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

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.

Full text

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
1ManuelGoyanes(mgoyanes@hum.uc3m.es),PhD,teachesattheCarlosIIIUniversityinMadridandhismain
interestsareinmediamanagementandsociologyofcommunicationsciences.Hehaswrittenaboutleadership,
newsoverload,consumercultureandbusinessmodels.HeistheauthorofDesafíoalaInvestigaciónEstándaren
Comunicación.CríticayAlternativas,EditorialUOC.Address:UniversidadCarlosIIIdeMadrid,C/Madrid,133.
28903Getafe(Madrid),Spain.(CorrespondingAuthor)
2AnaLavín(analavinv@gmail.com),studiesattheUniversityCarlosIIIUniversityinMadridandhermain
interestsareinpoliticalcommunication,speciallypoliticalextremismandpopulism,andgenderstudiesinmedia.
Address:UniversidadCarlosIIIdeMadrid,C/Madrid,133,28903Getafe(Madrid)Spain.

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PublishedbyMedia@LSE,LondonSchoolofEconomicsandPoliticalScience(ʺLSEʺ),
HoughtonStreet,LondonWC2A2AE.TheLSEisaSchooloftheUniversityofLondon.Itisa
CharityandisincorporatedinEnglandasacompanylimitedbyguaranteeunderthe
CompaniesAct(Regnumber70527).

Copyright,ManuelGoyanesandAnaLavin©2018.
Theauthorshaveassertedtheirmoralrights.

ISSN:1474‐1938/1946
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Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem
ortransmittedinanyformorbyanymeanswithoutthepriorpermissioninwritingofthe
publishernorbeissuedtothepublicorcirculatedinanyformofbindingorcoverotherthan
thatinwhichitispublished.Intheinterestsofprovidingafreeflowofdebate,viewsexpressed
inthispaperarenotnecessarilythoseofthecompilersortheLSE.
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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.
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1 INTRODUCTION
Inthepastfewdecades,readers’newsconsumptionbehaviourhasdramaticallychanged
(Goyanes,2014;Shuetal.,2017).Thegrowingpopularityofdigitalinformationplatformsand
thesharpdeclineinnewspapercirculationandnetworknewsratingshaveledmanyscholars
tospeculatethatnewmediawouldeventuallyreplacetraditionalsourcesofnewsand
information(Meyer2004;Ahlers2006).Inthischangingmediacontext,thepowerand
importanceofsocialmediaplatformstoaccessorbeingexposedtonewsfromdifferentmedia
outlets(GildeZuñiga,WeeksandArdèvol‐Abreu,2017),hasgrowndramatically(Reuters,
2014).Forinstance,accordingtorecentdatafromtheReutersInstitute(2017),morethanhalf
(54%)ofallonlineusersacross36countriesusesocialmediaasasourceofnews,andmore
thanoneinten(14%)usesocialmediaastheirmainsource.Theseconsumptionpatternsare
especiallyremarkableintheUSA,acountryinwhich43%ofthepopulationgotnewsfrom
socialmediain2013(ReutersInstitute2013),increasingupto67%in2017(PewResearch
Center2017).
Althoughsomeobservershaveclaimedsocialmediamightpositivelyimpactmediabrands
bydrivingtraffic(NielsenandSchrøder,2014;Ju,JeongandChyi,2014),offeringconsumers
thepossibilitytointeractwithotherreaders(Shuetal.,2017),thealternativehypothesis,i.e.
thatsocialmediaplatformsaresubstitutingmediaoutlets(asasource),seemsequally(ifnot
more)plausible,givingrisetoanambientjournalism(newsisomnipresent),andaperception
thatnewswillfindreaders(Herminda,2010;GildeZuñiga,WeeksandArdèvol‐Abreu,2017).
Thisrisingpopularityofsocialmediaplatformsintermsofnewsconsumptionhasalsoledto
seriousconcernsamongscholarsandlegislatorsaroundtheworldabouttheirpotential
influenceindisseminatinglargevolumesofnon‐supervisedjournalisticcontent(Baumetal.,
2017),empoweringamisinformationphenomenon,(Darnton,2017)andthusprovokingthe
possibilitytomanipulatethepublic’sperceptionofrealitythroughtheviralspreadoffake
news(Gu,etal.,2017).
Thelimited,butgrowingtheoreticalandempiricalresearchonfakenewshaveaddressed
differentdimensionsofthephenomena,suchasitscross‐countryprevalence(Reuters
Institute,2017),the(theoretical)consequencesofitsspread(Guptaetal.,2013)and/orthemain
producers’motivationsfortheircreationanddissemination(AllcottandGentzkow,2017;
Subramanian,2017;Silverman,2016;MarwickandLewis,2017).Theliteraturesuggeststhat
althoughthereisanincreasingawarenessoftheprevalenceoffakenews,theextentofits
impactinEuropeisstillverylimited(ReutersInstitute,2017),thekeymotivationsare
pecuniary(AllcottandGentzkow,2016)butalso(andincreasingly)ideological(Marwickand
Lewis,2017),whilethetheoreticalandpotentiallynegativeconsequencesoftheirspreadpoint

