IslamophobiaonTwitter:MarchtoJuly2016

CarlMillerJoshSmithJackDaleCentrefortheAnalysisofSocialMedia,Demos

RESULTSTheCentrefortheAnalysisofSocialMedia(CASM)atDemosisconductingcontinuousresearchonhateful,xenophobic,anti-disability,anti-Semiticandanti-IslamicideasandexpressionsonTwitter.Thisispartofabroadefforttounderstandthescale,scopeandnatureofusesofsocialmediathatarepossiblysociallyproblematicanddamaging.

ThisshortpaperdetailsrecentresultsfortheuseofTwittertoshareexpressionswhichareidentiEiedasIslamophobic,derogatoryandhateful.Itcoversabroadstretchoftime,fromthe22ndFebruary2016tothetimeofwriting,August4th,butfocusesespeciallyonactivityoverthemonthofJuly2016.ForadiscussionofhowtheseTweetswerecollectedandanalysed,seethemethodologysectionofthisreport.

July2016

OverJuly,weidentiEied215,247Tweets,sentinEnglishandfromaroundtheworld,ashighlylikelytobehateful,derogatory,andanti-Islamic. Onaverage,thisis289perhour,or6943perday.Thisisthe1

highestmonthlyaveragesincemeasurementsbeganattheendofFebruary.However,therateofanti-IslamicactivityonTwittersigniEicantlychangedoverthemonth.Twitteris,ingeneral,areal-time,reactiveandevent-speciEicplatform,andmostoftheanti-IslamicactivityidentiEiedwaslikewiselinkedtoaneventthathadrecentlyhappened.ThemostsigniEicantincreasewasintheimmediatewakeoftheterroristattackinNice,onJuly14th,withanotherappreciableincreaseintherateofanti-islamicexpressionsintheaftermathofthekillingofJacquesHamelinNormandy.TheEivemostsigniEicantspikesareanalysedingreaterdepth,below.Thisistotrytouncoverthetriggers,driversanddynamicsofanti-Islamichatredonline.

� Figure1–IslamophobicTweetscollectedduringJuly2016

Seemethodologyformoreinformationonhowthisresearchwasconducted.1

Spike1–July5th

WeidentiEied9,220IslamophobicTweetson5July.ItisdifEiculttoidentifyonesingulareventthattriggeredthisriseinIslamophobiclanguage.Onepossibleexplanationisthatthiswas4daysafterthe12-hoursiegebyISmilitantsinacaféinBangladesh.American,Italian,Indian,JapaneseandBangladeshivictimswereamongthe22peoplekilledinthisattack.Furthermore,thisdaymarkedtheendofRamadanbeforethestartofEidal-Fitr,perhapsintensifyingaglobalfocusonIslam.Examplesoftweetsfromthisdayinclude:“NobodycanstopMuslimscommittingjihadattacksanymorethantheycanstopBuddhistsmeditatingorMormonsknockingonpeople'sdoors”;“MoroccodeletesawholesectionoftheKoranfromschoolcurriculumasit’sfullofjihadincitementandviolence.TheReligionofpeace”;and“Ifuckinghatepakis.”

Spike2–July8th

11,320Islamophobictweetsweresenton8July.Again,itisdifEiculttoattributethisrisetoonespeciEicevent,thoughthiswasthedayaftertheshootingsinDallas,U.S.,inwhichMicahXavierJohnsonshotandkilled5policeofEicers,wounded7othersandwounded2civilians. ManytweetsappearedtotryandlinkthiseventtoIslam.Anexampleincludes:“ObamaisadamnRagheadexplainsalot”. Spike3–July15th

