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The Sociology of Fake News: Factors Affecting the Probability of Sharing Political Fake News Online

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Abstract

Drawing on recent literature on fake news, this working paper sheds light on the demographic factors and situational predictors that influence the probability to share political fake news through social media platforms. By using a representative sample of 1.002 US adults from the Pew Research Center, the results of the logistic regression analysis revealed relationships between the probability to share political fake news online and predictor variables such as demographics (age, gender, political orientation and income), and situational factors (perception of frequency of political fake news online, previous unconsciously fake news sharing and perception of responsibility [of different agents]). The research offers evidence regarding the prototype user that contributes to the spread of misinformation and the main implications that this phenomenon entails for professional journalism.
Media and
Communications
Media@LSE Working Paper Series
Editors: Bart Cammaerts, Nick Anstead and Richard Stupart
The Sociology of Fake News
Factors affecting the probability of sharing political
fake news online
Goyanes, Manuel
Lavin, Ana
The Sociology of Fake News
Factorsaffectingtheprobabilityofsharingpoliticalfakenewsonline
GOYANES,MANUEL1
LAVÍN,ANA2
1ManuelGoyanes(mgoyanes@hum.uc3m.es),PhD,teachesattheCarlosIIIUniversityinMadridandhismain
interestsareinmediamanagementandsociologyofcommunicationsciences.Hehaswrittenaboutleadership,
newsoverload,consumercultureandbusinessmodels.HeistheauthorofDesafíoalaInvestigaciónEstándaren
Comunicación.CríticayAlternativas,EditorialUOC.Address:UniversidadCarlosIIIdeMadrid,C/Madrid,133.
28903Getafe(Madrid),Spain.(CorrespondingAuthor)
2AnaLavín(analavinv@gmail.com),studiesattheUniversityCarlosIIIUniversityinMadridandhermain
interestsareinpoliticalcommunication,speciallypoliticalextremismandpopulism,andgenderstudiesinmedia.
Address:UniversidadCarlosIIIdeMadrid,C/Madrid,133,28903Getafe(Madrid)Spain.
PublishedbyMedia@LSE,LondonSchoolofEconomicsandPoliticalScience(ʺLSEʺ),
HoughtonStreet,LondonWC2A2AE.TheLSEisaSchooloftheUniversityofLondon.Itisa
CharityandisincorporatedinEnglandasacompanylimitedbyguaranteeunderthe
CompaniesAct(Regnumber70527).
Copyright,ManuelGoyanesandAnaLavin©2018.
Theauthorshaveassertedtheirmoralrights.
ISSN:14741938/1946
Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem
ortransmittedinanyformorbyanymeanswithoutthepriorpermissioninwritingofthe
publishernorbeissuedtothepublicorcirculatedinanyformofbindingorcoverotherthan
thatinwhichitispublished.Intheinterestsofprovidingafreeflowofdebate,viewsexpressed
inthispaperarenotnecessarilythoseofthecompilersortheLSE.
Abstract
Drawingonrecentliteratureonfakenews,thisworkingpapershedslightonthedemographic
factorsandsituationalpredictorsthatinfluencetheprobabilitytosharepoliticalfakenews
throughsocialmediaplatforms.Byusingarepresentativesampleof1.002USadultsfromthe
PewResearchCenter,theresultsofthelogisticregressionanalysisrevealedrelationships
betweentheprobabilitytosharepoliticalfakenewsonlineandpredictorvariablessuchas
demographics(age,gender,politicalorientationandincome),andsituationalfactors
(perceptionoffrequencyofpoliticalfakenewsonline,previousunconsciouslyfakenews
sharingandperceptionofresponsibility[ofdifferentagents]).Theresearchoffersevidence
regardingtheprototypeuserthatcontributestothespreadofmisinformationandthemain
implicationsthatthisphenomenonentailsforprofessionaljournalism.

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1 INTRODUCTION
Inthepastfewdecades,readers’newsconsumptionbehaviourhasdramaticallychanged
(Goyanes,2014;Shuetal.,2017).Thegrowingpopularityofdigitalinformationplatformsand
thesharpdeclineinnewspapercirculationandnetworknewsratingshaveledmanyscholars
tospeculatethatnewmediawouldeventuallyreplacetraditionalsourcesofnewsand
information(Meyer2004;Ahlers2006).Inthischangingmediacontext,thepowerand
importanceofsocialmediaplatformstoaccessorbeingexposedtonewsfromdifferentmedia
outlets(GildeZuñiga,WeeksandArdèvolAbreu,2017),hasgrowndramatically(Reuters,
2014).Forinstance,accordingtorecentdatafromtheReutersInstitute(2017),morethanhalf
(54%)ofallonlineusersacross36countriesusesocialmediaasasourceofnews,andmore
thanoneinten(14%)usesocialmediaastheirmainsource.Theseconsumptionpatternsare
especiallyremarkableintheUSA,acountryinwhich43%ofthepopulationgotnewsfrom
socialmediain2013(ReutersInstitute2013),increasingupto67%in2017(PewResearch
Center2017).