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toamyriadofagentsandsocialcircumstances,suchaspoliticalleadersanddemocratic
societies(Gu,etal.,2017)stockmarkets(Ferraraetal.,2016),healthpolicies(Fernández‐Luque
andBau,2015)citizensincrisissituations(Guptaetal.,2013),and,fundamentally,readers’
interpretationofreality(Cooketal.,2012;Silverman,2016).
Whilethisgrowingliteraturehasgenerallyemphasizedthecentralityoffakenewsproducers
andhowthespreadoffakeinformationleadstoagrowingdifficultyofaudiencesto
distinguishbetweenprofessionalandnon‐professionalnewscontent(Tandocetal.,2017)and
thusmakinghumansmorevulnerabletoonlinemanipulations(Shaoetal.,2017),akey
questionstillremainsunanswered:whatdemographicandsituationalfactorsdriveconsumers
tosharefakenewsonline?Specifically,giventheincreasingimpactoffakenewsonthe
politicalagendaandvoters’decisions(Balmas,2014),wefocusontheprobabilitytoshare
politicalfakenewsthroughsocialmedia.Byaddressingthisgapintheliterature,andbased
onarepresentativeUSAsurveyfromthePewResearchCenter(2017),weaimtocontributeto
amorenuancedunderstandingofthisrecentphenomenabyempiricallyconfirmingor
refutingmanyoftheassumptionsheldinthemediaandpoliticalphoraregardingthe“who”
question,offeringalsosomeindirectinsightsregardingthemainmotivationsforfakenews
sharing.
Concretely,thispaperinvestigateshowdifferentdemographicfactors(sex,age,gender,
politicalorientationandincome)andsituationalpredictors(perceptionoffrequencyof
politicalfakenews,previousonlinefakenewssharing[unnoticed],andperceptionof
responsibilityinpreventingfakenews[ofmembersofthepublic,politiciansandsocial
networkingsites]),affectstheprobabilitytosharepoliticalfakenewsthroughsocialmedia
platforms.Byusingalogisticregressionanalysis,ninemainfindingsemerged:(1)the
probabilityofsharingpoliticalfakenewsonlineishigherinmalesthanfemales;(2)older
peoplearemorelikelytosharepoliticalfakenewsonlinethanyoungerpeople;(3)peoplewith
lowerincomeshavemoreprobabilitytosharepoliticalfakenewsonline;(4)democratvoters
havelessprobabilitytosharepoliticalfakenewsthanindependentvoters(thereisnostatistical
significancebetweendemocratsandrepublicans);(5)peoplewhohaveahighperceptionof
frequencyofonlinefakenewsaremorelikelytosharepoliticalfakenews;(6)peoplewho
inadvertentlyhavesharedfakenewshavelessprobabilitytosharepoliticalfakenewsonline
onpurpose;(7)peoplewhograntgreatresponsibilitytothepublicinpreventingfakenews
storiesfromgainingattentionarelesslikelytosharepoliticalfakenews;(8)peoplewhogrant
greatresponsibilitytosocialnetworkingsitesinpreventingfakenewsstoriesfromgaining
attentionaremorelikelytosharepoliticalfakenewsstoriesand(9)democrat‐femalevoters
arelesslikelytosharepoliticalfakenewsthanmale‐independentvoters.

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2 LITERATURE REVIEW AND RESEARCH QUESTIONS
Inordertoinvestigatethepotentialinfluencethatdifferentdemographicandsituational
factorsmighthaveontheprobabilityofconsumerstosharepoliticalfakenewsviasocial
mediaplatforms,itisimportanttofirstunderstandthemotivationsforproducingfakenews
andthescaleofthisphenomena.Wefirstassessrecentstudiesonfakenewsandaddressthe
relevanceofcommercialandpoliticalinterestsbehindfakenewsproviders,emphasizingthe
keyroleofsocialmediaplatformsascentralspacesoffakenewsamplification.Fromthere,we
turntotheconsequencesofthescaleofonlinefakenewsandtheroleofprofessional
journalismintheirprevention.Finally,welookattheimpactofnewsconsumptionthrough
socialnetworkingsitesandthedemographicsoffakenewssharing.
2.1 Fake news online
AccordingtoCollinsDictionarytheconceptof“fakenews”startedbeingusedonUStelevision
todescribe“false,oftensensational,informationdisseminatedundertheguiseofnews
reporting”.Communicationscholarshavescrutinizeddifferentanglesofthefakenews
phenomena,especiallyaroundthemotivationsfortheirproductionanddissemination(Allcott
andGentzkow,2017;Subramanian,2017)andthepotential(negative)consequencesoftheir
consumption(Ferraraetal.,2016;Silverman,2016;Gu,etal.,2017).Inthisregard,thereisan
academicagreementthatthemainmotivationsbehindfakenewsproductionarecommercial
(chrematisticinterest)andpolitical(ideological)(Hirst,2017).Ontheonehand,the
commercialmotivationsrefertothecreationanddisseminationoffakenewsinorderto
increasethereadershipofanewssite,andgetmoreadvertisingrevenuesasaconsequence
(AllcottandGentzkow,2016).Forinstance,inthe2016USelectionsoneofthemostimportant
fakenewsproviderswascreatedbyteenagersinatowninMacedoniawithnoideological
agendabutrathereconomicincentives(Subramanian,2017;Silverman,2016).Theystatedthat
publishingpro‐Trumpcontentgeneratedthemmoreadvertisingrevenue(Marwickand
Lewis,2017).
Ontheotherhand,thesecondmotivationisideological(AllcottandGentzkow,2017;Gu,
Kropotov,andYarochkin,2017),basedoncampaignsmanipulation,defamationofcandidates
withtheintentofdamagingtheirpublicimage.Inthiscase,theobjectiveoffakenews
providersistoempowerthecandidatetheyfavourthroughfalseinformationthatcanchange
theopinionoftheaudience(AllcottandGentzkow,2017;Gu,etal.,2017).Aclearexampleof
thisphenomenonhappenedonJuly2016whenthewebsitewtoe5news.compublishedan
articleallegingthatPopeFrancissupportedDonaldTrump’spresidentialcandidacy(Allcott
andGentzkow,2017).ThenewswassharedonFacebookmorethanonemilliontimesand
manypeoplebelievedthattheheadlinewastrustworthy.

