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Figure 1plotsthedailycountsofTweetsusingCOVID19hashtags(6hashtags)againstthenumberofcasesinSingapore,usingthreemeasures:thenumberofconfirmednewcasesannouncedonthatday,thecumulativetotalnumberofconfirmedcases,andthenetnumberofcases(confirmedcases– casesinwhichthepatienthasbeendischargedfromhospital).Casesdischargedfromhospitalaresometimesreferredtoas‘recovered’inthemedia,butthepossibilityremainsofincompleterecoveryorrelapse.

TheplotshowsahighlevelofvolatilityinTwitterattentiontoCOVID19withdailyrisesandfallsinTweetnumbersofmorethan100%onsomedays.ThelevelofactivitydoesnotappeartobecorrelatedwithoverallprevalenceofthecoronavirusinSingaporeorwiththenumberofhospitalised cases.Afirstreadingofthiscouldbethatsocialmediaattentiontotheissueisbeingdrivenbyotherfactorssuchasmoodorfalserumours,orbythelevelofattentiontotheissueintraditionalmediaorgovernmentannouncements.

Weextendtheanalysiswithintheconstraintsofthedataavailabletoconsiderwhetherthesefactorsappeartobecorrelatedwithsocialmediasalience.

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Figure 2plotsTwittersalienceagainstthenumberofnewcases.HereweseeastrongcorrelationbetweenthenumberofnewcasesannouncedandthelevelofTwittersalienceonthefollowingday.Thisrelationshipappearstoweakenattheendoftheperiodunderstudy.Thismaybeduetotherapidglobalisation ofthespreadofthevirusatthistime,whichmightincreasetheinfluenceofinternationalvirusprevalenceinformationonsalienceoverlocalvirusprevalence.

ThisstrongrelationshipbetweensalienceandnumberofnewcasesisconsistentwiththeemphasisgiventothenumberofnewcasesasakeyindicatoringovernmentannouncementsandintraditionalmediareportingonCOVID19.WeexaminethisrelationshipfurtherbelowusingtheFacebookLSCdata.

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Figure 3showsthevolatilityofTwitterattentiontoCOVID19.Twoparticularpeaksinattentioncanbenoted,onthe4-Feb,whenthenumberofTweetsrose148%andon9-Febwhentheyrose163%.ThefirstpeakcoincideswithreportingofthefirstcaseoflocaltransmissioninSingaporeandfollowthePrimeMinister’sspeechandtheannouncementof7newcaseson8-Feb.TheannouncementofDORSCONOrangeon7-Febandthepanic-buyingontheeveningof7-Febdidnotimmediatelytriggerveryhighlevelsofactivity.NumberofTweetsfell39%on7-Febandrose41%on8-Feb.

Intheperiodstudied,Twittersaliencedoesnotappeartobeself-sustaining.Increasesinsaliencearesustainedforamaximumoftwodaysbeforedecliningandthereisonlyoneinstanceinwhichtheincreaseinsalienceishigherthanonthepreviousday(7-9Feb),whichwouldindicateissuesaliencemomentum.Therapidfallinissuesalienceon10-FebafterthePM’sspeechwaswidelyreportedcouldbeinterpretedasshowingtheeffectivenessofthisinterventioninreducingpublicanxiety.However,giventhelargenumberoffactorsinfluencingTwitteractivityandthelimitationsofthedataavailable,weshouldbecautiousintheinterpretationofthesefindings.

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Figure4 showsthepredominanceofkeywordsevokingnegativeandpositiveemotionalvalenceinvirus-hashtagTweetsinthestudyperiod.

Notethatalargersetofkeywordswereused;onlythosewhichhadanoccurrencerateof>5overtheperiodwereincluded.

Akeyfindingisthattherearemorepositiveemotionmarkersthannegativeones(62%comparedto38%).Thisgivesapreliminaryindicationthatsocialmediausersmayseektoencourageandcheertheirnetworkmembersduringaperiodofhighuncertaintyasmuchifnotmorethantodrawattentionbysharingshockingorfear-inducingstories.Thiswouldbeconsistentwithatendencytoseekandprovidesupportindirectpersonalinteractionsinperiodsinwhichexternalrisksareperceivedtobehigh.

