Accounts that spread fake news are mostly unverified, recently created and have on average high friends/followers ratio
ABSTRACTIVE
have a larger proportion of friends/followers (i.e. they have, on average, the same number of friends but a smaller number of followers) than those spreading viral content only
EXTRACTIVE
We found that 82 users within our sample were spreading fake news (i.e. they produced at least one tweet which was labelled as fake news). Out of those, 34 had verified accounts, and the rest were unverified. From the 48 unverified accounts, 6 have been suspended by Twitter at the date of writing, 3 tried to imitate legitimate accounts of others, and 4 accounts have been already deleted. Figure FIGREF28 shows the proportion of verified accounts to unverified accounts for viral tweets (containing fake news vs. not containing fake news). From the chart, it is clear that there is a higher chance of fake news coming from unverified accounts. A useful representation for friends and followers is the ratio between friends/followers. Figures FIGREF31 and FIGREF32 show this representation. Notice that accounts spreading viral tweets with fake news have, on average, a larger ratio of friends/followers. The distribution of those accounts not generating fake news is more evenly distributed. Figure FIGREF24 shows that, in contrast to other kinds of viral tweets, those containing fake news were created more recently. As such, Twitter users were exposed to fake news related to the election for a shorter period of time.
Accounts spreading fake news appear to have a larger proportion of friends/followers (i.e. they have, on average, the same number of friends but a smaller number of followers) than those spreading viral content only. Together with the fact that, on average, tweets containing fake news have more URLs than those spreading viral content, it is possible to hypothesize that, both, the ratio of friends/followers of the account producing a viral tweet and number of URLs contained in such a tweet could be useful to single-out fake news in Twitter. Not only that, but our finding related to the number of URLs is in line with intuitions behind the incentives to create fake news commonly found in the literature BIBREF9 (in particular that of obtaining revenue through click-through advertising).
1712.05999
10 pt </s> 1 . 10 pt </s> [ ĠCharacter izing ĠPolitical ĠFake ĠNews Ġin ĠTwitter Ġby Ġits ĠMeta - Data Jul io ĠAm ador ĠD ÃŃ az ĠL Ã³ pez Ax el ĠO eh mic hen ĠMiguel ĠMol ina - Sol ana ( Ġj . am ador , Ġax elf ran co is . oe h mic hen 11 , Ġmmol inas @ im perial . ac . uk Ġ) ĠImperial ĠCollege ĠLondon ĠThis Ġarticle Ġpresents Ġa Ġpreliminary Ġapproach Ġtowards Ġcharacter izing Ġpolitical Ġfake Ġnews Ġon ĠTwitter Ġthrough Ġthe Ġanalysis Ġof Ġtheir Ġmeta - data . ĠIn Ġparticular , Ġwe Ġfocus Ġon Ġmore Ġthan Ġ1 . 