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Accordingtopreviousstudies,thescaleandinfluenceoffakenewsistiedtothepowerof
socialnetworkstodisseminateitandtheirincreasingroleonnewsconsumption;AsAllcot
andGentzkow(2017:221)pointout:
Onsocialmediathefixedcostsofenteringthemarketandproducingcontentare
vanishinglysmallandtheformatofsocialmediacanmakedifficulttojudgeanarticle’s
veracity.
Recentdataontherelationshipbetweensocialnetworkingsitesandthescaleoffakenews
indicatesthat41,8%ofthefakenewswebsitestrafficcomesfromsocialmedia,while
traditionalandtopnewssitesonlyrepresent10%ofthetotalsharetraffic(Allcottand
Gentzkow,2017).Someobserverslinktheviraldiffusionofdigitalfakeinformationtotherise
ofsocialbots(Shaoetal.,2017).Ferraraetal.(2017:1)observeamassiveincreasein‘social
mediaaccountscontrolledbycomputerscriptsthattrytodisguisethemselvesaslegitimate
humanusers’.Suchfakeaccounts,onFacebookorInstagram,butespeciallyonTwitter,post
content,interactwitheachotherandlegitimateusersviasocialconnections,makinghumans
morevulnerabletoonlinemanipulations(Shaoetal.,2017).AccordingtothePewResearch
Center,74%ofTwitterusersgotnewsthere,whereasbetween9%and15%ofactiveaccounts
onTwitterarebots(Ferraraetal.,2017).InRussia,45%ofTwitteractivityismanagedbyhighly
automatedaccounts(Woolley,andHoward,2017).
Thescaleoffakenewsisgrowingrapidlybecausetheaccessbarrierstoinformation
consumptionhavealmostdisappearedandsocialmediasiteshavebecomeopen,freeand
unrestrictedplatformsfornewssharingandconsumption(AllcottandGentzkow,2017).In
today’smediaenvironment,informationisfree‐floatingontheInternet(Sundar,2008)and
traditionalgatekeeperslikeprofessionaleditorsjournalistsarelargelyabsent(McGrewetal.,
2017;Cooketal.,2012).Thisphenomenongivespeopleahugeresponsibilitytocriticallyself‐
evaluatethereliabilityofonlineinformation(McGrewetal.,2017),generatingagrowing
difficultyfortheaudiencetodistinguishbetweenjournalisticandnon‐journalisticnews
contentandthustocalibratethedifferencebetweenfalseandcorrectinformation(Tandocet
al.,2017).Accordingtorecentmarketresearch,64%ofAmericanssayfabricatednewsstories
causesthemagreatdealofconfusionrelatingtothebasicfactsofcurrentissuesandevents
(PewResearchCenter2016).Thismightleadthemtomakedecisionsagainsttheirown
interests(McGrewetal.,2017).Inthiscontextofnewsuncertainty,thereisacademicconsensus
onthecontinuedimportanceofprofessionaljournalismandfact‐checkinginordertoreduce
theprobabilityofaudiencesbeinginfluencedbymisinformation(Amazeen,2017).
Atthesametime,trustandconfidenceamongstcitizensintraditionalmassmediais
continuouslydecreasing(GoyanesandVara‐Miguel,2017;AllcottandGentzkow,2017).This
lackoftrustinmainstreammediacouldexplaintheincreaseddemandfornewsfromnon‐