21,190tweetssenton15JulywereidentiEiedasIslamophobic.ThiswasthedayaftertheattackinNice,inwhichanarmedISmilitantdroveatruckthroughcrowdsofpeoplecelebratingBastilleDay;84peoplewerekilledandmanymoreinjured. Tweetssentonthisdayfocusedontheattack:“Sorrytohearaboutfrance-Thesemuzziesjustdontquit”;“and“Stopsaying'themajorityarepeace-loving'.Untilthemajoritydenounceeveryjihadi&turnthemin,wearesaferbelievingtheevidence”. Spike4–July17th10,610tweetsweresenton17July.ThiswasthedayafteranattemptedmilitarycoupinTurkeyfailed;afactionwithintheTurkishArmedForcesorganisedthemselvesunderthe‘PeaceatHomeCouncil’.TheCouncilcitedtheerosionofsecularismasonereasonbehindtheattemptedcoup.Assuch,sometweetscommentedonthis:“That'stheendofTurkey.AnothercountryruinedbyIslamanditsterroristculture.SameshithappenedtoPersia.NowitsIslamicRepublic”.Othertweetsweremoregeneral:“France'sIslamicpopulationisat9.6%.10%isusuallywhenJihadbegins.They'restartingearlybecausetheFrencharesoweak”;“ALLthisbecauseofthemuzzieintheWhiteHouse”. Spike5–July26th8,950tweetsweresenton26July.ThiswasthedayoftheNormandychurchattack,inwhichISmilitantskilledFatherJacquesHamel,andseriouslywoundedanother. Tweetssentonthisdaycommentonthisattack:“Normandyisreason1488thatyoushouldelectMarineLePen!>closeborders>deportmurderousIslam&Muzzies>deporttheriotingnegroes”;“Sosomesleazyscumcommittedjihadinthenameof#IslamthistimeinNormandybutheylet'skeeptellingMuslimswelovethem”;and“Priestkilledin#NormandytodaybyaRadicalIslamicTerroristyetHillarysaysthatIslamispeaceful!1274attacksthisyear=peaceful?Ok.”

GEOGRAPHYTheanalysiswasconductedonlyforanti-IslamicexpressionsintheEnglishlanguage.Consequently,thevastmajorityoftheTweetsthatcouldbelocatedtoEuropecamefromtheUnitedKingdom.However,asthemapsbelowillustrate,anti-IslamicTweetsweresentfromeveryEuropeanUnionmemberstate,withotherconcentrationsintheNetherlands,FranceandGermany.

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Geo-locatedanti-IslamicTweetsoverJuly2016

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Countryconcentrationsofgeo-locatedanti-IslamicTweets

Geographicalconcentrationsofanti-islamicexpressionsonTwitteralsovariedacrossthemonth.ThetweetssentinreactiontotheNiceattack(spike3,above),asshownbelow,hadhigherconcentrationsofanti-IslamicexpressionsinHollandandFrancethanthereactiontotheTurkishcoupattempt(spike4).

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Geo-locatedanti-IslamicTweetsfor15thJuly

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Countryconcentrationsofgeo-locatedanti-IslamicTweetsforJuly15th

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Geo-locatedanti-IslamicTweetsfor17thJuly

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Countryconcentrationsofgeo-locatedanti-IslamicTweetsfor17thJuly

VOLUME

FromthebeginningofMarchtotheendofJuly2016,anaverageof4972TweetswereidentiEiedaday. 2TheratedroppedsharplybetweenMarch(themonthoftheterroristattacksinBrussels,andthesubjectofearlierDemosworkinthisarea)andApril,andhassincethenbeenincreasingmonth-on-month. 3

July,withanaverageof6,943anti-IslamicTweetsperday,or215,247acrossthemonth,hasthehighestrateofanti-IslamicTweetsofanymonthanalysed,andconsiderablyabovethemonthlyaverage(168,595)duringthistime.

Islamophobictweetssenteachmonth,fromMaytoJuly

Month IslamophobicTweetssentperday(average)

%increase/decreaseonpreviousmonth

March 5,024 N/A

April 2,512 -50%

May 3,985 +37%

June 5,480 +27%

July 6,943 +21%

N.B.Duringtheinitialpilotofthissystem,only19dayswereanalysedinMarchandApril,andaverageswerebasedonthe2

daysmeasured.

ForDemos’analysisoftheonlinereactiontotheBrusselsattacks,seehttp://www.demos.co.uk/project/hate-speech-after-3

brexit/

Overthemostrecentperiod,thehighestvolumeofIslamophobicTweetsweresentfrom11thto17thJuly(64,143),whenboththeNiceattacksandtheattemptedmilitarycoupinTurkeyoccurred.ThisweekwasoneofthebiggestspikesinIslamophobiathroughoutthedataset,secondonlyto13thto19thJune.

