshallow modellingfordetectinghatespeechdirectedtowardsmigrantsandrefugeesintweetsin Spanishsincethemodelstrainedwithneuralnetworksweretheonesthatbroadlypresented thebestevaluationmetrics,somethingthathadalreadybeenevidencedinnumerouspast studies[23,29,30].Thatiswhytheyweretheonesusedforthemodel’simprovementand adaptationtodetectanti-immigrationonlinehatespeechinnewlanguagesandsources, bothofferingacceptableperformancesinallnewcases.Atthispoint,itshouldalsobenoted thatthetransformer-basedmodelseemstoofferaslightlybetterperformanceingeneral termsthantheadhocdevelopedRNNmodel,somethingthatwasalsoexpectedsinceit iswhatthemostrecentstudiesshowed[48,50].Ourfindingscomplywiththoseofrecent studies,suchasin[51],wheremachinelearningmodels(supportvectormachinesand logisticregression),deeplearningmodels(LSTM,CNN,andBi-LSTM),andtransformerbasedlanguagemodels(multilingualBERT,XLM,andmonolingualSpanishBETO)are comparedforhatespeechdetectioninSpanish.TheBERT-basedmodelsoutperformedthe MLandDL,withtheBETOachievingthehigherF1-scoreof77.62%.
7.Conclusions
Inwayofconclusion,thisresearchworkprovesthatitisfeasibletogenerateautomatic detectorsofonlineracist/xenophobichatespeechusingmachinelearningwithsolid performances.Inaddition,specificvalidateddatasetshavebeengeneratedadhocforthe trainingofthepredictivemodels,somethingthatotherauthorshavenotconsideredso farandthatallowsforthemodelstoovercomethepossibleweaknessesderivedfromthe internalvalidityofthedetectorspreviouslydeveloped.Inthissense,wecanpointoutthat, althoughtheamountofhateandnohatemessagesaddedtothedatasetsaftercross-coding anddatacleaningmayseemlow,themostimportantthinginthisprocessistohavequality examplesoverquantity.Moreover,thisisbecause,althoughtheevaluationmetricscouldbe acceptable,iftheexamplesarenotcompletelyreliable,theinternalvalidityoftheprototype couldbecontaminatedwithfalsepositivesornegatives.Forthisreason,wealsowanted togofurtherandconductanexternalvalidationtochecktheperformanceofourprimary prototypemoreeffectivelywhenappliedtonewrealexamples.Thisvalidationwithnew datahasservedtoverifythatourdeepmodelisreliableandhasanacceptableperformance whenusedinpractice,withnewrealcases,andcomparedwithanewvalidatedmanual classification.Nevertheless,fromthebeginning,thefocuswasongeneratingaquality andreliabledatasetthatcanbeusedasatrainingcorpus,since,inaddition,thequantity canalwaysbeimprovedbyincludingnewvalidatedexamplesintothecorpus.Insum, wehaveresolvedthatlogisticregressionandsupportvectormachinesaretheshallow algorithmsthatofferedthebestperformanceforthistaskoutofthesixtested.However,as indicated,wehaveconfirmedthatdeeplearningperformsbetterthanshallowlearning fordetectingracistandxenophobiconlinehatespeech.Thetwotestedarchitectures,the adhocdevelopedRNNandtheonebasedontransformers,presentedconsiderablybetter performancesthanthetraditionalmodels,bothintheSpanishlanguageandonTwitter, aswellasinotherlanguagesandsources.Nevertheless,itshouldbehighlightedthatthe BERT-basedmodelseemstoofferaslightlybetterperformancethantheadhocRNNmodel, asexpected.