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traditionalsources(AllcottandGentzkow,2017).AstudyfromReutersshowsthatamong36
countriesjustone‐thirdoftherespondentsfeltthattheycouldtrustnewsmedia(Reuters
Institute,2017).Peopleprefernewssourcesthatsupporttheirexistingviews[selective
exposure](Cooketal.,2012),whichreflectsthathumansarebiasedinformation‐seekers(Baum
etal.,2017).Becauseofthisprocess,audiencesperceivepartisancontentasmoreinteresting
andinformativethancontentwhichcontradictstheirownideas(Coeetal.,2008).Social
networkslikeFacebookorTwitterareideologicallysegregatedanduserstendtoreadand
sharenewsarticlesalignedwiththeirideologicalposition(Bakshyetal.,2015).Inadditionto
this,readerstrustthesharermorethanwhoproducesthearticle–regardlessofwhetherthe
articleisproducedbyarealnewsorganizationorafictionalone(MediaInsightProject2016).
Whenpeopleseeapostfromatrustedpersontheyfeelmorelikelytorecommendthenews
sourcetofriends(MediaInsightProject2016),andwhentheinformationcomesfroman
unfamiliaroranoppositionsourceitwillusuallybeignored(Baumetal.,2017).
Peoplewhousemoretime‐consumingmedia,arenotonlyolderandtendtohaveahigher
educationallevel,theyalsohavemoreaccuratebeliefsaboutnews(AlcottandGentzkow,
2017).Ontheotherhand,youngsocialmediausers’newsconsumptioncanbedefinedas
“incidental”,becauseforthem,newscomestothemundifferentiatedfromentertainment
informationtheyfindonInternetwhiletheyaresurfingonsocialnetworks(GildeZuñigaet
al.,2017).Ontheotherhand,emotionsareimportantinhowpeoplerespondtoincorrect
politicalmisinformation(Weeks,2015).Unlikeangrypeoplewhoprocessinformationina
partisanmanner,peoplewithanxietyreducetherelianceonpartisanship(Weeks,2015).
Peopleshareinformationthatwillevokeanemotionalresponseinthereceiver,regardlessof
ofwhethertheinformationistrueornot(Cooketal.,2012).Asaresult,newsreadersmight
contributeconsciouslyorunconsciouslytothespreadoffakeinformationbysharingnews
thatmighthavealargerimpactontheironlinesocialconnections(Bartheletal.,2016).
Whenitcomestoideology,Democratsaremorepronetodistinghuishtruefromfakearticles
thanRepublicanswhose‘trustandconfidence’intraditionalmassmediaissharplydecreasing
(AllcottandGentzkow,2017).TheescalationoftheRepublicans’discredittowardstraditional
mediaisreflectedonthecurrentpresidentoftheU.S.,DonaldTrump,whoclaimed146times
inhisTwitterpersonalaccountthatmainstreammediaisasourceoffakeinformationand
newsmanipulation(Hirst,2017).Inthiscase,thetermfakenewsreferstonewshedoesnot
like.DuringtheUSelections2016campaign,peoplewhosupportedTrumpusedtovisitfake
newswebsitesmorethanHillarysupporters(Guessetal.,2018)andonFacebook,therewere
aboutthreetimesmorefakepro‐Trumparticlesthanpro‐Clintonones(AllcottandGentzkow,
2017).Inaddition,Republicans(39%)haveslightlymoreprobabilitiestousesocialmediato
repostcontentrelatedtopoliticalmattersthanDemocrats(34%)(Rainieetal.,2012).

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Despitetherelevanceandtheoreticalramificationsoftheliteratureonfakenews,researchon
thedemographicandsituationalfactorsthatmightinfluencenewsconsumers’probabilityto
sharepoliticalfakenewsonlineisscarce.Paststudieshavemainlyexaminedtheprevalence
ofthephenomena(ReutersInstitute,2017),themotivationsforfakenewscreation(Allcottand
Gentzkow,2017;MarwickandLewis,2017)andtheeffectsoffakenewsdisseminationon
society,politicalleaders,stockmarkets,etc(Silverman,2016;Ferraraetal.,2016;Guetal.,2017).
However,giventheincreasingimpactoffakenewsonthepoliticalagendaandvoters’
decisions(Balmas,2014)andtherelevanceofsocialmediaplatformsintheamplificationof
themisinformationphenomenon,thisstudyfocusesontheconsumerimpactonfakenews
disseminationandanalysesthedemographicandsituationalfactorsbehindthereachof
politicalfakenews.Therefore,thisstudyexploresthefollowingresearchquestions(RQ):
 RQ1.Howdodemographics(sex,age,gender,politicalorientationandincome),affect
theprobabilitytosharepoliticalfakenewsonline?
 RQ2.Whatistheinteractionbetweenpoliticalorientationandgender?
 RQ3.Howdoestheperceptionoffrequencyofpoliticalfakenewsonlineaffectthe
probabilitytosharethem?
 RQ4.Howdoespreviousonlinefakenewssharing(unnoticed),affecttheprobability
tosharepoliticalfakenewsonline(onpurpose)?
 RQ5.Howdoestheperceptionofresponsibilityof1)membersofthepublic,2)
government,politiciansandelectedofficialsand3)FacebookandTwitter,intryingto
preventfakenewsaffecttheprobabilitytosharethemonline?
3 METHODOLOGY
TheanalysisinthisstudyisbasedonaPewResearchCentersurveyconductedDec.1through
Dec.4,2016,amonganationalrepresentativesampleof1,002adults,18yearsofageorolder,
livinginthecontinentalUnitedStates3.TheresultsofthestudycarriedoutbythePew
ResearchCenter(Journalism&Media)aredescriptive.Thatis,onlydescriptivestatisticsare
usedtoexplorethegeneralsituationofU.S.fakenewswithoutadvancinganyinferential
analysis.Theanalysisperformedandtheresultsobtainedandexposedherearenew,except
forthedescriptive(raw)datainrelationtothedependentvariableitself.