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Weturnnexttothetheattentionpaidtoparticularissuesrelatingtothevirus.

Masks:Theissueofmaskavailability,hoardingandtheappropriateuseofmaskshasbeenwidelydiscussedandreportedonsincetheoutbreakofCOVID19.InSingapore,asurgeindemandledtoashortageofmasksinretailoutletsinlateJanuary.Thegovernmentthenannouncedon30-Janthatitwoulddistribute5.2millionfacemasks,providing4masksperhouseholdatnochargeandstatedthattherewasnoshortageofmasksinSingapore,givenappropriateuse.Atthesametime,variousgovernmentagencieswereadvisingthepublicthatmaskswerenotnecessaryunlessonewassick.Medicalexpertsinthemediaalsohighlightedthelimitationsofmaskstopreventcontractingthevirusandrecommendedothercomplementarypracticesofgoodhygiene.TheTwitterdatasuggeststhatmasksarestillasalientissueforthepublic,althoughattentiontotheissuefellafterthegovernment’sannouncementaboutdirectdistribution,mentionedinmorethan200Tweetsinjustbelowhalfthedaysintheperiodofdatacollection.Quarantine,similarly,isanissuethathasattractedcontinuedattention,eventhoughquarantinedirectlyaffectsonlyaverysmallproportionoftheoverallpopulation.

“Localtransmission”and“airborne”:Thesetwoissueshavenotattractedmuch

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attentiononTwitter.Theybothshowasingle,lowpeak,andminimalornoattentioninthesecondhalfoftheperiodstudied.Weincludedthesekeywordsastheywerethesubjectofmisleading/falsesocialmediarumoursthatwerecounteredbygovernment,eitherthroughPOFMAorongov.sg.Whiletheseinterventionsmayhavehelpedtoreversethemomentumoftheserumours,theoverallreachoftherumoursonTwitterappearstohavebeenlowinanycase.

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Figure6examines inmoredetailtherelationshipbetweenanxietyandcommunicationsinterventionsbygovernmenttocontainorcounterfalsehoodsorunnecessarypanic.Weusethekeyword‘panic’anditsvariantsasaproxyforanxiety.Itisimportanttonotethelimitationsofthisapproachinattemptingtoassessanxietylevels,whichgeneratesbothType1andType2erros.Ontheonehand,thekeywordapproachcapturesstatementsevokingacalmingmessagelike“noneedtopanic.”Ontheotherhand,itexcludesawidevarietyofothertermsandindicators(e.g.emojis)ofpeople’sanxietylevels.Bearinginmindtheselimitations,whichwehopetoaddressinfuturework,wecanseethat‘panic’haspeakedonthreeoccasions:atthetimeofthemaskshortage,whenthegovernmentannounceditsintentiontoprovidealimitednumberofmasksdirectlytohouseholds,onthefirstcasesoflocaltransmissionandattheendoftheperiodunderstudywheninformationontherapidspreadofthevirusinS.Koreabecameavailable.TheannouncementofDORSCONOrange,rumours ofDORSCONredandtheircorrection,hadlittleapparentpositiveornegativeimpactonanxietylevels,asmeasuredbythekeyword‘panic.’

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Figure7reports findingsonthenumberofTweetscontainingkeywordsrelatingtotruthvalue:“Truth”,“True”,“Lie”,“Fake”.InordertoavoidType1errorswhereTweetsmayincludestatementslike“nottrue”or“notfake”,weinsteadfocusedontheoverallengagementwithtruthclaimsonTwitter– “truthvalue”.Wedidthisbyaggregatingalltweetswiththeaforementionedkeywordsasanindicationofthelevelofengagementwithfakenews,rumours andcounter-informationtofakenewsandrumours.