5 M Ġtweets Ġcollected Ġon Ġthe Ġday Ġof Ġthe Ġelection Ġof ĠDonald ĠTrump Ġas Ġ45 th Ġpresident Ġof Ġthe ĠUnited ĠStates Ġof ĠAmerica . ĠWe Ġuse Ġthe Ġmeta - data Ġembedded Ġwithin Ġthose Ġtweets Ġin Ġorder Ġto Ġlook Ġfor Ġdifferences Ġbetween Ġtweets Ġcontaining Ġfake Ġnews Ġand Ġtweets Ġnot Ġcontaining Ġthem . ĠSpecifically , Ġwe Ġperform Ġour Ġanalysis Ġonly Ġon Ġtweets Ġthat Ġwent Ġviral , Ġby Ġstudying Ġproxies Ġfor Ġusers ' Ġexposure Ġto Ġthe Ġtweets , Ġby Ġcharacter izing Ġaccounts Ġspreading Ġfake Ġnews , Ġand Ġby Ġlooking Ġat Ġtheir Ġpolarization . ĠWe Ġfound Ġsignificant Ġdifferences Ġon Ġthe Ġdistribution Ġof Ġfollowers , Ġthe Ġnumber Ġof ĠURLs Ġon Ġtweets , Ġand Ġthe Ġverification Ġof Ġthe Ġusers . </s> ] </s> Introduction </s> While Ġfake Ġnews , Ġunderstood Ġas Ġdeliberately Ġmisleading Ġpieces Ġof Ġinformation , Ġhave Ġexisted Ġsince Ġlong Ġago Ġ( e . g . Ġit Ġis Ġnot Ġunusual Ġto Ġreceive Ġnews Ġfalsely Ġclaiming Ġthe Ġdeath Ġof Ġa Ġcelebrity ), Ġthe Ġterm Ġreached Ġthe Ġmainstream , Ġparticularly Ġso Ġin Ġpolitics , Ġduring Ġthe Ġ2016 Ġpresidential Ġelection Ġin Ġthe ĠUnited ĠStates ĠB IB REF 0 Ġ. ĠSince Ġthen , Ġgovernments Ġand Ġcorporations Ġalike Ġ( e . g . ĠGoogle ĠB IB REF 1 Ġand ĠFacebook ĠB IB REF 2 Ġ) Ġhave Ġbegun Ġefforts Ġto Ġtackle Ġfake Ġnews Ġas Ġthey Ġcan Ġaffect Ġpolitical Ġdecisions ĠB IB REF 3 Ġ. ĠYet , Ġthe Ġability Ġto Ġdefine , Ġidentify Ġand Ġstop Ġfake Ġnews Ġfrom Ġspreading Ġis Ġlimited . </s> Since Ġthe ĠObama Ġcampaign Ġin Ġ2008 , Ġsocial Ġmedia Ġhas Ġbeen Ġpervasive Ġin Ġthe Ġpolitical Ġarena Ġin Ġthe ĠUnited ĠStates . ĠStudies Ġreport Ġthat Ġup Ġto Ġ62 % Ġof ĠAmerican Ġadults Ġreceive Ġtheir Ġnews Ġfrom Ġsocial Ġmedia ĠB IB REF 4 Ġ. ĠThe Ġwide Ġuse Ġof Ġplatforms Ġsuch Ġas ĠTwitter Ġand ĠFacebook Ġhas Ġfacilitated Ġthe Ġdiffusion Ġof Ġfake Ġnews Ġby Ġsimpl ifying Ġthe Ġprocess Ġof Ġreceiving Ġcontent Ġwith Ġno Ġsignificant Ġthird Ġparty Ġfiltering , Ġfact - checking Ġor Ġeditorial Ġjudgement . ĠSuch Ġcharacteristics Ġmake Ġthese Ġplatforms Ġsuitable Ġmeans Ġfor Ġsharing Ġnews Ġthat , Ġdisguised Ġas Ġlegit Ġones , Ġtry Ġto Ġconfuse Ġreaders . </s> Such Ġuse Ġand Ġtheir Ġprominent Ġrise Ġhas Ġbeen Ġconfirmed Ġby ĠCraig ĠSilver man , Ġa ĠCanadian Ġjournalist Ġwho Ġis Ġa Ġprominent Ġfigure Ġon Ġfake Ġnews ĠB IB REF 5 Ġ: ĠâĢ ľ In Ġthe Ġfinal Ġthree Ġmonths Ġof Ġthe ĠUS Ġpresidential Ġcampaign , Ġthe Ġtop - performing Ġfake Ġelection Ġnews Ġstories Ġon ĠFacebook Ġgenerated Ġmore Ġengagement Ġthan Ġthe Ġtop Ġstories Ġfrom Ġmajor Ġnews Ġoutlet âĢ Ŀ . </s> Our Ġcurrent Ġresearch Ġhence Ġdep arts Ġfrom Ġthe Ġassumption Ġthat Ġsocial Ġmedia Ġis Ġa Ġconduit Ġfor Ġfake Ġnews Ġand Ġasks Ġthe Ġquestion Ġof Ġwhether Ġfake Ġnews Ġ( as Ġspam Ġwas Ġsome Ġyears Ġago ) Ġcan Ġbe Ġidentified , Ġmod elled Ġand Ġeventually Ġblocked . ĠIn Ġorder Ġto Ġdo Ġso , Ġwe Ġuse Ġa Ġsample Ġof Ġmore Ġthat Ġ1 . 5 M Ġtweets Ġcollected Ġon ĠNovember Ġ8 th Ġ2016 ĠâĢĶ election Ġday Ġin Ġthe ĠUnited ĠStates âĢĶ Ġwith Ġthe Ġgoal Ġof Ġidentifying Ġfeatures Ġthat Ġtweets Ġcontaining Ġfake Ġnews Ġare Ġlikely Ġto Ġhave . ĠAs Ġsuch , Ġour Ġpaper Ġaims Ġto Ġprovide Ġa Ġpreliminary Ġcharacterization Ġof Ġfake Ġnews Ġin ĠTwitter Ġby Ġlooking Ġinto Ġmeta - data Ġembedded Ġin Ġtweets . ĠConsidering Ġmeta - data Ġas Ġa Ġrelevant Ġfactor Ġof Ġanalysis Ġis Ġin Ġline Ġwith Ġfindings Ġreported Ġby ĠMorris Ġet Ġal . ĠB IB REF 6 Ġ. ĠWe Ġargue Ġthat Ġunderstanding Ġdifferences Ġbetween Ġtweets Ġcontaining Ġfake Ġnews Ġand Ġregular Ġtweets Ġwill Ġallow Ġresearchers Ġto Ġdesign Ġmechanisms Ġto Ġblock Ġfake Ġnews Ġin