3Tohavemoreinformationaboutthemethodologyconsultsthefollowingweb:
http://www.pewresearch.org/methodology/u‐s‐survey‐research/


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Themodelconstructedisbasedonabinomiallogisticregression,analyzingtheprobabilityof
sharingpoliticalfakenewsasadependentvariable.Logisticregressionteststheprobabilityof
adichotomouseventoccurring—inthiscase,sharingornotpoliticalfakenews.Thepredicted
proportionofactivitiesfollowsthelogisticmodeloflnP/(1

Pi)=βXi,wherePiisthe
probabilityofsharingpoliticalfakenews.Allpredictorvariableswereintroducedfive
differentblocks:demographics,frequencyofpoliticalfakenews,unnoticedfakenews,
perceptionofresponsibility,andinteractionbetweenpoliticsandgender.
3.1 Variables and Measurements
Dependentvariable:Thedependentvariable,theprobabilitytosharepoliticalfakenews,was
measuredbyaskingparticipantsthefollowingquestion:“Haveyoueversharedapolitical
newsstoryonlinethatyouthoughtatthetimewasmadeup?(0)Noand(1)yes.
Independentvariables:Dataforthedemographicvariables(i.e.,gender,age,income,education
andpoliticalorientation)werecollectedusingstandardsurveymeasurements.Theperception
offrequencyofpoliticalfakenewswasmeasuredbyaskingparticipantsthefollowing
question:“Howoftendoyoucomeacrossnewsstoriesonlinethatyouthinkarealmost
completelymadeup”,onafour‐pointLikertscale,rangingfrom1)never,2)hardlyever,3)
sometimesand,4)often.Unnoticedfakenewssharingwasmeasuredbyaskingparticipants
thefollowingquestion:“Haveyoueversharedapoliticalnewsstoryonlinethatyoulater
foundoutwasmadeup?(0)Noand(1)yes.Theperceptionofresponsibilitywasmeasured
byaskingparticipantsthefollowingquestion:“Asyoumayhaveheard,therehaverecently
beensomeinstancesofsocalled“fakenewsstories”circulatingwidelyonline.Howmuch
responsibilitydoeseachofthefollowinghaveintryingtopreventmadeupstoriesfrom
gainingattention”a)membersofthepublic,b)thegovernment,politicians,andelected
officials,c)socialnetworkingsiteslikeFacebookandTwitter,andsearchsiteslikeGoogle,on
afour‐pointLikertscale,rangingfrom:1)noresponsibilityatall,2)notmuchresponsibility,
3)afairamountofresponsibility,4)agreatdealofresponsibility.
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4 RESULTS
4.1 Descriptive statistics
Thesampleof1,002U.S.residentsincludedslightlymorewomen(51,3%)thanmen(48.7%),
withanagerangebetween18and99(M=47.22;SD=18.22).Mostparticipants(50,7%)
declaredthey“often”cameacrossnewsstoriesonlinethattheythinkwerealmostcompletely
madeup,27,1%declared“sometimes”,8,4%“hardlyever”andanegligible10,4%declared
they“never”cameacrosspoliticalfakenews.Withregardtothedependentvariable,13.8%of
thetotalsampledeclaredtheysharedpoliticalfakenews,whilethevastmajority,thatis,85.5%
ofparticipantsdeclaredtheyneversharedpoliticalfakenewsonline.Thecorrelationanalysis
revealedsomeimportantassociations.Interestingtonote,forexample,arethenegative
associationsbetweenageandfakenewsfrequencyandpublicresponsibility,whileapositive
statisticallyassociationwithgovernmentresponsibility.Ontheotherhand,ourcorrelation
analysisrevealedastatisticallysignificantandpositiveassociationbetweeneducationand
fakenewsfrequency,publicresponsibilityandSNSresponsibility.Thesamepatternsare
appliedwithregardtoincome.
Table 1. Correlations between quantitative variables
4.2 Probability to share political fake news online: Logistic regression
analysis
Thelogisticregressionanalysisresultsrevealedtherelationshipbetweentheprobabilityto
sharepoliticalfakenewsandsomepredictorvariables(p<.05)Thefirstmodel