Islamophobictweetssenteachweek,from25thAprilto31stJuly

ISLAMOPHOBICTWEETSFROMTHEUK:MAYTOJULY

ThereportalsoanalysedIslamophobicTweetsthatwerelikelysentfromtheUK.ItisimportanttonotethatnotallTweetscanbegeographicallyplaced.AsmallamountoftweetshavedeEinitiveinformationaboutwheretheyweresentfrom.Thesearegeo-tags:preciselongitudeandlatitudecoordinatesthatindicateverypreciselywherethetweetwasposted.Onlyuserswhoproactivelyturnonthegeo-locationfacilityontheirsmartphonewillincludethisinformation.Alargernumberoftweetscanbealgorithmicallylocatedbasedongeo-locationmeta-dataattachedtotheTweet.Theseinclude(inadditiontothelongitudinal-latitudinaldatacontainedabove),the‘locationEield’–whereusersreportwheretheyarefrom,andtimezone.OntestsofthismethodofgeolocatingTweets,ithasbeenfoundtobebetween80and90percentaccurateforthoseTweetsitcouldlocate,andbeabletolocatebetween40and70percentofTweets. 4

FromthebeginningofMarchtotheendofJuly2016,anaverageof367IslamophohbicTweetsfromtheUKwereidentiEiedaday.Unliketheglobaldata,therateofIslamophobicTweetsdecreasedslightlybetweenMayandJune.However,consistentwiththeglobalpicture,theratethenincreasedsharplybetweenJuneandJuly.

Month Islamophobictweetssentperday(average)

%increase/decreaseonpreviousmonth

Totalmonth

May 380 N/A 11,766

June 351 -8% 10,557

July 468 +33% 14,512

Formoreinformationonthis,seetheDemospaperTheRoadtoRepresentivity,page30.http://www.demos.co.uk/Eiles/4

Road_to_representivity_Einal.pdf?1441811336

July,withanaverageof468anti-Islamictweetsperday,or14,512acrossthemonth,hasthehighestrateofanti-Islamictweetsofanyofthethreemonths,andabovethemonthlyaverageof12,278forthethreemonths.

Islamophobictweetssenteachmonth,fromMaytoJuly

Overthemostrecentperiod(July),thehighestvolumeofIslamophobictweetsweresentfrom11thto17thJuly(3,958)whenboththeNiceattacksandtheattemptedcoupinTurkeyoccurred.ThisweekwasoneofbiggestspikesinIslamophobiathroughoutthedataset,secondonlyto2nd–8thMay.

Islamophobictweetssenteachweek,from25thAprilto31stJuly

ETHICS

AtDemoswebelieveitisimportantthattheprincipleofinternetfreedomshouldbemaintained;andthatitshouldbeaplacewherepeoplefeeltheycanspeaktheirmindopenlyandfreely.However,racist,xenophobic,Islamophobicandmisogynisticabusecancurtailfreedom,andthecapacitytospeakandactfreelyonline,asmuchasitcanbeanexpressionofit.Itisimportant,associetyconfrontsthewaysthatsocialmediaactsasanewplatformfortheexpressionanddisseminationoftheseofkindsofviews,tounderstandasbestaspossiblethescale,scope,natureandseverityofthesekindsofpractices:whentheyhappen,whotheyhappento,andwhy.Thisiswhatthisresearchhopestocontributeto.

CASMhasconductedextensiveworkontheethicsandpublicacceptabilityofsocialmediaresearch. Anethicalframeworkhasbeenappliedtothisproject,suchthat:5

• Theresearchonlyusespubliclyavailabledata,viewableandvisibletoanyTwitteruser;

• Theresearchconductedisaggregatedandanonymous:theresearchdoesnotidentifyanyspeciEicuserorusers,buttounderstandtheoverallscaleandnatureofIslamophobicabuseonTwitter;

• Wherequotationsareusedasexamplesandelaborations,theyhavebeenalteredtomaintaintheoverallmeaning,buttopreventtheretrospectiveidentiEicationofanyTwitteruseronthebasisofthequotation;

• Thereisnosuggestionofanyillegalityofanyofthecontentmeasured:thepurposeoftheresearchwasnottolookforcontentthatwasillegal,anditdoesnotsuggestthatthecontentthatwasfoundwasillegal.Thisresearchisnotseekingtoinformhowlawsshouldbeenforcedonsocialmedia.Thisresearch,andDemos’broaderresearchagenda,seeksinsteadtoinformthebroaderquestionofhowpeoplefromdifferentraces,religions,sexualitiesandgendersarespokenaboutonsocialmedia,andtheextentthatpeoplefromdifferentbackgroundsfaceabuseandhostility.