Althoughsomeobservershaveclaimedsocialmediamightpositivelyimpactmediabrands
bydrivingtraffic(NielsenandSchrøder,2014;Ju,JeongandChyi,2014),offeringconsumers
thepossibilitytointeractwithotherreaders(Shuetal.,2017),thealternativehypothesis,i.e.
thatsocialmediaplatformsaresubstitutingmediaoutlets(asasource),seemsequally(ifnot
more)plausible,givingrisetoanambientjournalism(newsisomnipresent),andaperception
thatnewswillfindreaders(Herminda,2010;GildeZuñiga,WeeksandArdèvolAbreu,2017).
Thisrisingpopularityofsocialmediaplatformsintermsofnewsconsumptionhasalsoledto
seriousconcernsamongscholarsandlegislatorsaroundtheworldabouttheirpotential
influenceindisseminatinglargevolumesofnonsupervisedjournalisticcontent(Baumetal.,
2017),empoweringamisinformationphenomenon,(Darnton,2017)andthusprovokingthe
possibilitytomanipulatethepublic’sperceptionofrealitythroughtheviralspreadoffake
news(Gu,etal.,2017).
Thelimited,butgrowingtheoreticalandempiricalresearchonfakenewshaveaddressed
differentdimensionsofthephenomena,suchasitscrosscountryprevalence(Reuters
Institute,2017),the(theoretical)consequencesofitsspread(Guptaetal.,2013)and/orthemain
producers’motivationsfortheircreationanddissemination(AllcottandGentzkow,2017;
Subramanian,2017;Silverman,2016;MarwickandLewis,2017).Theliteraturesuggeststhat
althoughthereisanincreasingawarenessoftheprevalenceoffakenews,theextentofits
impactinEuropeisstillverylimited(ReutersInstitute,2017),thekeymotivationsare
pecuniary(AllcottandGentzkow,2016)butalso(andincreasingly)ideological(Marwickand
Lewis,2017),whilethetheoreticalandpotentiallynegativeconsequencesoftheirspreadpoint
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toamyriadofagentsandsocialcircumstances,suchaspoliticalleadersanddemocratic
societies(Gu,etal.,2017)stockmarkets(Ferraraetal.,2016),healthpolicies(FernándezLuque
andBau,2015)citizensincrisissituations(Guptaetal.,2013),and,fundamentally,readers’
interpretationofreality(Cooketal.,2012;Silverman,2016).
Whilethisgrowingliteraturehasgenerallyemphasizedthecentralityoffakenewsproducers
andhowthespreadoffakeinformationleadstoagrowingdifficultyofaudiencesto
distinguishbetweenprofessionalandnonprofessionalnewscontent(Tandocetal.,2017)and
thusmakinghumansmorevulnerabletoonlinemanipulations(Shaoetal.,2017),akey
questionstillremainsunanswered:whatdemographicandsituationalfactorsdriveconsumers
tosharefakenewsonline?Specifically,giventheincreasingimpactoffakenewsonthe
politicalagendaandvoters’decisions(Balmas,2014),wefocusontheprobabilitytoshare
politicalfakenewsthroughsocialmedia.Byaddressingthisgapintheliterature,andbased
onarepresentativeUSAsurveyfromthePewResearchCenter(2017),weaimtocontributeto
amorenuancedunderstandingofthisrecentphenomenabyempiricallyconfirmingor
refutingmanyoftheassumptionsheldinthemediaandpoliticalphoraregardingthe“who”
question,offeringalsosomeindirectinsightsregardingthemainmotivationsforfakenews
sharing.
Concretely,thispaperinvestigateshowdifferentdemographicfactors(sex,age,gender,
politicalorientationandincome)andsituationalpredictors(perceptionoffrequencyof
politicalfakenews,previousonlinefakenewssharing[unnoticed],andperceptionof
responsibilityinpreventingfakenews[ofmembersofthepublic,politiciansandsocial
networkingsites]),affectstheprobabilitytosharepoliticalfakenewsthroughsocialmedia
platforms.Byusingalogisticregressionanalysis,ninemainfindingsemerged:(1)the
probabilityofsharingpoliticalfakenewsonlineishigherinmalesthanfemales;(2)older
peoplearemorelikelytosharepoliticalfakenewsonlinethanyoungerpeople;(3)peoplewith
lowerincomeshavemoreprobabilitytosharepoliticalfakenewsonline;(4)democratvoters
havelessprobabilitytosharepoliticalfakenewsthanindependentvoters(thereisnostatistical
significancebetweendemocratsandrepublicans);(5)peoplewhohaveahighperceptionof
frequencyofonlinefakenewsaremorelikelytosharepoliticalfakenews;(6)peoplewho
inadvertentlyhavesharedfakenewshavelessprobabilitytosharepoliticalfakenewsonline
onpurpose;(7)peoplewhograntgreatresponsibilitytothepublicinpreventingfakenews
storiesfromgainingattentionarelesslikelytosharepoliticalfakenews;(8)peoplewhogrant
greatresponsibilitytosocialnetworkingsitesinpreventingfakenewsstoriesfromgaining
attentionaremorelikelytosharepoliticalfakenewsstoriesand(9)democratfemalevoters
arelesslikelytosharepoliticalfakenewsthanmaleindependentvoters.