ĠTwitter . </s> Specifically , Ġour Ġgoals Ġare : Ġ1 ) Ġcompare Ġthe Ġcharacteristics Ġof Ġtweets Ġlabelled Ġas Ġcontaining Ġfake Ġnews Ġto Ġtweets Ġlabelled Ġas Ġnot Ġcontaining Ġthem , Ġ2 ) Ġcharacterize , Ġthrough Ġtheir Ġmeta - data , Ġviral Ġtweets Ġcontaining Ġfake Ġnews Ġand Ġthe Ġaccounts Ġfrom Ġwhich Ġthey Ġoriginated , Ġand Ġ3 ) Ġdetermine Ġthe Ġextent Ġto Ġwhich Ġtweets Ġcontaining Ġfake Ġnews Ġexpressed Ġpolarized Ġpolitical Ġviews . </s> For Ġour Ġstudy , Ġwe Ġused Ġthe Ġnumber Ġof Ġret we ets Ġto Ġsingle - out Ġthose Ġthat Ġwent Ġviral Ġwithin Ġour Ġsample . ĠTwe ets Ġwithin Ġthat Ġsubset Ġ( v iral Ġtweets Ġhereafter ) Ġare Ġvaried Ġand Ġrelate Ġto Ġdifferent Ġtopics . ĠWe Ġconsider Ġthat Ġa Ġtweet Ġcontains Ġfake Ġnews Ġif Ġits Ġtext Ġfalls Ġwithin Ġany Ġof Ġthe Ġfollowing Ġcategories Ġdescribed Ġby ĠRubin Ġet Ġal . ĠB IB REF 7 Ġ( see Ġnext Ġsection Ġfor Ġthe Ġdetails Ġof Ġsuch Ġcategories ): Ġserious Ġfabrication , Ġlarge - scale Ġhoax es , Ġjokes Ġtaken Ġat Ġface Ġvalue , Ġsl anted Ġreporting Ġof Ġreal Ġfacts Ġand Ġstories Ġwhere Ġthe Ġtruth Ġis Ġcontentious . ĠThe Ġdataset ĠB IB REF 8 Ġ, Ġmanually Ġlabelled Ġby Ġan Ġexpert , Ġhas Ġbeen Ġpublicly Ġreleased Ġand Ġis Ġavailable Ġto Ġresearchers Ġand Ġinterested Ġparties . </s> From Ġour Ġresults , Ġthe Ġfollowing Ġmain Ġobservations Ġcan Ġbe Ġmade : </s> Our Ġfindings Ġresonate Ġwith Ġsimilar Ġwork Ġdone Ġon Ġfake Ġnews Ġsuch Ġas Ġthe Ġone Ġfrom ĠAll cot Ġand ĠGent z k ow ĠB IB REF 9 Ġ. ĠTherefore , Ġeven Ġif Ġour Ġstudy Ġis Ġa Ġpreliminary Ġattempt Ġat Ġcharacter izing Ġfake Ġnews Ġon ĠTwitter Ġusing Ġonly Ġtheir Ġmeta - data , Ġour Ġresults Ġprovide Ġexternal Ġvalidity Ġto Ġprevious Ġresearch . ĠMoreover , Ġour Ġwork Ġnot Ġonly Ġstresses Ġthe Ġimportance Ġof Ġusing Ġmeta - data , Ġbut Ġalso Ġunderscores Ġwhich Ġparameters Ġmay Ġbe Ġuseful Ġto Ġidentify Ġfake Ġnews Ġon ĠTwitter . </s> The Ġrest Ġof Ġthe Ġpaper Ġis Ġorganized Ġas Ġfollows . ĠThe Ġnext Ġsection Ġbriefly Ġdiscusses Ġwhere Ġthis Ġwork Ġis Ġlocated Ġwithin Ġthe Ġliterature Ġon Ġfake Ġnews Ġand Ġcontextual izes Ġthe Ġtype Ġof Ġfake Ġnews Ġwe Ġare Ġstudying . ĠThen , Ġwe Ġpresent Ġour Ġhypotheses , Ġthe Ġdata , Ġand Ġthe Ġmethodology Ġwe Ġfollow . ĠFinally , Ġwe Ġpresent Ġour Ġfindings , Ġconclusions Ġof Ġthis Ġstudy , Ġand Ġfuture Ġlines Ġof Ġwork . </s> Def ining ĠFake Ġnews </s> Our Ġresearch Ġis Ġconnected Ġto Ġdifferent Ġstrands Ġof Ġacademic Ġknowledge Ġrelated Ġto Ġthe Ġphenomenon Ġof Ġfake Ġnews . ĠIn Ġrelation Ġto ĠComputer ĠScience , Ġa Ġrecent Ġsurvey Ġby ĠCon roy Ġand Ġcolleagues ĠB IB REF 10 Ġidentifies Ġtwo Ġpopular Ġapproaches Ġto Ġsingle - out Ġfake Ġnews . ĠOn Ġthe Ġone Ġhand , Ġthe Ġauthors Ġpointed Ġto Ġlinguistic Ġapproaches Ġconsisting Ġin Ġusing Ġtext , Ġits Ġlinguistic Ġcharacteristics Ġand Ġmachine Ġlearning Ġtechniques Ġto Ġautomatically Ġflag Ġfake Ġnews . ĠOn Ġthe Ġother , Ġthese Ġresearchers Ġundersc ored Ġthe Ġuse Ġof Ġnetwork Ġapproaches , Ġwhich Ġmake Ġuse Ġof Ġnetwork Ġcharacteristics Ġand Ġmeta - data , Ġto Ġidentify Ġfake Ġnews . </s> With Ġrespect Ġto Ġsocial Ġsciences , Ġefforts Ġfrom Ġpsychology , Ġpolitical Ġscience Ġand Ġsociology , Ġhave Ġbeen Ġdedicated Ġto Ġunderstand Ġwhy Ġpeople Ġconsume