See,forinstance,Demos’recentpaperwithIpsosMORI#socialethics:AGuidetoEmbeddingEthicsinSocialMediaResearch5

,https://www.ipsos-mori.com/Assets/Docs/Publications/im-demos-social-ethics-in-social-media-research-summary.pdf

OVERALLMETHODOLOGYTwitterdataisoftenchallengingtoanalyse.Datadrawnfromsocialmediaareoftentoolargetofullyanalysemanually,andalsooftennotamenabletotheconventionalresearchmethodsofsocialscience.TheresearchteamusedatechnologyplatformcalledMethod52,developedbyCASMtechnologistsbasedattheTextAnalyticsGroupattheUniversityofSussex. Itisdesignedtoallownon-technical6

researcherstoanalyseverylargedatasetslikeTwitter.

DeiiningIslamophobia

ThispaperispredicatedonthetrainingofamachinetobeabletodistinguishbetweenanexpressionthatisIslamophobicandonethatisn’t.AnIslamophobicexpressionwasdeEinedastheillegitimateandprejudicialdislikeofMuslimsbecauseoftheirfaith.However,Islamophobiacantakeonaverylargenumberofdifferentforms,anditsidentiEication,especiallywithinTwitterresearch,wasoftenchallenging.Ultimately,thisresearchcomesdowntothejudgementoftheresearchersinvolved.FourmainqualitativecategoriesofIslamophobia,onthejudgementoftheresearchersconductingtheanalysis,wereidentiEied:

• ‘Islamistheenemy’:TheideathatitisafundamentalinjunctionofIslamforallofitsfollowerstobeengagedinaviolentstruggleagainstnon-MuslimsandtheWest;

• TheconElationofMuslimpopulationswithsexualviolenceandaproclivitytowardsrape;• Especiallyinthewakeofterroristattacks,theapportioningofblamefortheattacknotontheterroriststhemselves,oronIslamistmilitancy,butontheMuslimpopulationgenerally;

• Generalabuse,andthegeneraluseofanti-IslamicslursandderogatorydescriptionsofMuslims.

DataCollectionMethod52wasusedtodirectlycollectTweetsfromTwitter’sStreamandSearch‘ApplicationProgrammingInterfaces’(orAPIs).TheyallowallTweetstobecollectedthatcontainoneofanumberofspeciEiedkeywords.Thekeywordsusedinthevariouscollectionsusedinthisresearcharedetailedintheannex.

DataAnalysisMethod52allowsresearcherstotrainalgorithmstosplitapart(‘toclassify’)Tweetsintocategories,accordingtothemeaningoftheTweet,andonthebasisofthetexttheycontain.Todothis,itusesatechnologycallednaturallanguageprocessing.NaturallanguageprocessingisabranchofartiEicialintelligenceresearch,andcombinesapproachesdevelopedintheEieldsofcomputerscience,appliedmathematics,andlinguistics.Ananalyst‘marksup’whichcategoryheorsheconsidersatweettofallinto,andthis‘teaches’thealgorithmtospotpatternsinthelanguageuseassociatedwitheachcategorychosen.Thealgorithmlooksforstatisticalcorrelationsbetweenthelanguageusedandthecategoriesassignedtodeterminetheextenttowhichwordsandbigramsareindicativeofthepre-deEinedcategories.Detailsabouthowthesealgorithmswereused,andhowwelltheyworked,areprovidedbelow. 7

TheAccuracyofAlgorithmsTomeasuretheaccuracyofalgorithmsintothecategorieschosenbytheanalyst,weuseda‘goldstandard’approach.Foreach,around100TweetswererandomlyselectedfromtherelevantdatasettoformagoldstandardtestsetforeachclassiEier.TheseweremanuallycodedintothecategoriesdeEinedabove.TheseTweetswerethenremovedfromthemaindatasetandsowerenotusedtotraintheclassiEier.