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2 LITERATURE REVIEW AND RESEARCH QUESTIONS
Inordertoinvestigatethepotentialinfluencethatdifferentdemographicandsituational
factorsmighthaveontheprobabilityofconsumerstosharepoliticalfakenewsviasocial
mediaplatforms,itisimportanttofirstunderstandthemotivationsforproducingfakenews
andthescaleofthisphenomena.Wefirstassessrecentstudiesonfakenewsandaddressthe
relevanceofcommercialandpoliticalinterestsbehindfakenewsproviders,emphasizingthe
keyroleofsocialmediaplatformsascentralspacesoffakenewsamplification.Fromthere,we
turntotheconsequencesofthescaleofonlinefakenewsandtheroleofprofessional
journalismintheirprevention.Finally,welookattheimpactofnewsconsumptionthrough
socialnetworkingsitesandthedemographicsoffakenewssharing.
2.1 Fake news online
AccordingtoCollinsDictionarytheconceptof“fakenews”startedbeingusedonUStelevision
todescribe“false,oftensensational,informationdisseminatedundertheguiseofnews
reporting”.Communicationscholarshavescrutinizeddifferentanglesofthefakenews
phenomena,especiallyaroundthemotivationsfortheirproductionanddissemination(Allcott
andGentzkow,2017;Subramanian,2017)andthepotential(negative)consequencesoftheir
consumption(Ferraraetal.,2016;Silverman,2016;Gu,etal.,2017).Inthisregard,thereisan
academicagreementthatthemainmotivationsbehindfakenewsproductionarecommercial
(chrematisticinterest)andpolitical(ideological)(Hirst,2017).Ontheonehand,the
commercialmotivationsrefertothecreationanddisseminationoffakenewsinorderto
increasethereadershipofanewssite,andgetmoreadvertisingrevenuesasaconsequence
(AllcottandGentzkow,2016).Forinstance,inthe2016USelectionsoneofthemostimportant
fakenewsproviderswascreatedbyteenagersinatowninMacedoniawithnoideological
agendabutrathereconomicincentives(Subramanian,2017;Silverman,2016).Theystatedthat
publishingproTrumpcontentgeneratedthemmoreadvertisingrevenue(Marwickand
Lewis,2017).
Ontheotherhand,thesecondmotivationisideological(AllcottandGentzkow,2017;Gu,
Kropotov,andYarochkin,2017),basedoncampaignsmanipulation,defamationofcandidates
withtheintentofdamagingtheirpublicimage.Inthiscase,theobjectiveoffakenews
providersistoempowerthecandidatetheyfavourthroughfalseinformationthatcanchange
theopinionoftheaudience(AllcottandGentzkow,2017;Gu,etal.,2017).Aclearexampleof
thisphenomenonhappenedonJuly2016whenthewebsitewtoe5news.compublishedan
articleallegingthatPopeFrancissupportedDonaldTrump’spresidentialcandidacy(Allcott
andGentzkow,2017).ThenewswassharedonFacebookmorethanonemilliontimesand
manypeoplebelievedthattheheadlinewastrustworthy.
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Accordingtopreviousstudies,thescaleandinfluenceoffakenewsistiedtothepowerof
socialnetworkstodisseminateitandtheirincreasingroleonnewsconsumption;AsAllcot
andGentzkow(2017:221)pointout:
Onsocialmediathefixedcostsofenteringthemarketandproducingcontentare
vanishinglysmallandtheformatofsocialmediacanmakedifficulttojudgeanarticle’s
veracity.
Recentdataontherelationshipbetweensocialnetworkingsitesandthescaleoffakenews
indicatesthat41,8%ofthefakenewswebsitestrafficcomesfromsocialmedia,while
traditionalandtopnewssitesonlyrepresent10%ofthetotalsharetraffic(Allcottand
Gentzkow,2017).Someobserverslinktheviraldiffusionofdigitalfakeinformationtotherise
ofsocialbots(Shaoetal.,2017).Ferraraetal.(2017:1)observeamassiveincreasein‘social
mediaaccountscontrolledbycomputerscriptsthattrytodisguisethemselvesaslegitimate
humanusers’.Suchfakeaccounts,onFacebookorInstagram,butespeciallyonTwitter,post
content,interactwitheachotherandlegitimateusersviasocialconnections,makinghumans
morevulnerabletoonlinemanipulations(Shaoetal.,2017).AccordingtothePewResearch
Center,74%ofTwitterusersgotnewsthere,whereasbetween9%and15%ofactiveaccounts
onTwitterarebots(Ferraraetal.,2017).InRussia,45%ofTwitteractivityismanagedbyhighly
automatedaccounts(Woolley,andHoward,2017).