Ġand / or Ġbelieve Ġmisinformation ĠB IB REF 11 Ġ, ĠB IB REF 12 Ġ, ĠB IB REF 13 Ġ, ĠB IB REF 14 Ġ. ĠMost Ġof Ġthese Ġstudies Ġconsistently Ġreported Ġthat Ġpsychological Ġbiases Ġsuch Ġas Ġprim ing Ġeffects Ġand Ġconfirmation Ġbias Ġplay Ġan Ġimportant Ġrole Ġin Ġpeople Ġability Ġto Ġdiscern Ġmisinformation . </s> In Ġrelation Ġto Ġthe Ġproduction Ġand Ġdistribution Ġof Ġfake Ġnews , Ġa Ġrecent Ġpaper Ġin Ġthe Ġfield Ġof ĠEconomics ĠB IB REF 9 Ġfound Ġthat Ġmost Ġfake Ġnews Ġsites Ġuse Ġnames Ġthat Ġresemble Ġthose Ġof Ġlegitimate Ġorganizations , Ġand Ġthat Ġsites Ġsupplying Ġfake Ġnews Ġtend Ġto Ġbe Ġshort - lived . ĠThese Ġauthors Ġalso Ġnoticed Ġthat Ġfake Ġnews Ġitems Ġare Ġmore Ġlikely Ġshared Ġthan Ġlegitimate Ġarticles Ġcoming Ġfrom Ġtrusted Ġsources , Ġand Ġthey Ġtend Ġto Ġexhibit Ġa Ġlarger Ġlevel Ġof Ġpolarization . </s> The Ġconceptual Ġissue Ġof Ġhow Ġto Ġdefine Ġfake Ġnews Ġis Ġa Ġserious Ġand Ġunresolved Ġissue . ĠAs Ġthe Ġfocus Ġof Ġour Ġwork Ġis Ġnot Ġattempting Ġto Ġoffer Ġlight Ġon Ġthis , Ġwe Ġwill Ġrely Ġon Ġwork Ġby Ġother Ġauthors Ġto Ġdescribe Ġwhat Ġwe Ġconsider Ġas Ġfake Ġnews . ĠIn Ġparticular , Ġwe Ġuse Ġthe Ġcategor ization Ġprovided Ġby ĠRubin Ġet Ġal . ĠB IB REF 7 Ġ. ĠThe Ġfive Ġcategories Ġthey Ġdescribed , Ġtogether Ġwith Ġillust rative Ġexamples Ġfrom Ġour Ġdataset , Ġare Ġas Ġfollows : </s> Research ĠHyp othes es </s> Previous Ġworks Ġon Ġthe Ġarea Ġ( present ed Ġin Ġthe Ġsection Ġabove ) Ġsuggest Ġthat Ġthere Ġmay Ġbe Ġimportant Ġdetermin ants Ġfor Ġthe Ġadoption Ġand Ġdiffusion Ġof Ġfake Ġnews . ĠOur Ġhypotheses Ġbuilds Ġon Ġthem Ġand Ġidentifies Ġthree Ġimportant Ġdimensions Ġthat Ġmay Ġhelp Ġdistinguishing Ġfake Ġnews Ġfrom Ġlegit Ġinformation : </s> Taking Ġthose Ġthree Ġdimensions Ġinto Ġaccount , Ġwe Ġpropose Ġthe Ġfollowing Ġhypotheses Ġabout Ġthe Ġfeatures Ġthat Ġwe Ġbelieve Ġcan Ġhelp Ġto Ġidentify Ġtweets Ġcontaining Ġfake Ġnews Ġfrom Ġthose Ġnot Ġcontaining Ġthem . ĠThey Ġwill Ġbe Ġlater Ġtested Ġover Ġour Ġcollected Ġdataset . </s> Ex posure . </s> Character ization . </s> P olar ization . </s> Data Ġand ĠMethod ology </s> For Ġthis Ġstudy , Ġwe Ġcollected Ġpublicly Ġavailable Ġtweets Ġusing ĠTwitter 's Ġpublic ĠAPI . ĠGiven Ġthe Ġnature Ġof Ġthe Ġdata , Ġit Ġis Ġimportant Ġto Ġemphasize Ġthat Ġsuch Ġtweets Ġare Ġsubject Ġto ĠTwitter 's Ġterms Ġand Ġconditions Ġwhich Ġindicate Ġthat Ġusers Ġconsent Ġto Ġthe Ġcollection , Ġtransfer , Ġmanipulation , Ġstorage , Ġand Ġdisclosure Ġof Ġdata . ĠTherefore , Ġwe Ġdo Ġnot Ġexpect Ġethical , Ġlegal , Ġor Ġsocial Ġimplications Ġfrom Ġthe Ġusage Ġof Ġthe Ġtweets . ĠOur Ġdata Ġwas Ġcollected Ġusing Ġsearch Ġterms Ġrelated Ġto Ġthe Ġpresidential Ġelection Ġheld Ġin Ġthe ĠUnited ĠStates Ġon ĠNovember Ġ8 th Ġ2016 . ĠParticularly , Ġwe Ġquer ied ĠTwitter 's Ġstreaming ĠAPI , Ġmore Ġprecisely Ġthe Ġfilter Ġendpoint Ġof Ġthe Ġstreaming ĠAPI , Ġusing Ġthe Ġfollowing Ġhasht ags Ġand Ġuser Ġhandles : Ġ# My Vote 2016 , Ġ# E lection Day , Ġ# election night , Ġ@ realDonaldTrump Ġand Ġ@ Hillary Clinton . ĠThe Ġdata Ġcollection Ġran Ġfor Ġjust Ġone Ġday Ġ( Nov Ġ8 th Ġ2016 ). </s> One Ġstraightforward Ġway Ġof Ġsharing Ġinformation Ġon ĠTwitter Ġis Ġby Ġusing Ġthe Ġretweet Ġfunctionality , Ġwhich Ġenables Ġa Ġuser Ġto Ġshare Ġa Ġexact Ġcopy Ġof Ġa Ġtweet Ġwith Ġhis Ġfollowers . ĠAmong Ġthe Ġreasons Ġfor Ġretweet ing , ĠBody Ġet Ġal . ĠB IB REF 15 Ġreported Ġthe Ġwill Ġto : Ġ1 ) Ġspread Ġtweets Ġto Ġa Ġnew Ġaudience , Ġ2 ) Ġto Ġshow Ġone âĢ Ļ s Ġrole Ġas Ġa Ġlistener , Ġand Ġ3 ) Ġto Ġagree Ġwith Ġsomeone Ġor Ġvalidate Ġthe Ġthoughts Ġof Ġothers . ĠAs Ġindicated , Ġour Ġinitial Ġinterest Ġis Ġto Ġcharacterize Ġtweets Ġcontaining Ġfake Ġnews Ġthat Ġwent Ġviral Ġ( as Ġthey Ġare Ġthe Ġmost Ġharmful Ġones , Ġas Ġthey Ġreach Ġa Ġwider Ġaudience ), Ġand Ġunderstand Ġhow Ġit Ġdiffers Ġfrom Ġother Ġviral Ġtweets Ġ( that Ġdo Ġnot Ġcontain Ġfake Ġnews ). ĠFor Ġour Ġstudy , Ġwe Ġconsider Ġthat Ġa Ġtweet Ġwent Ġviral Ġif Ġit Ġwas Ġretweet ed Ġmore Ġthan Ġ1000 Ġtimes . </s> Once Ġwe Ġhave Ġthe Ġdataset Ġof Ġviral Ġtweets , Ġwe Ġeliminated Ġdupl icates Ġ( some Ġof Ġthe Ġtweets Ġwere Ġcollected Ġseveral Ġtimes Ġbecause Ġthey Ġhad Ġseveral Ġhandles ) Ġand Ġan Ġexpert Ġmanually Ġinspected Ġthe Ġtext Ġfield Ġwithin Ġthe Ġtweets Ġto Ġlabel Ġthem Ġas Ġcontaining Ġfake Ġnews , Ġor Ġnot Ġcontaining Ġthem Ġ( according Ġto Ġthe Ġcharacterization Ġpresented Ġbefore ). ĠThis Ġannot ated Ġdataset ĠB IB REF 8 Ġis Ġpublicly Ġavailable Ġand Ġcan Ġbe Ġfreely Ġreused . </s> Finally , Ġwe Ġuse Ġthe Ġfollowing Ġfields Ġwithin Ġtweets Ġ( from Ġthe Ġones Ġreturned Ġby ĠTwitter 's ĠAPI ) Ġto Ġcompare Ġtheir Ġdistributions Ġand Ġlook Ġfor Ġdifferences Ġbetween Ġviral Ġtweets Ġcontaining Ġfake Ġnews Ġand Ġviral Ġtweets Ġnot Ġcontaining Ġfake Ġnews : </s> In Ġthe Ġfollowing Ġsection , Ġwe Ġprovide Ġgraphical Ġdescriptions Ġof Ġthe Ġdistribution Ġof Ġeach Ġof Ġthe Ġidentified Ġattributes Ġfor Ġthe Ġtwo Ġsets Ġof Ġtweets Ġ( those Ġlabelled Ġas Ġcontaining Ġfake Ġnews Ġand Ġthose Ġlabelled Ġas Ġnot Ġcontaining Ġthem ). ĠWhere Ġappropriate , Ġwe Ġnormalized Ġand / or Ġtook Ġlog ar ith ms Ġof Ġthe Ġdata Ġfor Ġbetter Ġrepresentation . ĠTo Ġgain Ġa Ġbetter Ġunderstanding Ġof Ġthe Ġsignificance Ġof Ġthose Ġdifferences , Ġwe Ġuse Ġthe ĠKol m og or ov - Sm ir nov Ġtest Ġwith Ġthe Ġnull Ġhypothesis Ġthat Ġboth Ġdistributions Ġare Ġequal . </s> Results </s> The Ġsample Ġcollected Ġconsisted Ġon Ġ1 Ġ7 85 Ġ8 55 Ġtweets Ġpublished Ġby Ġ8 48 Ġ196 Ġdifferent Ġusers . ĠWithin Ġour Ġsample , Ġwe Ġidentified Ġ13 27 Ġtweets Ġthat Ġwent Ġviral Ġ( ret weet ed Ġmore Ġthan Ġ1000 Ġtimes Ġby Ġthe Ġ8 th Ġof ĠNovember Ġ2016 ) Ġproduced Ġby Ġ6 43 Ġusers . ĠSuch Ġsmall Ġsubset Ġof Ġviral Ġtweets Ġwere Ġretweet ed Ġon Ġ290 Ġ8 41 Ġoccasions Ġin Ġthe Ġobserved Ġtime - window . </s> The Ġ13 27 Ġ` v iral ' Ġtweets Ġwere Ġmanually Ġannot ated Ġas Ġcontaining Ġfake Ġnews Ġor Ġnot . ĠThe Ġannotation Ġwas Ġcarried Ġout Ġby Ġa Ġsingle Ġperson Ġin Ġorder Ġto Ġobtain Ġa Ġconsistent Ġannotation Ġthroughout Ġthe Ġdataset . ĠOut Ġof Ġthose Ġ13 27 Ġtweets , Ġwe Ġidentified Ġ136 Ġas Ġpotentially Ġcontaining Ġfake Ġnews Ġ( according Ġto Ġthe Ġcategories Ġpreviously Ġdescribed ), Ġand Ġthe Ġrest Ġwere Ġclassified Ġas Ġ` non Ġcontaining Ġfake Ġnews '. ĠNote Ġthat Ġthe Ġcategor ization Ġis Ġfar Ġfrom Ġbeing Ġperfect Ġgiven Ġthe Ġambiguity Ġof Ġfake Ġnews Ġthemselves Ġand Ġhuman Ġjudgement Ġinvolved Ġin Ġthe Ġprocess Ġof Ġcategor