Insummary,itcanbeconcludedthattheresultsofthisstudyarerelevant,significant, andtheyrepresentanoveltyinthescientificliteratureonthestudyofnewcomputational methodsappliedtothesocialsciencesand,specifically,tothedetectionandmonitoringof onlinehatespeech.Thisissobecause,untilnow,anti-immigrationonlinehatedetectorshad notbeentestedinSpanish,Greek,andItalianandinmultisourceusingadhocdeveloped
datasetsanddeeparchitectures,includinganadhocRNNandaBERT-basedmodel.So, itmustbehighlightedthatthisstudyoffersacontributioninmethodologicalterms,of course,withthelarge-scaledetectionstrategy,withthegenerationoftheadhocdatasetsof validatedrealexamplesofracistandxenophobichatespeechinSpanish,Greek,andItalian retrieved,firstly,fromTwitter,butalsoandsecondarilyfromYouTube,Facebook,and potentiallyhate-spreadingwebsites,andlastly,withthemodelsdevelopedwithbothdeep learningtechniques.Inaddition,thisresearchprovidesatheoreticaladvanceinthestudy ofonlinehatespeechdirectedtowardsmigrantsandrefugees.Finally,apracticalandsocial contributionisalsopresentedsincethetechnologydevelopedherecanbeappliedindiverse publicandprivatespheres,beingabletobenefitfromprivatecompanies,researchgroups, nonprofitorganizations,aswellasgovernmentagencies,amongotherthings,todrawthe possiblesocialacceptanceorrejectionofmigrantsindifferentEuropeanregions,andthus aidinexecutingstrategiestoimprovelong-termintegrationinmigrationprocesses.Infact, theprototypesthathavebeenevaluatedarealreadybeingusedtodevelopnewprojects, suchastheonerecentlypublishedbyArcila-Calderónetal.[52]usingtheinfrastructure andthecomputationalstrategyvalidatedinthePHARMProjectthatallowsfortheretrieval ofgeolocatedtextmessagesfromonlinesourcesusingdifferentsearchqueriesandcriteria, anditsdirectandmassiveprocessingandclassificationthroughtheSupercomputingCentre ofCastillayLeón,Scayle,wherethemodelsareexecutedtolatergeneratedatasetswith themessagesfinallyclassifiedwithreliabilitythatcanbeconsultedandanalysed.
8.LimitationsandFutureLinesofResearch
Amajorlimitationoftheapproachcomesfromthefactthatcommentsandtweetsare treatedasstandalonetextswithouttakingintoconsiderationcontextinformation.Context informationmayrelyonnetworkanalysistodeterminethetopiconwhichacommentor replyisposteandtakesintoconsiderationpreviouscommentsandrepliesthatmaybe crucialtocompletethemeaningofthetextthatisanalysed.However,themetadatathat arecollectedalongwiththetextualinformationmaybeusefulforamorecomprehensive analysisinthefuture.Furthermore,thedetectorshouldbeusableforpeoplewhodo nothavecomputerskillsorarenotcapableofrunningormanipulatingascriptwithout complications.Withthispurpose,aGUIintegrationhasbeenpresentedintheworkof Vrysisetal.[49].ThisledtothecreationofthePHARMwebinterfacethatincorporatesthe modelsthatarepresentedinthecurrentpaper,providingavisualandfriendlyinterfacethat allowsfortheuseofthedetectorsbynonexperts,universalizingitsuseandapplications. Thiscanalsobroadentherangeofpracticalpossibilitiesaswellassocialbenefitssince thedetectorcouldbeimplementedbymoresocialactors.Thereisstrongevidencethat therobustnessandgeneralizationcapabilitiesofdeeplearningmodelsdependhighlyon thequalityandquantityofavailabledata.ThewidespreaduseofthePHARMinterface isexpectedtoleadtoanextensioninthedataset,sinceitmayallowtheretrainingof modelsinthefuturefollowingthepresentedmethodology.Finally,thefuturepossibility ofimplementinganearly-warningsystemisraised.Thiscouldwarnaboutincreasesin racist/xenophobichatespeechinageo-localizedwayandthuspreventandpredictpossible increasesinracist/xenophobichatecrimesincertainregions.Thiswarningsystemcould alsotakeadvantageoftheinterfacementionedabovetobevisualand,inthesameway, usablebyalltypesofusers.
AuthorContributions: Conceptualization,C.A.-C.andJ.J.A.;methodology,C.A.-C.,J.J.A.,L.V.and N.V.;validation,J.J.A.,L.V.andN.V.;formalanalysis,J.J.A.,L.V.andN.V.;investigation,J.J.A.,P.S.-H., M.O.A.,L.V.andN.V.;resources,C.A.-C.;writing—originaldraftpreparation,C.A.-C.andJ.J.A.; projectadministration,P.S.-H.;fundingacquisition,C.A.-C.Allauthorshavereadandagreedtothe publishedversionofthemanuscript.
Funding: ThisresearchwasfundedbytheEuropeanUnionthroughtheRights,Equality,and CitizenshipprogrammeREC-RRAC-RACI-AG-2019[GAn.875217].
InstitutionalReviewBoardStatement: Notapplicable.Inthisstudy,authorshaveonlyworked withpublicdataextractedfromtheTwitterAPIv2.Therefore,theauthorsconsiderthatthereisno ethicalconflictthatneedsapproval.
InformedConsentStatement: Notapplicable.Nopersonexternaltotheprojectparticipatedinthis study,sonoconsentwasnecessary.
DataAvailabilityStatement: Allthematerialusedinthisworkwillbeavailableontheproject website: https://pharmproject.usal.es/,accessedon8September2022.
Acknowledgments: AndreasVeglisandSergioSplendore.
ConflictsofInterest: Theauthorsdeclarenoconflictofinterestinthiswork.
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