Thescaleoffakenewsisgrowingrapidlybecausetheaccessbarrierstoinformation
consumptionhavealmostdisappearedandsocialmediasiteshavebecomeopen,freeand
unrestrictedplatformsfornewssharingandconsumption(AllcottandGentzkow,2017).In
today’smediaenvironment,informationisfreefloatingontheInternet(Sundar,2008)and
traditionalgatekeeperslikeprofessionaleditorsjournalistsarelargelyabsent(McGrewetal.,
2017;Cooketal.,2012).Thisphenomenongivespeopleahugeresponsibilitytocriticallyself
evaluatethereliabilityofonlineinformation(McGrewetal.,2017),generatingagrowing
difficultyfortheaudiencetodistinguishbetweenjournalisticandnonjournalisticnews
contentandthustocalibratethedifferencebetweenfalseandcorrectinformation(Tandocet
al.,2017).Accordingtorecentmarketresearch,64%ofAmericanssayfabricatednewsstories
causesthemagreatdealofconfusionrelatingtothebasicfactsofcurrentissuesandevents
(PewResearchCenter2016).Thismightleadthemtomakedecisionsagainsttheirown
interests(McGrewetal.,2017).Inthiscontextofnewsuncertainty,thereisacademicconsensus
onthecontinuedimportanceofprofessionaljournalismandfactcheckinginordertoreduce
theprobabilityofaudiencesbeinginfluencedbymisinformation(Amazeen,2017).
Atthesametime,trustandconfidenceamongstcitizensintraditionalmassmediais
continuouslydecreasing(GoyanesandVaraMiguel,2017;AllcottandGentzkow,2017).This
lackoftrustinmainstreammediacouldexplaintheincreaseddemandfornewsfromnon
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traditionalsources(AllcottandGentzkow,2017).AstudyfromReutersshowsthatamong36
countriesjustonethirdoftherespondentsfeltthattheycouldtrustnewsmedia(Reuters
Institute,2017).Peopleprefernewssourcesthatsupporttheirexistingviews[selective
exposure](Cooketal.,2012),whichreflectsthathumansarebiasedinformationseekers(Baum
etal.,2017).Becauseofthisprocess,audiencesperceivepartisancontentasmoreinteresting
andinformativethancontentwhichcontradictstheirownideas(Coeetal.,2008).Social
networkslikeFacebookorTwitterareideologicallysegregatedanduserstendtoreadand
sharenewsarticlesalignedwiththeirideologicalposition(Bakshyetal.,2015).Inadditionto
this,readerstrustthesharermorethanwhoproducesthearticleregardlessofwhetherthe
articleisproducedbyarealnewsorganizationorafictionalone(MediaInsightProject2016).
Whenpeopleseeapostfromatrustedpersontheyfeelmorelikelytorecommendthenews
sourcetofriends(MediaInsightProject2016),andwhentheinformationcomesfroman
unfamiliaroranoppositionsourceitwillusuallybeignored(Baumetal.,2017).
Peoplewhousemoretimeconsumingmedia,arenotonlyolderandtendtohaveahigher
educationallevel,theyalsohavemoreaccuratebeliefsaboutnews(AlcottandGentzkow,
2017).Ontheotherhand,youngsocialmediausers’newsconsumptioncanbedefinedas
“incidental”,becauseforthem,newscomestothemundifferentiatedfromentertainment
informationtheyfindonInternetwhiletheyaresurfingonsocialnetworks(GildeZuñigaet
al.,2017).Ontheotherhand,emotionsareimportantinhowpeoplerespondtoincorrect
politicalmisinformation(Weeks,2015).Unlikeangrypeoplewhoprocessinformationina
partisanmanner,peoplewithanxietyreducetherelianceonpartisanship(Weeks,2015).
Peopleshareinformationthatwillevokeanemotionalresponseinthereceiver,regardlessof
ofwhethertheinformationistrueornot(Cooketal.,2012).Asaresult,newsreadersmight
contributeconsciouslyorunconsciouslytothespreadoffakeinformationbysharingnews
thatmighthavealargerimpactontheironlinesocialconnections(Bartheletal.,2016).
Whenitcomestoideology,Democratsaremorepronetodistinghuishtruefromfakearticles
thanRepublicanswhose‘trustandconfidence’intraditionalmassmediaissharplydecreasing
(AllcottandGentzkow,2017).TheescalationoftheRepublicans’discredittowardstraditional
mediaisreflectedonthecurrentpresidentoftheU.S.,DonaldTrump,whoclaimed146times
inhisTwitterpersonalaccountthatmainstreammediaisasourceoffakeinformationand
newsmanipulation(Hirst,2017).Inthiscase,thetermfakenewsreferstonewshedoesnot
like.DuringtheUSelections2016campaign,peoplewhosupportedTrumpusedtovisitfake
newswebsitesmorethanHillarysupporters(Guessetal.,2018)andonFacebook,therewere
aboutthreetimesmorefakeproTrumparticlesthanproClintonones(AllcottandGentzkow,
2017).Inaddition,Republicans(39%)haveslightlymoreprobabilitiestousesocialmediato
repostcontentrelatedtopoliticalmattersthanDemocrats(34%)(Rainieetal.,2012).
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Despitetherelevanceandtheoreticalramificationsoftheliteratureonfakenews,researchon
thedemographicandsituationalfactorsthatmightinfluencenewsconsumers’probabilityto
sharepoliticalfakenewsonlineisscarce.Paststudieshavemainlyexaminedtheprevalence
ofthephenomena(ReutersInstitute,2017),themotivationsforfakenewscreation(Allcottand
Gentzkow,2017;MarwickandLewis,2017)andtheeffectsoffakenewsdisseminationon
society,politicalleaders,stockmarkets,etc(Silverman,2016;Ferraraetal.,2016;Guetal.,2017).