ization . ĠBecause Ġof Ġthis , Ġwe Ġdo Ġnot Ġclaim Ġthat Ġthis Ġdataset Ġcan Ġbe Ġconsidered Ġa Ġground Ġtruth . </s> The Ġfollowing Ġresults Ġdetail Ġcharacteristics Ġof Ġthese Ġtweets Ġalong Ġthe Ġpreviously Ġmentioned Ġdimensions . ĠTable ĠT AB REF 23 Ġreports Ġthe Ġactual Ġdifferences Ġ( together Ġwith Ġtheir Ġassociated Ġp - values ) Ġof Ġthe Ġdistributions Ġof Ġviral Ġtweets Ġcontaining Ġfake Ġnews Ġand Ġviral Ġtweets Ġnot Ġcontaining Ġthem Ġfor Ġevery Ġvariable Ġconsidered . </s> Ex posure </s> Figure ĠFIG REF 24 Ġshows Ġthat , Ġin Ġcontrast Ġto Ġother Ġkinds Ġof Ġviral Ġtweets , Ġthose Ġcontaining Ġfake Ġnews Ġwere Ġcreated Ġmore Ġrecently . ĠAs Ġsuch , ĠTwitter Ġusers Ġwere Ġexposed Ġto Ġfake Ġnews Ġrelated Ġto Ġthe Ġelection Ġfor Ġa Ġshorter Ġperiod Ġof Ġtime . </s> However , Ġin Ġterms Ġof Ġret we ets , ĠFigure ĠFIG REF 25 Ġshows Ġno Ġapparent Ġdifference Ġbetween Ġcontaining Ġfake Ġnews Ġor Ġnot Ġcontaining Ġthem . ĠThat Ġis Ġconfirmed Ġby Ġthe ĠKol m og or ov - Sm ir n off Ġtest , Ġwhich Ġdoes Ġnot Ġdiscard Ġthe Ġhypothesis Ġthat Ġthe Ġassociated Ġdistributions Ġare Ġequal . </s> In Ġrelation Ġto Ġthe Ġnumber Ġof Ġfavourites , Ġusers Ġthat Ġgenerated Ġat Ġleast Ġa Ġviral Ġtweet Ġcontaining Ġfake Ġnews Ġappear Ġto Ġhave , Ġon Ġaverage , Ġless Ġfavourites Ġthan Ġusers Ġthat Ġdo Ġnot Ġgenerate Ġthem . ĠFigure ĠFIG REF 26 Ġshows Ġthe Ġdistribution Ġof Ġfavourites . ĠDespite Ġthe Ġapparent Ġvisual Ġdifferences , Ġthe Ġdifference Ġare Ġnot Ġstatistically Ġsignificant . </s> Finally , Ġthe Ġnumber Ġof Ġhasht ags Ġused Ġin Ġviral Ġfake Ġnews Ġappears Ġto Ġbe Ġlarger Ġthan Ġthose Ġin Ġother Ġviral Ġtweets . ĠFigure ĠFIG REF 27 Ġshows Ġthe Ġdensity Ġdistribution Ġof Ġthe Ġnumber Ġof Ġhasht ags Ġused . ĠHowever , Ġonce Ġagain , Ġwe Ġwere Ġnot Ġable Ġto Ġfind Ġany Ġstatistical Ġdifference Ġbetween Ġthe Ġaverage Ġnumber Ġof Ġhasht ags Ġin Ġa Ġviral Ġtweet Ġand Ġthe Ġaverage Ġnumber Ġof Ġhasht ags Ġin Ġviral Ġfake Ġnews . </s> Character ization </s> We Ġfound Ġthat Ġ82 Ġusers Ġwithin Ġour Ġsample Ġwere Ġspreading Ġfake Ġnews Ġ( i . e . Ġthey Ġproduced Ġat Ġleast Ġone Ġtweet Ġwhich Ġwas Ġlabelled Ġas Ġfake Ġnews ). ĠOut Ġof Ġthose , Ġ34 Ġhad Ġverified Ġaccounts , Ġand Ġthe Ġrest Ġwere Ġun verified . ĠFrom Ġthe Ġ48 Ġun verified Ġaccounts , Ġ6 Ġhave Ġbeen Ġsuspended Ġby ĠTwitter Ġat Ġthe Ġdate Ġof Ġwriting , Ġ3 Ġtried Ġto Ġimitate Ġlegitimate Ġaccounts Ġof Ġothers , Ġand Ġ4 Ġaccounts Ġhave Ġbeen Ġalready Ġdeleted . ĠFigure ĠFIG REF 28 Ġshows Ġthe Ġproportion Ġof Ġverified Ġaccounts Ġto Ġun verified Ġaccounts Ġfor Ġviral Ġtweets Ġ( containing Ġfake Ġnews Ġvs . Ġnot Ġcontaining Ġfake Ġnews ). ĠFrom Ġthe Ġchart , Ġit Ġis Ġclear Ġthat Ġthere Ġis Ġa Ġhigher Ġchance Ġof Ġfake Ġnews Ġcoming Ġfrom Ġun verified Ġaccounts . </s> Turn ing Ġto Ġfriends , Ġaccounts Ġdistributing Ġfake Ġnews Ġappear Ġto Ġhave , Ġon Ġaverage , Ġthe Ġsame Ġnumber Ġof Ġfriends Ġthan Ġthose Ġdistributing Ġtweets Ġwith Ġno Ġfake Ġnews . ĠHowever , Ġthe Ġdensity Ġdistribution Ġof Ġfriends Ġfrom Ġthe Ġaccounts Ġ( Figure ĠFIG REF 29 Ġ) Ġshows Ġthat Ġthere Ġis Ġindeed Ġa Ġstatistically Ġsignificant Ġdifference Ġin Ġtheir Ġdistributions . </s> If Ġwe Ġtake Ġinto Ġconsideration Ġthe Ġnumber Ġof