(demographics),accountedfor4.4%or2.5%ofthevarianceintheprobabilitytosharepolitical
fakenews(NagelkerkeR2andCox&SnellR2),thesecondmodel(perceptionoffrequencyof
politicalfakenews),accountedfor4.7%or2.7%ofthevariance,thethirdmodel(unnoticed
fakenews),accountedfor23.5%or13.3%ofthevariance,thefourthmodel(perceptionof
responsibility),accountedfor24.1%or13.6%ofthevariance,andthefifthmodel(interaction
AgeEduc.Inc.FakeNews
Freq
Resp.(1)Resp.(2)Resp.(3)
Age1 ‐,010,008 ‐,092** ‐,060**,097**,031
Education ‐,0101,507**,114**,148** ‐,031,048**
Income,008,507**1,190**,115**,001,088**
FNFreq. ‐,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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betweenpoliticsandgender),accountedfor24.1%or13.6%ofthevariance.Thesefindings
thussuggestconsiderableexplanatorypower.
Withregardtothefirstresearchquestion,whichaddressedtheassociationbetween
demographicvariablesandtheprobabilitytosharepoliticalfakenews;age,gender,income,
educationandpoliticalorientationwereenteredinthefirstblockoftheanalysis.Thelogistic
regressionresultsrevealedastatisticallysignificantassociation(p<.05)withgender,age,
income,andpoliticalorientation.Therefore,theprobabilityofsharingpoliticalfakenews(p=
<.05;β=‐,695;,499ex)washigherinmalesthanfemales.Thelogisticregressionalsoidentified
ageasastatisticallysignificantandpositivepredictor;thatis,theprobabilityofsharing
politicalfakenews(p=<.05;β=,013;1,013ex)increaseswithage.However,incomewasa
negativepredictor(p=<.05;β=‐,073;,930ex),insuchawaythatpeoplewithlowerincome
havemoreprobabilitytosharepoliticalfakenews.Finally,theregressionanalysisrevealeda
significantandnegativeassociationbetweenpoliticalorientationandtheprobabilitytoshare
politicalfakenews.Specifically,democratvoters(p=<.05;β=‐,237;,789ex)haveless
probabilitytosharepoliticalfakenewsthatindependentvoters(thereisnostatistical
significancebetweendemocratsandrepublicans).
Thesecondresearchquestionaskedwhethertheperceptionoffrequencyofonlinefakenews
wouldpredicttheprobabilitytosharepoliticalfakenews.Inadditiontogender(p=<.05;β=
‐,701;,496ex),age(p=<.05;β=,014;1,014ex),andincome(p=<.05;β=‐,081;,923ex),the
perceptionofonlinefakenewsfrequency(p=<.05;β=,125;1,133ex)wasasignificant
predictor.Hence,theincreasingperceptionofonlinefakenewsincreasestheprobabilityto
sharepoliticalfakenews.Thethirdresearchquestionaskedwhetherpreviouslyunnoticed
fakenewssharingwouldpredicttheprobabilitytosharepoliticalfakenews.Inadditionto
gender(p=<.05;β=‐,800;,449ex),age(p=<.05;β=,011;1,011ex),income(p=<.05;β=‐,072;
,931ex),andpolitics(p=<.05;β=‐,296;,744ex),unnoticedfakenewssharing(p=<.05;β=‐
2,210;,110ex),wasasignificantandnegativepredictorofpoliticalfakenewssharingonline.
Therefore,peoplewhohaveinadvertentlysharedfakenewshavelessprobabilitytoshare
politicalfakenewsonline.
Thefourthresearchquestionaskedwhethertheperceptionofresponsibilityofdifferentsocial
stakeholdersintryingtopreventfakenewscouldpredicttheprobabilitytosharepoliticalfake
news.Inadditiontogender(p=<.05;β=‐,805;,447ex),age(p=<.05;β=,010;1,010ex),income
(p=<.05;β=‐,068;,934ex),politics(p=<.05;β=‐,327;,721ex),andunnoticedsharingfake
news(p=<.05;β=‐2,24;,106ex),publicresponsibility(p=<.05;β=‐,132;,876ex),andsocial
networkingsitesresponsibility(p=<.05;β=,161;1,174ex),werefoundtobesignificant
predictorsofpoliticalfakenewssharing.
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Table2.Logisticregressionanalysis.*p<0,05;**p</0,01