However,giventheincreasingimpactoffakenewsonthepoliticalagendaandvoters’
decisions(Balmas,2014)andtherelevanceofsocialmediaplatformsintheamplificationof
themisinformationphenomenon,thisstudyfocusesontheconsumerimpactonfakenews
disseminationandanalysesthedemographicandsituationalfactorsbehindthereachof
politicalfakenews.Therefore,thisstudyexploresthefollowingresearchquestions(RQ):
RQ1.Howdodemographics(sex,age,gender,politicalorientationandincome),affect
theprobabilitytosharepoliticalfakenewsonline?
RQ2.Whatistheinteractionbetweenpoliticalorientationandgender?
RQ3.Howdoestheperceptionoffrequencyofpoliticalfakenewsonlineaffectthe
probabilitytosharethem?
RQ4.Howdoespreviousonlinefakenewssharing(unnoticed),affecttheprobability
tosharepoliticalfakenewsonline(onpurpose)?
RQ5.Howdoestheperceptionofresponsibilityof1)membersofthepublic,2)
government,politiciansandelectedofficialsand3)FacebookandTwitter,intryingto
preventfakenewsaffecttheprobabilitytosharethemonline?
3 METHODOLOGY
TheanalysisinthisstudyisbasedonaPewResearchCentersurveyconductedDec.1through
Dec.4,2016,amonganationalrepresentativesampleof1,002adults,18yearsofageorolder,
livinginthecontinentalUnitedStates3.TheresultsofthestudycarriedoutbythePew
ResearchCenter(Journalism&Media)aredescriptive.Thatis,onlydescriptivestatisticsare
usedtoexplorethegeneralsituationofU.S.fakenewswithoutadvancinganyinferential
analysis.Theanalysisperformedandtheresultsobtainedandexposedherearenew,except
forthedescriptive(raw)datainrelationtothedependentvariableitself.

3Tohavemoreinformationaboutthemethodologyconsultsthefollowingweb:
http://www.pewresearch.org/methodology/ussurveyresearch/
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Themodelconstructedisbasedonabinomiallogisticregression,analyzingtheprobabilityof
sharingpoliticalfakenewsasadependentvariable.Logisticregressionteststheprobabilityof
adichotomouseventoccurring—inthiscase,sharingornotpoliticalfakenews.Thepredicted
proportionofactivitiesfollowsthelogisticmodeloflnP/(1
Pi)=βXi,wherePiisthe
probabilityofsharingpoliticalfakenews.Allpredictorvariableswereintroducedfive
differentblocks:demographics,frequencyofpoliticalfakenews,unnoticedfakenews,
perceptionofresponsibility,andinteractionbetweenpoliticsandgender.
3.1 Variables and Measurements
Dependentvariable:Thedependentvariable,theprobabilitytosharepoliticalfakenews,was
measuredbyaskingparticipantsthefollowingquestion:“Haveyoueversharedapolitical
newsstoryonlinethatyouthoughtatthetimewasmadeup?(0)Noand(1)yes.
Independentvariables:Dataforthedemographicvariables(i.e.,gender,age,income,education
andpoliticalorientation)werecollectedusingstandardsurveymeasurements.Theperception
offrequencyofpoliticalfakenewswasmeasuredbyaskingparticipantsthefollowing
question:“Howoftendoyoucomeacrossnewsstoriesonlinethatyouthinkarealmost
completelymadeup”,onafourpointLikertscale,rangingfrom1)never,2)hardlyever,3)
sometimesand,4)often.Unnoticedfakenewssharingwasmeasuredbyaskingparticipants
thefollowingquestion:“Haveyoueversharedapoliticalnewsstoryonlinethatyoulater
foundoutwasmadeup?(0)Noand(1)yes.Theperceptionofresponsibilitywasmeasured
byaskingparticipantsthefollowingquestion:“Asyoumayhaveheard,therehaverecently
beensomeinstancesofsocalled“fakenewsstories”circulatingwidelyonline.Howmuch
responsibilitydoeseachofthefollowinghaveintryingtopreventmadeupstoriesfrom
gainingattention”a)membersofthepublic,b)thegovernment,politicians,andelected
officials,c)socialnetworkingsiteslikeFacebookandTwitter,andsearchsiteslikeGoogle,on
afourpointLikertscale,rangingfrom:1)noresponsibilityatall,2)notmuchresponsibility,
3)afairamountofresponsibility,4)agreatdealofresponsibility.
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4 RESULTS
4.1 Descriptive statistics
Thesampleof1,002U.S.residentsincludedslightlymorewomen(51,3%)thanmen(48.7%),
withanagerangebetween18and99(M=47.22;SD=18.22).Mostparticipants(50,7%)
declaredthey“often”cameacrossnewsstoriesonlinethattheythinkwerealmostcompletely
madeup,27,1%declared“sometimes”,8,4%“hardlyever”andanegligible10,4%declared
they“never”cameacrosspoliticalfakenews.Withregardtothedependentvariable,13.8%of
thetotalsampledeclaredtheysharedpoliticalfakenews,whilethevastmajority,thatis,85.5%
ofparticipantsdeclaredtheyneversharedpoliticalfakenewsonline.Thecorrelationanalysis
revealedsomeimportantassociations.Interestingtonote,forexample,arethenegative
associationsbetweenageandfakenewsfrequencyandpublicresponsibility,whileapositive
statisticallyassociationwithgovernmentresponsibility.Ontheotherhand,ourcorrelation
analysisrevealedastatisticallysignificantandpositiveassociationbetweeneducationand
fakenewsfrequency,publicresponsibilityandSNSresponsibility.Thesamepatternsare
appliedwithregardtoincome.