Thedata showsincreasesinTweetsrelatingtotruthvalueatpointswhenthequestionofwhetherthevirusisairbornewasanissue,andduringDORSCONrelatedannouncementsandrumours. Itisinterestingtonote thelowengagementonTwitterwithtruthvaluetweetsevenatpointswhenfakenewswasatitsheight:POFMA1(SGPoutofmasks),POFMA2(localtransmission)andPOFMA3(WoodlandsMRTclosedfordisinfection)between28-30Jan.

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Figure8plotsTwitter activitywitheconomy-relatedkeywords. Itshowsanincreasingtrendtowardstheendoftheperiodunderstudy,withmoreactivityafter reportsofsignificantnumbersofcasesinS.Korea, followedbythespreadtoItalyandtheUS.Thetrendin“Stockmarket”Tweetsincreases veryrapidlyafterCOVID-19caseswerereported inEurope. Governmentannouncementsaboutfinancialaidtolocalbusinesseswerenotwidelypickedup inTwitteractivity. Overall,wenotethatthetotalnumberofTweetsusingthesekeywordsislowthroughouttheperiod.

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Weturnnexttoexaminewhichtypesofarticlesfromtraditionalmediapeoplearemostlikelytosharewiththeirnetworkofcontacts.Thisprovidesanindicationofwhether“sharingiscaring”or“sharingisscaring.”

Wepositthatpeoplemaysharestoriesinordertoprovidetheirnetworkwithobjectivefactsrelevanttopersonalriskassessment(codedasRISK,yellow);storiesaboutactionstakenbygovernmentinresponsetotheevolvingsituation(codedasRESPONSE,orange);primarilyaffective(emotional)storiesofadmirablebehaviour ortriumph,orconverselyofdeplorablebehaviour ortragedy(codedasAFFECT,blue);articlesprovidingguidancefromexperts/figuresofauthority(OPINION);storiesreferringspecificallytoacademicstudies(SCIENCE).StoriesnotfallingintothesecategoriesarecodedasOTHER,green– theyincludearticlesontheeffectsofthevirusontheeconomy,responsesofthepublictothevirussuchasprotests,onlinepetitionsorpanic-buyingandgeneralintereststories.

Thetablepresentsthetop10articlesbySLCintheperiodunderstudytoillustratetherangeofstoriesthataresharedandthecodingprocess.Storiesaboutthenumberofcases,likethetop3storiesinthetablearecodedasRISK:theyprovidepotentiallyusefulinformationonriskswhichcouldbeusedbyindividualstoupdatetheir

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assessmentoftheirindividualriskfromCOVID19.

Stories4&6areconcernedwiththegovernmentresponsetothecrisis(RESPONSE).Fromthedataavailable,wearenotabletodistinguishbetweenstoriesthatareLikedandthosethataresharedbutnotcommendedbytheuserandsoonceagainwemustbecautiousintheinterpretation.

Story5isconcernedwiththedeathofadoctor“atthefrontline”oftheoutbreakinChina.Asitisconcernedwithanindividual,wecodeitasAFFECT.

Story8isrelatedtothevirusandprovidessomeinsightintothepotentialoriginofthevirus,butisnotdirectlyusefulforriskassessment.ItiscodedasOTHER.

TheresultsareshowninTable2andFigure9.

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Figure9shows thecountofSLCs(referredtoas‘Facebookbandwidth’)byarticletypefollowingthecodesdescribedabove.Itshowsthatthevastmajorityofarticles(61%)receivingattentiononFacebookarethosethatprimarilyprovidefactualinformationrelevanttoriskassessment.Wecomparethepredominanceofrisk-relatedarticleswiththosegivenmostprominenceintraditionalmedia.Forthis,werecordedtheStraitsTimestopfront-pageheadlineforeachdayoftheperiodunderstudy.Incontrasttosocialmediaactivity,wefindthatthemajorityofheadlinesrelatetoresponses.Thissuggeststhatthearticlespeoplechoosetosharearenotonlyorprimarilydrivenbythearticlesthatmediaoutletsgivemostprominencebutmaybedrivenbyotherfactors,includingthosethattheyfeelaremostrelevanttotheirownriskexposure.Itwouldbeinterestingtoexploretheserelationshipsfurtherwithadditionaldata.

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