Model1Model2Model3Model4Model5
BExp(B)BExp(B)BExp(B)BExp(B)BExp(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,0211,021 ‐,017,983 ‐,027,973 ‐,040,961
Politics(2) ‐,237*,789 ‐,212,809 ‐,296*,744 ‐,327*,721 ‐,122,885
FNFreq   ,125*1,133 ‐,031,969 ‐,059,943 ‐,065,937
UnnoticedFN     ‐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
NagelkerkeR2,044  ,047  ,235  ,241  ,243 
Cox&SnellR2,025  ,027  ,133  ,136  ,137 
Chi266,89**  4,26*  306,9**  9,76*  3,625 
Loglikelihood2146,0  2141,8  1834,8  1825,0  1821,4 
No.ofobservations1.002  1.002  1.002  1.002  1.002 

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Withregardtopublicresponsibility,thelogisticregressionanalysisrevealedastatistically
significantandnegativerelationwithpoliticalfakenewssharing.Therefore,peoplewhogrant
greatresponsibilitytothepublicinpreventingfakenewsstoriesfromgainingattentionare
lesslikelytosharepoliticalfakenews.However,whenitcomestosocialnetworkingsites,the
logisticregressionrevealedastatisticallysignificantbutpositiveassociation,insuchaway
thatpeoplewhograntgreatresponsibilitytosocialnetworkingsitesinpreventingfakenews
storiesfromgainingattentionaremorelikelytosharepoliticalfakenewsstories.
Thefifthresearchquestionaskedwhethertheinteractionbetweengenderandpolitical
orientationcouldpredicttheprobabilitytosharepoliticalfakenewsonline.Inadditionto
gender(p=<.05;β=;ex),age(p=<.05;β=;ex),income(p=<.05;β=;ex),unnoticedfake
newssharing(p=<.05;β=;ex),andsocial(p=<.05;β=;ex),andsocialnetworkingsites
responsibility(p=<.05;β=;ex),theinteractionbetweengender(1)andpolitics(1),werefound
tobeasignificantpredictoroftheprobabilitytosharepoliticalfakenewsonline(p=<.05;β=
;ex).Therefore,democrat‐femalevotersarelesslikelytosharepoliticalfakenewsthanmale‐
independentvoters.
5 CONCLUSION
Thispaperinvestigateshowdifferentdemographicfactors(age,gender,politicalorientation
andincome)andsituationalpredictors(perceptionoffrequencyofpoliticalfakenews,
previousonlinefakenewssharing[unnoticed],andperceptionofresponsibilityinpreventing
fakenews[ofmembersofthepublic,politiciansandsocialnetworkingsites]),affectthe
probabilitytosharepoliticalfakenewsthroughsocialmediaplatforms.First,ourresearch
addressestherelevantroleofdemographicvariablesinexplainingthevarianceofpolitical
fakenewssharing.Resultsofthelogisticregressionanalysisprovidesstrongevidence
regardingthekeyroleofage,gender,incomeandpoliticalorientationinthespreadofpolitical
misinformationonline.Inthisregard,theprobabilityofsharingpoliticalfakenewsonlineis
higherinmalesthanfemales,despitethefactthatwomenusesocialmediamorethanmen
(Krasnovaetal.,2017).Inaddition,ouranalysisalsorevealsthatsharingpoliticalfakenews
increaseswithage,despitethefactthatyoungpeoplearethemajorityofinternetusers
(McGrewetal,2017).Peopletendtoshowanincreasinginterestinnewsastheygetolder.For
youngerusers,topicslikedomesticpolitics,internationalpoliticsandeconomyareseenasless
interesting(Costera,2007),whichmightexplainwhyolderpeoplearemorelikelytoshare
politicalfakenewsonline.
Peoplewithlowincomesarealsomorelikelytosharepoliticalfakenews.Previousstudies
suggestthateducatedpeopleearnmoremoneythatpeoplethathavenotaccessedtheschool
system(DienerandBiswas‐Diener,2002)whereeducationincreasespeople’scapacitiesto

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differentiatefactfromfictionand,also,offerpeoplebettermeanstocounterargueinconsistent
information(AllcottandGentzkow,2017).Whenitcomestoideology,democratvotersare
lesslikelytosharepoliticalfakenewsthanindependentvoters.However,thereisnostatistical
differenceintheprobabilitytosharepoliticalfakenewsbetweendemocratsandrepublicans,
despitethefactthatdemocratshavesomewhatlessprobability(34%)torepostnewsrelated
topoliticalaffairsthatwerepreviouslypostedbyotherpeopleonsocialmediathan
republicans(39%)(Rainieetal.,2012).
Resultsofthelogisticregressionrevealthattheincreasingperceptionofonlinefakenews
increasestheprobabilitytosharepoliticalfakenews.Nowadays,informationontheinternet
isreplacingprofessionaljournalismandexpertadvice(Cooketal.,2012)andabout40%ofthe
fakenewswebsitestrafficcomesfromsocialmediaplatforms(AllcottandGentzkow,2017).
Inaddition,peoplewhogetnewsfromsocialmediareadinformationthatisideologically
aligned(AllcottandGentzkow,2017)andmanyareunawareoftheeffectsofsocialmedia
manipulation(Glenski,andWeninger,2016).Furthermore,64%ofAmericansaffirmthatfake
newshasgeneratedthemagreatdealofconfusionaboutbasicfacts,and24%ofU.S.citizens
affirmthatthisphenomenonhasprovokedthemsomeconfusion(Bartheletal.,2016).Ina
contextwherefakenewsiseasilyspreadandconsumedthroughthemostimportantsocial
networks,theperceptionoftheirscalepositivelyaffectspeople’ssharingbehaviour.
Ouranalysissuggeststhatpeoplewhoinadvertentlysharefakenewshavelessprobabilities
tosharepoliticalfakenewsonline.Formanypeopleitiscomplicatedtorecognizethatapiece
ofinformationisfalseuntiltheyreceiveacorrection(Cooketal.,2012).Warningsseemtobe
effectivebecausetheyinducepeopleinatemporarystateofscepticism,increasingtheir
capacitiestodifferentiatebetweentrueandfalseinformation(Cooketal,2012).Therefore,
peoplewhograntgreatresponsibilitytothepublicinpreventingfakenewsstoriesfrom
gainingattentionarelesslikelytosharepoliticalfakenews.However,whenitcomestosocial
networkingsites,thelogisticregressionrevealedastatisticallysignificantbutpositive
association,insuchawaythatpeoplewhogranttheresponsibilitytopreventfakenewsstories
fromgainingattentiontosocialnetworkingsitesarethemselvesmorelikelytosharepolitical
fakenewsstories.Finally,ourstatisticalanalysisshowsaninteractionbetweenpolitical
orientationandgender.Tothisregard,democrat‐femalevotersarelesslikelytosharepolitical
fakenewsthanmale‐independentvoters.
Inconclusion,thecentraltheoreticalimplicationsemanatingfromtheobservationsmadein
thisarticleinclude(1)ashiftinthepointofviewbehindfakenewsdissemiantion,fromthe
importanceofproducerstotherelevanceofconsumers,(2)thesignificanceofdemographic
andsituationalfactorsinexplainingpoliticalfakenewssharingbehaviour,and(3)the
increasinglydecisive/complexroleoftraditionalandnewmediatocontrolthejournalisticflow