Ġfollowers , Ġaccounts Ġgenerating Ġviral Ġtweets Ġwith Ġfake Ġnews Ġdo Ġhave Ġa Ġvery Ġdifferent Ġdistribution Ġon Ġthis Ġdimension , Ġcompared Ġto Ġthose Ġaccounts Ġgenerating Ġviral Ġtweets Ġwith Ġno Ġfake Ġnews Ġ( see ĠFigure ĠFIG REF 30 Ġ). ĠIn Ġfact , Ġsuch Ġdifferences Ġare Ġstatistically Ġsignificant . </s> A Ġuseful Ġrepresentation Ġfor Ġfriends Ġand Ġfollowers Ġis Ġthe Ġratio Ġbetween Ġfriends / follow ers . ĠFigures ĠFIG REF 31 Ġand ĠFIG REF 32 Ġshow Ġthis Ġrepresentation . ĠNotice Ġthat Ġaccounts Ġspreading Ġviral Ġtweets Ġwith Ġfake Ġnews Ġhave , Ġon Ġaverage , Ġa Ġlarger Ġratio Ġof Ġfriends / follow ers . ĠThe Ġdistribution Ġof Ġthose Ġaccounts Ġnot Ġgenerating Ġfake Ġnews Ġis Ġmore Ġevenly Ġdistributed . </s> With Ġrespect Ġto Ġthe Ġnumber Ġof Ġmentions , ĠFigure ĠFIG REF 33 Ġshows Ġthat Ġviral Ġtweets Ġlabelled Ġas Ġcontaining Ġfake Ġnews Ġappear Ġto Ġuse Ġmentions Ġto Ġother Ġusers Ġless Ġfrequently Ġthan Ġviral Ġtweets Ġnot Ġcontaining Ġfake Ġnews . ĠIn Ġother Ġwords , Ġtweets Ġcontaining Ġfake Ġnews Ġmostly Ġcontain Ġ1 Ġmention , Ġwhereas Ġother Ġtweets Ġtend Ġto Ġhave Ġtwo ). ĠSuch Ġdifferences Ġare Ġstatistically Ġsignificant . </s> The Ġanalysis Ġ( Figure ĠFIG REF 34 Ġ) Ġof Ġthe Ġpresence Ġof Ġmedia Ġin Ġthe Ġtweets Ġin Ġour Ġdataset Ġshows Ġthat Ġtweets Ġlabelled Ġas Ġnot Ġcontaining Ġfake Ġnews Ġappear Ġto Ġpresent Ġmore Ġmedia Ġelements Ġthan Ġthose Ġlabelled Ġas Ġfake Ġnews . ĠHowever , Ġthe Ġdifference Ġis Ġnot Ġstatistically Ġsignificant . </s> On Ġthe Ġother Ġhand , ĠFigure ĠFIG REF 35 Ġshows Ġthat Ġviral Ġtweets Ġcontaining Ġfake Ġnews Ġappear Ġto Ġinclude Ġmore ĠURLs Ġto Ġother Ġsites Ġthan Ġviral Ġtweets Ġthat Ġdo Ġnot Ġcontain Ġfake Ġnews . ĠIn Ġfact , Ġthe Ġdifference Ġbetween Ġthe Ġtwo Ġdistributions Ġis Ġstatistically Ġsignificant Ġ( assuming ĠIN LINE FORM 0 Ġ). </s> P olar ization </s> Finally , Ġmanual Ġinspection Ġof Ġthe Ġtext Ġfield Ġof Ġthose Ġviral Ġtweets Ġlabelled Ġas Ġcontaining Ġfake Ġnews Ġshows Ġthat Ġ117 Ġof Ġsuch Ġtweets Ġexpressed Ġsupport Ġfor ĠDonald ĠTrump , Ġwhile Ġonly Ġ8 Ġsupported ĠHillary ĠClinton . ĠThe Ġremaining Ġtweets Ġcontained Ġfake Ġnews Ġrelated Ġto Ġother Ġtopics , Ġnot Ġexpressing Ġsupport Ġfor Ġany Ġof Ġthe Ġcandidates . </s> Discussion </s> As Ġa Ġsummary , Ġand Ġconstrained Ġby Ġour Ġexisting Ġdataset , Ġwe Ġmade Ġthe Ġfollowing Ġobservations Ġregarding Ġdifferences Ġbetween Ġviral Ġtweets Ġlabelled Ġas Ġcontaining Ġfake Ġnews Ġand Ġviral Ġtweets Ġlabelled Ġas Ġnot Ġcontaining Ġthem : </s> These Ġfindings Ġ( related Ġto Ġour Ġinitial Ġhypothesis Ġin ĠTable ĠT AB REF 44 Ġ) Ġclearly Ġsuggest Ġthat Ġthere Ġare Ġspecific Ġpieces Ġof Ġmeta - data Ġabout Ġtweets Ġthat Ġmay Ġallow Ġthe Ġidentification Ġof Ġfake Ġnews . ĠOne Ġsuch Ġparameter Ġis Ġthe Ġtime Ġof Ġexposure . ĠVir al Ġtweets Ġcontaining Ġfake Ġnews Ġare Ġshorter - lived Ġthan Ġthose Ġcontaining Ġother Ġtype Ġof Ġcontent . ĠThis Ġnotion Ġseems Ġto Ġresonate Ġwith Ġour Ġfindings Ġshowing Ġthat Ġa Ġnumber Ġof Ġaccounts Ġspreading Ġfake Ġnews Ġhave Ġalready Ġbeen Ġdeleted Ġor Ġsuspended Ġby ĠTwitter Ġby Ġthe Ġtime Ġof Ġwriting . ĠIf Ġone Ġconsiders Ġthat Ġresearchers Ġusing Ġdifferent Ġdata Ġhave Ġfound Ġsimilar Ġresults ĠB IB REF 9 Ġ, Ġit