Table 1. Correlations between quantitative variables
4.2 Probability to share political fake news online: Logistic regression
analysis
Thelogisticregressionanalysisresultsrevealedtherelationshipbetweentheprobabilityto
sharepoliticalfakenewsandsomepredictorvariables(p<.05)Thefirstmodel
(demographics),accountedfor4.4%or2.5%ofthevarianceintheprobabilitytosharepolitical
fakenews(NagelkerkeR2andCox&SnellR2),thesecondmodel(perceptionoffrequencyof
politicalfakenews),accountedfor4.7%or2.7%ofthevariance,thethirdmodel(unnoticed
fakenews),accountedfor23.5%or13.3%ofthevariance,thefourthmodel(perceptionof
responsibility),accountedfor24.1%or13.6%ofthevariance,andthefifthmodel(interaction
AgeEduc.Inc.FakeNews
Freq
Resp.(1)Resp.(2)Resp.(3)
Age1 ‐,010,008 ‐,092** ‐,060**,097**,031
Education ‐,0101,507**,114**,148** ‐,031,048**
Income,008,507**1,190**,115**,001,088**
FNFreq. ,092**,114**,190**1,171**,143**,227**
Resp.(1) ‐,060**,148**,115**,171**1,268**,234**
Resp.(2),097** ‐,031,001,143**,268**1,330**
Resp.(3),031,048**,088**,227**,234**,330**1
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betweenpoliticsandgender),accountedfor24.1%or13.6%ofthevariance.Thesefindings
thussuggestconsiderableexplanatorypower.
Withregardtothefirstresearchquestion,whichaddressedtheassociationbetween
demographicvariablesandtheprobabilitytosharepoliticalfakenews;age,gender,income,
educationandpoliticalorientationwereenteredinthefirstblockoftheanalysis.Thelogistic
regressionresultsrevealedastatisticallysignificantassociation(p<.05)withgender,age,
income,andpoliticalorientation.Therefore,theprobabilityofsharingpoliticalfakenews(p=
<.05;β=‐,695;,499ex)washigherinmalesthanfemales.Thelogisticregressionalsoidentified
ageasastatisticallysignificantandpositivepredictor;thatis,theprobabilityofsharing
politicalfakenews(p=<.05;β=,013;1,013ex)increaseswithage.However,incomewasa
negativepredictor(p=<.05;β=‐,073;,930ex),insuchawaythatpeoplewithlowerincome
havemoreprobabilitytosharepoliticalfakenews.Finally,theregressionanalysisrevealeda
significantandnegativeassociationbetweenpoliticalorientationandtheprobabilitytoshare
politicalfakenews.Specifically,democratvoters(p=<.05;β=‐,237;,789ex)haveless
probabilitytosharepoliticalfakenewsthatindependentvoters(thereisnostatistical
significancebetweendemocratsandrepublicans).
Thesecondresearchquestionaskedwhethertheperceptionoffrequencyofonlinefakenews
wouldpredicttheprobabilitytosharepoliticalfakenews.Inadditiontogender(p=<.05;β=
,701;,496ex),age(p=<.05;β=,014;1,014ex),andincome(p=<.05;β=‐,081;,923ex),the
perceptionofonlinefakenewsfrequency(p=<.05;β=,125;1,133ex)wasasignificant
predictor.Hence,theincreasingperceptionofonlinefakenewsincreasestheprobabilityto
sharepoliticalfakenews.Thethirdresearchquestionaskedwhetherpreviouslyunnoticed
fakenewssharingwouldpredicttheprobabilitytosharepoliticalfakenews.Inadditionto
gender(p=<.05;β=‐,800;,449ex),age(p=<.05;β=,011;1,011ex),income(p=<.05;β=‐,072;
,931ex),andpolitics(p=<.05;β=‐,296;,744ex),unnoticedfakenewssharing(p=<.05;β=‐
2,210;,110ex),wasasignificantandnegativepredictorofpoliticalfakenewssharingonline.
Therefore,peoplewhohaveinadvertentlysharedfakenewshavelessprobabilitytoshare
politicalfakenewsonline.
Thefourthresearchquestionaskedwhethertheperceptionofresponsibilityofdifferentsocial
stakeholdersintryingtopreventfakenewscouldpredicttheprobabilitytosharepoliticalfake
news.Inadditiontogender(p=<.05;β=‐,805;,447ex),age(p=<.05;β=,010;1,010ex),income
(p=<.05;β=‐,068;,934ex),politics(p=<.05;β=‐,327;,721ex),andunnoticedsharingfake
news(p=<.05;β=‐2,24;,106ex),publicresponsibility(p=<.05;β=‐,132;,876ex),andsocial
networkingsitesresponsibility(p=<.05;β=,161;1,174ex),werefoundtobesignificant
predictorsofpoliticalfakenewssharing.