AstheanalysttrainedtheclassiEier,thesoftwarereportedbackonhowaccuratetheclassiEierwasatcategorisingthegoldstandard,ascomparedtotheanalyst’sdecisions.Onthebasisofthiscomparison,classiEierperformancestatistics–‘recall’,‘precision’,and‘F-score’arecreatedandappraisedbya

This group is led by Professor David Weir and Dr Jeremy Reffin. More information is available about their work at: 6

http://users.sussex.ac.uk/~davidw/styled-3/

For a more detailed description of this methodology, see the Demos paper Vox Digitas, 7

humananalyst.EachmeasurestheabilityoftheclassiEiertomakethesamedecisionsasahumaninadifferentway:

Overallaccuracy:ThisrepresentsthepercentagelikelihoodofanyrandomlyselectedTweetwithinthedatasetbeingplacedintotheappropriatecategorybythealgorithm.Itisbasedonthreeothermeasures(below).

Recall:ThenumberofcorrectselectionsthattheclassiEiermakesasaproportionofthetotalcorrectselectionsitcouldhavemade.Iftherewere10relevantTweetsinadataset,andarelevancyclassiEiersuccessfullypicks8ofthem,ithasarecallscoreof80percent.

Precision:ThisisthenumberofcorrectselectionstheclassiEiersmakesasaproportionofalltheselectionsithasmade.IfarelevancyclassiEierselects10Tweetsasrelevant,and8ofthemactuallyareindeedrelevant,ithasaprecisionscoreof80percent.

F-Score:AllclassiEiersareatrade-offbetweenrecallandprecision.ClassiEierswithahighrecallscoretendtobelessprecise,andviceversa.The‘overall’scorereconcilesprecisionandrecalltocreateone,overallmeasurementofperformanceforeachdecisionbranchoftheclassiEier.

N.B.thevaluesforeachalgorithm(calledaclassiEier)arepresentedwithinthedetailedmethodologyofthisreport.Thevaluesareexpressedasvalueupto1:avalueof0.76,forinstance,indicatesa76%accuracy.

CAVEATS

Theresearchoflargesocialmediadatasetsisareasonablynewundertaking.Itisimportanttosetoutaseriesofcaveatsrelatedtotheresearchmethodologythattheresultsmustbeunderstoodinthelightof:

• Thealgorithmsusedarenotperfect:throughoutthereport,someofthedatawillbemis-classiEied.ThetechnologyusedtoanalyseTweetsisinherentlyprobabilistic,andnoneofthealgorithmstrainedandusedtoproducetheEindingsforthispaperwere100%accurate.Theaccuracyofallalgorithmsusedinthereportareclearlysetoutinthisreport.

• Somedatawillbemissed:AcquiringTweetsonthebasisofthekeywordsthattheycontainpresentstwopossibleproblems.First,theinitialdatasetmaycontainTweetsthatareirrelevanttothethingbeingstudied.Secondly,itmaymissTweetsthatarerelevanttothethingbeingstudied.Researchersworkedtoconstructascomprehensivealistofkeywordsaspossible(thesearedetailedinthereport,below),howeveritislikelysomeweremissed,andthenumberspresentedinthisreportarelikelyasubsetofthetotal.

• TwitterisnotarepresentativewindowintoBritishsociety:TwitterisnotevenlyusedbyallpartsofBritishsociety.Ittendstobeusedbygroupsthatareyounger,moresocio-economicallyprivilegedandmoreurban.Additionally,thepoorest,mostmarginalisedandmostvulnerablegroupsofsocietyareleastrepresentedonTwitter;anissueespeciallyimportantwhenstudyingtheprevalenceofxenophobia,Islamophobiaandthereportingofhateincidents. 8

• Overall,thisresearchisintendedtobeanindicative,Eirst-takeofthereactiononTwittertotheseimportantevents.ItisnotpresentedaseitherexhaustiveordeEinitive;anditisverymuchhopedthatitwillstimulatefurtherresearchonthisvitaltopicinthefuture.