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ontheInternet,whereacombinationofdigitaltoolsandconsumers’behaviour,might
challengetheethosofjournalismandtheveracyofinformationonline.Inthiscontext,
professionaljournalismandfact‐checkingareincreasinglyimportanttomitigate,controland
discoverpoliticalfakenewsonlineandtolessentheirpotentialdamagetodemocratic
societies.
6 LIMITATIONS
Severallimitationsofthecurrentanalysisarenoteworthy.First,itisworthnotingthatwith
respecttopolitical‘fakenews’theAmericancontextisdifferenttoothercountries.Thereare
particularhistoric,socialandotherfactors,especiallyinthecurrenthighlypolarisedUS
politicalandmediacontextthatappearstomakemisinformationmoreeffectivethaninother
demoi.Second,thedatasetbeingfromthehighpointofpoliticalpolarisationjustafteroneof
themostdivisivepresidentialcampaignsever,shouldbetakenintoaccountwhen
generalizingthefindings.Third,thecross‐sectionalnatureofthesurveydatadoesnotallow
ustoidentifywithcertaintythedirectionofthecausalpatternsunderlyingthecorrelations
thatwefound.Therefore,wecannotruleoutthepossibilitythatthecausalordersarereversed.
Morerobustcausalclaimswouldbewarrantedbylongitudinalorexperimentalratherthan
cross‐sectionalsurveydataandmoreworkisneededtodisentanglethecausalmechanisms
behindthecorrelationspresentedhere.Thus,therelationshipstheorizedinthisworking
papersshouldbeinterpretedwithcaution.Futureresearchmayadoptalongitudinaldesign
todrawcausalinferenceswithgreaterconfidence.Furthermore,allvariablesweremeasured
usingasingleitem,whichprohibitsreliabilityassessments.Aswithanysurvey,wealsorelied
onself‐reportedmeasuresofonlinebehavior,whichrequiredsubjectiveassessmentsofthe
frequencyoffakenewssharing.
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

Media@LSEWorkingPaperSeries
TheMedia@LSEWorkingPaperSerieswill:
 Presenthighqualityresearchandwriting(includingresearchin‐progress)toawideaudienceofacademics,policy‐
makersandcommercial/mediaorganisations.
 Settheagendainthebroadfieldofmediaandcommunicationstudies.
 Stimulateandinformdebateandpolicy.AllpaperswillbepublishedelectronicallyasPDFfiles,subjecttoreview
andapprovalbytheEditorsandwillbegivenanISSN.
 Anadvantageoftheseriesisaquickturnaroundbetweensubmissionandpublication.Authorsretaincopyright,
andpublicationheredoesnotprecludethesubsequentdevelopmentofthepaperforpublicationelsewhere.
 TheEditoroftheseriesisBartCammaerts.TheDeputyEditorsareNickAnsteadandRichardStupart.Theeditorial
boardismadeupofotherLSEacademicsandfriendsofMedia@LSEwithawiderangeofinterestsininformation
andcommunicationtechnologies,themediaandcommunicationsfromavarietyofdisciplinaryperspectives
(includingeconomics,geography,law,politics,sociology,politicsandinformationsystems,cultural,genderand
developmentstudies).
Notesforcontributors:
Contributorsareencouragedtosubmitpapersthataddressthesocial,political,economicandculturalcontextofthemedia
andcommunication,includingtheirforms,institutions,audiencesandexperiences,andtheirglobal,national,regionaland
localdevelopment.Papersaddressinganyofthethemesmentionedbelowarewelcome,butotherthemesrelatedtomedia
andcommunicationarealsoacceptable:
 CommunicationandDifference
 GlobalisationandComparativeStudies
 Innovation,GovernanceandPolicy
 Democracy,PoliticsandJournalismEthics
 MediationandResistance
 MediaandIdentity
 MediaandNewMediaLiteracies
 TheCulturalEconomy
ContributionsarewelcomedfromacademicsandPhDstudents.IntheAutumnTermwealsoinviteselectedMaster’s
studentsfromtheprecedingyeartosubmittheirdissertationswhichwillbehostedinaseparatepartofthissiteas
‘dissertations’ratherthanasWorkingPapers.Contributorsshouldbearinmindwhentheyarepreparingtheirpaperthat
itwillbereadonline.
Papersshouldconformtothefollowingformat:
6,000‐10,000words(excludingbibliography,includingfootnotes)
150‐200wordabstract
Headingsandsub‐headingsareencouraged
TheHarvardsystemofreferencingshouldbeused
PapersshouldbepreparedasaWordfile
Graphs,picturesandtablesshouldbeincludedasappropriateinthesamefileasthepaper
ThepapershouldbesentbyemailtoBartCammaerts(b.cammaerts@lse.ac.uk),theeditoroftheMedia@LSEWorking
PaperSeries

ISSN:1474‐1938/1946
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