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Table2.Logisticregressionanalysis.*p<0,05;**p</0,01

Model1Model2Model3Model4Model5
BExp(B)BExp(B)BExp(B)BExp(B)BExp(B)
Gender ‐,695**,499 ‐,701**,496 ‐,800**,449 ‐,805**,447 ‐,630**,528
Age,013**1,013,014**1,014,011**1,011,010**1,010,010**1,010
Education ‐,016,984 ‐,016,984 ‐,017,983 ‐,011,989 ‐,005,995
Income ‐,073**,930 ‐,081**,923 ‐,072**,931 ‐,068*,934 ‐,071*,931
Politics(1) ‐,001,999,0211,021 ‐,017,983 ‐,027,973 ‐,040,961
Politics(2) ‐,237*,789 ‐,212,809 ‐,296*,744 ‐,327*,721 ‐,122,885
FNFreq   ,125*1,133 ‐,031,969 ‐,059,943 ‐,065,937
UnnoticedFN  2,210**,110 ‐2,24**,106 ‐2,23**,107
Resp.Pub       ‐,132*,876 ‐,136*,873
Resp.Gov       ‐,009,991 ‐,005,995
Resp.SNS      ,161**1,174,161**1,17
Gend(1)*Pol(1)     ,005,995
Gend(1)*Pol(2)     ,511*,553
NagelkerkeR2,044  ,047  ,235  ,241  ,243 
Cox&SnellR2,025  ,027  ,133  ,136  ,137 
Chi266,89**  4,26*  306,9**  9,76*  3,625 
Loglikelihood2146,0  2141,8  1834,8  1825,0  1821,4 
No.ofobservations1.002  1.002  1.002  1.002  1.002 
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Withregardtopublicresponsibility,thelogisticregressionanalysisrevealedastatistically
significantandnegativerelationwithpoliticalfakenewssharing.Therefore,peoplewhogrant
greatresponsibilitytothepublicinpreventingfakenewsstoriesfromgainingattentionare
lesslikelytosharepoliticalfakenews.However,whenitcomestosocialnetworkingsites,the
logisticregressionrevealedastatisticallysignificantbutpositiveassociation,insuchaway
thatpeoplewhograntgreatresponsibilitytosocialnetworkingsitesinpreventingfakenews
storiesfromgainingattentionaremorelikelytosharepoliticalfakenewsstories.
Thefifthresearchquestionaskedwhethertheinteractionbetweengenderandpolitical
orientationcouldpredicttheprobabilitytosharepoliticalfakenewsonline.Inadditionto
gender(p=<.05;β=;ex),age(p=<.05;β=;ex),income(p=<.05;β=;ex),unnoticedfake
newssharing(p=<.05;β=;ex),andsocial(p=<.05;β=;ex),andsocialnetworkingsites
responsibility(p=<.05;β=;ex),theinteractionbetweengender(1)andpolitics(1),werefound
tobeasignificantpredictoroftheprobabilitytosharepoliticalfakenewsonline(p=<.05;β=
;ex).Therefore,democratfemalevotersarelesslikelytosharepoliticalfakenewsthanmale
independentvoters.
5 CONCLUSION
Thispaperinvestigateshowdifferentdemographicfactors(age,gender,politicalorientation
andincome)andsituationalpredictors(perceptionoffrequencyofpoliticalfakenews,
previousonlinefakenewssharing[unnoticed],andperceptionofresponsibilityinpreventing
fakenews[ofmembersofthepublic,politiciansandsocialnetworkingsites]),affectthe
probabilitytosharepoliticalfakenewsthroughsocialmediaplatforms.First,ourresearch
addressestherelevantroleofdemographicvariablesinexplainingthevarianceofpolitical
fakenewssharing.Resultsofthelogisticregressionanalysisprovidesstrongevidence
regardingthekeyroleofage,gender,incomeandpoliticalorientationinthespreadofpolitical
misinformationonline.Inthisregard,theprobabilityofsharingpoliticalfakenewsonlineis
higherinmalesthanfemales,despitethefactthatwomenusesocialmediamorethanmen
(Krasnovaetal.,2017).Inaddition,ouranalysisalsorevealsthatsharingpoliticalfakenews
increaseswithage,despitethefactthatyoungpeoplearethemajorityofinternetusers
(McGrewetal,2017).Peopletendtoshowanincreasinginterestinnewsastheygetolder.For
youngerusers,topicslikedomesticpolitics,internationalpoliticsandeconomyareseenasless
interesting(Costera,2007),whichmightexplainwhyolderpeoplearemorelikelytoshare
politicalfakenewsonline.