For a longer discussion of this issue, see the Demos paper The Road to Representivity 8

DETAILEDMETHODOLOGY

IdentifyingTweetsthatwerehateful,derogatoryandanti-Islamicwasaformidableanalyticalchallenge.First,allTweetswerecollectedthatcontainedoneofanextensivelistoftermsthatcouldbeusedinananti-Islamicway(seeannex).ThiscollectionbeganonFebruary29thandcontinueduntilthe2ndAugust.ItreturnedaverylargenumberofTweetsoverthisperiod,over34,000,000.TheverylargemajorityoftheseTweetswerenotanti-Islamicorhateful.Aseriesofalgorithmswerebuilttorespondtothedifferentchallengesthatthisdatasetposedinordertoidentifytheanti-Islamicsubsetwithinthelargerbodyofdata.EachwasdesignedtoremoveTweetswhichwerenotIslamophobicfromthedataset:

• AlargenumberofTweetscontainedtheword‘Paki’. AclassiEierwasusedtoseparatederogatory9

usesofthiswordfromnon-derogatoryuses.• AlargenumberofTweetsalsocontainedtheword‘terrorist’.Ofcourse,manyTweetscontainingthiswordwereinnowayderogatoryoranti-Islamic.TwoclassiEierswerebuilttoanalysetweetscontainingthesewords:• First,aclassiEierwastrainedtoseparateTweetsreferringtoIslamistterrorismfromotherformsofterrorism.

• Second,oftheTweetsreferringtoIslamistterrorism,aclassiEiertodistinguishviewsbroadlyattackingMuslimcommunitiesinthecontextofterrorism,fromthosebroadlydefendingMuslimcommunities.

• AclassiEierwastrainedtoseparateallotherTweetsinthedatasetintothosethatwerederogatoryandanti-Islamicfromthosewhichwerenot.

• Last,theTweetsthat,basedontheabove,(a)usedtheterm‘Paki’inaderogatoryway,(b)thatusedtheterm‘terrorist’tobroadlyattackMuslimsorMuslimcommunities,(c)thatusedtheotherpossibleslurtermsinthecollectioninawaythatwasanti-Islamicwerecombined.ThesewerethenEilteredtoincludeonlyTweetssentfromtheUK.ThisresultedintheEinaltotalofIslamophobicTweets.

Theaccuracyofthesealgorithmsareasfollows: 10

N.B. whilst this word refers to an ethnic rather than religious group, it was found that it was often used interchangeably 9

to refer to Muslim communities

Due to the large number of classifiers used, the accuracy was checked by taking a random sample of Tweets that - 10

according to the system of algorithms described above - were classified as derogatory anti-Islamic. 75 of these 100 were identified by an analyst as derogatory and anti-Islamic.

Thesealgorithmswereconnectedtogetherintoan‘architecture’,shownbelow.EachTweetcollectedpassedthroughthearchitectureonthebasisofhowitwasclassiEied.Overall,thissystemofalgorithmssucceededinEilteringtheverylarge(over34,000,000)numberofTweetsintoamuchsmaller(657,650)subsetthatweremuchmorelikelytobehateful,derogatoryandanti-Islamic.

Annex-DataCollectionKeywordsTheannexcontainsthekeywordsusedtocollectTweetsanalysedthroughoutthisreport.

1. Words/HashtagsusedtocollectTweetsthatcouldbederogatoryandanti-Islamic

• Jihad• Jihadi• SandFlea• Terrorist• hijab• CamelFucker• CarpetPilot• Clitless• DerkaDerka• Diaper-Head• DiaperHead• DuneCoon• DuneNigger• Durka-durka• Jig-Abdul• Muzzie• Q-TipHead• Rab• Racoon• Rag-head• RugPilot• Rug-Rider• SandMonkey• SandMoolie• SandNigger• SandRat• SlurpeeNigger• Towel-head• MuslimPaedos• Muslimpigs• Muslimscum• Muslimterrorists• Muzrats• muzzies• Paki• Pakis• Pisslam• raghead• ragheads• Towelhead• FuckMuslims• WhiteGenocide• Pegida• EDL• BNP• Rapefugee• Rapeugee• mudshark• kuffar• kafEir