Peoplewithlowincomesarealsomorelikelytosharepoliticalfakenews.Previousstudies
suggestthateducatedpeopleearnmoremoneythatpeoplethathavenotaccessedtheschool
system(DienerandBiswasDiener,2002)whereeducationincreasespeople’scapacitiesto
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differentiatefactfromfictionand,also,offerpeoplebettermeanstocounterargueinconsistent
information(AllcottandGentzkow,2017).Whenitcomestoideology,democratvotersare
lesslikelytosharepoliticalfakenewsthanindependentvoters.However,thereisnostatistical
differenceintheprobabilitytosharepoliticalfakenewsbetweendemocratsandrepublicans,
despitethefactthatdemocratshavesomewhatlessprobability(34%)torepostnewsrelated
topoliticalaffairsthatwerepreviouslypostedbyotherpeopleonsocialmediathan
republicans(39%)(Rainieetal.,2012).
Resultsofthelogisticregressionrevealthattheincreasingperceptionofonlinefakenews
increasestheprobabilitytosharepoliticalfakenews.Nowadays,informationontheinternet
isreplacingprofessionaljournalismandexpertadvice(Cooketal.,2012)andabout40%ofthe
fakenewswebsitestrafficcomesfromsocialmediaplatforms(AllcottandGentzkow,2017).
Inaddition,peoplewhogetnewsfromsocialmediareadinformationthatisideologically
aligned(AllcottandGentzkow,2017)andmanyareunawareoftheeffectsofsocialmedia
manipulation(Glenski,andWeninger,2016).Furthermore,64%ofAmericansaffirmthatfake
newshasgeneratedthemagreatdealofconfusionaboutbasicfacts,and24%ofU.S.citizens
affirmthatthisphenomenonhasprovokedthemsomeconfusion(Bartheletal.,2016).Ina
contextwherefakenewsiseasilyspreadandconsumedthroughthemostimportantsocial
networks,theperceptionoftheirscalepositivelyaffectspeople’ssharingbehaviour.
Ouranalysissuggeststhatpeoplewhoinadvertentlysharefakenewshavelessprobabilities
tosharepoliticalfakenewsonline.Formanypeopleitiscomplicatedtorecognizethatapiece
ofinformationisfalseuntiltheyreceiveacorrection(Cooketal.,2012).Warningsseemtobe
effectivebecausetheyinducepeopleinatemporarystateofscepticism,increasingtheir
capacitiestodifferentiatebetweentrueandfalseinformation(Cooketal,2012).Therefore,
peoplewhograntgreatresponsibilitytothepublicinpreventingfakenewsstoriesfrom
gainingattentionarelesslikelytosharepoliticalfakenews.However,whenitcomestosocial
networkingsites,thelogisticregressionrevealedastatisticallysignificantbutpositive
association,insuchawaythatpeoplewhogranttheresponsibilitytopreventfakenewsstories
fromgainingattentiontosocialnetworkingsitesarethemselvesmorelikelytosharepolitical
fakenewsstories.Finally,ourstatisticalanalysisshowsaninteractionbetweenpolitical
orientationandgender.Tothisregard,democratfemalevotersarelesslikelytosharepolitical
fakenewsthanmaleindependentvoters.
Inconclusion,thecentraltheoreticalimplicationsemanatingfromtheobservationsmadein
thisarticleinclude(1)ashiftinthepointofviewbehindfakenewsdissemiantion,fromthe
importanceofproducerstotherelevanceofconsumers,(2)thesignificanceofdemographic
andsituationalfactorsinexplainingpoliticalfakenewssharingbehaviour,and(3)the
increasinglydecisive/complexroleoftraditionalandnewmediatocontrolthejournalisticflow
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ontheInternet,whereacombinationofdigitaltoolsandconsumers’behaviour,might
challengetheethosofjournalismandtheveracyofinformationonline.Inthiscontext,
professionaljournalismandfactcheckingareincreasinglyimportanttomitigate,controland
discoverpoliticalfakenewsonlineandtolessentheirpotentialdamagetodemocratic
societies.
6 LIMITATIONS
Severallimitationsofthecurrentanalysisarenoteworthy.First,itisworthnotingthatwith
respecttopolitical‘fakenews’theAmericancontextisdifferenttoothercountries.Thereare
particularhistoric,socialandotherfactors,especiallyinthecurrenthighlypolarisedUS
politicalandmediacontextthatappearstomakemisinformationmoreeffectivethaninother
demoi.Second,thedatasetbeingfromthehighpointofpoliticalpolarisationjustafteroneof
themostdivisivepresidentialcampaignsever,shouldbetakenintoaccountwhen
generalizingthefindings.Third,thecrosssectionalnatureofthesurveydatadoesnotallow
ustoidentifywithcertaintythedirectionofthecausalpatternsunderlyingthecorrelations
thatwefound.Therefore,wecannotruleoutthepossibilitythatthecausalordersarereversed.
Morerobustcausalclaimswouldbewarrantedbylongitudinalorexperimentalratherthan
crosssectionalsurveydataandmoreworkisneededtodisentanglethecausalmechanisms
behindthecorrelationspresentedhere.Thus,therelationshipstheorizedinthisworking
papersshouldbeinterpretedwithcaution.Futureresearchmayadoptalongitudinaldesign
todrawcausalinferenceswithgreaterconfidence.Furthermore,allvariablesweremeasured
usingasingleitem,whichprohibitsreliabilityassessments.Aswithanysurvey,wealsorelied
onselfreportedmeasuresofonlinebehavior,whichrequiredsubjectiveassessmentsofthe
frequencyoffakenewssharing.
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