Ġappears Ġthat Ġthe Ġlifetime Ġof Ġaccounts , Ġtogether Ġwith Ġthe Ġage Ġof Ġthe Ġquestioned Ġviral Ġcontent Ġcould Ġbe Ġuseful Ġto Ġidentify Ġfake Ġnews . ĠIn Ġthe Ġlight Ġof Ġthis Ġfinding , Ġaccounts Ġnewly Ġcreated Ġshould Ġprobably Ġput Ġunder Ġhigher Ġscrutiny Ġthan Ġolder Ġones . ĠThis Ġin Ġfact , Ġwould Ġbe Ġa Ġnice Ġa - pri ori Ġbias Ġfor Ġa ĠBay esian Ġclass ifier . </s> Account s Ġspreading Ġfake Ġnews Ġappear Ġto Ġhave Ġa Ġlarger Ġproportion Ġof Ġfriends / follow ers Ġ( i . e . Ġthey Ġhave , Ġon Ġaverage , Ġthe Ġsame Ġnumber Ġof Ġfriends Ġbut Ġa Ġsmaller Ġnumber Ġof Ġfollowers ) Ġthan Ġthose Ġspreading Ġviral Ġcontent Ġonly . ĠTogether Ġwith Ġthe Ġfact Ġthat , Ġon Ġaverage , Ġtweets Ġcontaining Ġfake Ġnews Ġhave Ġmore ĠURLs Ġthan Ġthose Ġspreading Ġviral Ġcontent , Ġit Ġis Ġpossible Ġto Ġhypothes ize Ġthat , Ġboth , Ġthe Ġratio Ġof Ġfriends / follow ers Ġof Ġthe Ġaccount Ġproducing Ġa Ġviral Ġtweet Ġand Ġnumber Ġof ĠURLs Ġcontained Ġin Ġsuch Ġa Ġtweet Ġcould Ġbe Ġuseful Ġto Ġsingle - out Ġfake Ġnews Ġin ĠTwitter . ĠNot Ġonly Ġthat , Ġbut Ġour Ġfinding Ġrelated Ġto Ġthe Ġnumber Ġof ĠURLs Ġis Ġin Ġline Ġwith Ġintu itions Ġbehind Ġthe Ġincentives Ġto Ġcreate Ġfake Ġnews Ġcommonly Ġfound Ġin Ġthe Ġliterature ĠB IB REF 9 Ġ( in Ġparticular Ġthat Ġof Ġobtaining Ġrevenue Ġthrough Ġclick - through Ġadvertising ). </s> Finally , Ġit Ġis Ġinteresting Ġto Ġnotice Ġthat Ġthe Ġcontent Ġof Ġviral Ġfake Ġnews Ġwas Ġhighly Ġpolarized . ĠThis Ġfinding Ġis Ġalso Ġin Ġline Ġwith Ġthose Ġof ĠAl cott Ġet Ġal . ĠB IB REF 9 Ġ. ĠThis Ġfeature Ġsuggests Ġthat Ġtextual Ġsentiment Ġanalysis Ġof Ġthe Ġcontent Ġof Ġtweets Ġ( as Ġmost Ġresearchers Ġdo ), Ġtogether Ġwith Ġthe Ġabove Ġmentioned Ġparameters Ġfrom Ġmeta - data , Ġmay Ġprove Ġuseful Ġfor Ġidentifying Ġfake Ġnews . </s> Con clusions </s> With Ġthe Ġelection Ġof ĠDonald ĠTrump Ġas ĠPresident Ġof Ġthe ĠUnited ĠStates , Ġthe Ġconcept Ġof Ġfake Ġnews Ġhas Ġbecome Ġa Ġbroadly - known Ġphenomenon Ġthat Ġis Ġgetting Ġtremendous Ġattention Ġfrom Ġgovernments Ġand Ġmedia Ġcompanies . ĠWe Ġhave Ġpresented Ġa Ġpreliminary Ġstudy Ġon Ġthe Ġmeta - data Ġof Ġa Ġpublicly Ġavailable Ġdataset Ġof Ġtweets Ġthat Ġbecame Ġviral Ġduring Ġthe Ġday Ġof Ġthe Ġ2016 ĠUS Ġpresidential Ġelection . ĠOur Ġaim Ġis Ġto Ġadvance Ġthe Ġunderstanding Ġof Ġwhich Ġfeatures Ġmight Ġbe Ġcharacteristic Ġof Ġviral Ġtweets Ġcontaining Ġfake Ġnews Ġin Ġcomparison Ġwith Ġviral Ġtweets Ġwithout Ġfake Ġnews . </s> We Ġbelieve Ġthat Ġthe Ġonly Ġway Ġto Ġautomatically Ġidentify Ġthose Ġdeceit ful Ġtweets Ġ( i . e . Ġcontaining Ġfake Ġnews ) Ġis Ġby Ġactually Ġunderstanding Ġand Ġmodelling Ġthem . ĠOnly Ġthen , Ġthe Ġautomation Ġof Ġthe Ġprocesses Ġof Ġtagging Ġand Ġblocking Ġthese Ġtweets Ġcan Ġbe Ġsuccessfully Ġperformed . ĠIn Ġthe Ġsame Ġway Ġthat Ġspam Ġwas Ġfought , Ġwe Ġanticipate Ġfake Ġnews Ġwill Ġsuffer Ġa Ġsimilar Ġevolution , Ġwith Ġsocial Ġplatforms Ġimplementing Ġtools Ġto Ġdeal Ġwith Ġthem . ĠWith Ġmost Ġworks Ġso Ġfar Ġfocusing Ġon Ġthe Ġactual Ġcontent Ġof Ġthe Ġtweets , Ġours Ġis Ġa Ġnovel Ġattempt Ġfrom Ġa Ġdifferent , Ġbut Ġalso Ġcomplementary , Ġangle . </s> Within Ġthe Ġused Ġdataset , Ġwe Ġfound Ġthere Ġare Ġdifferences Ġaround Ġexposure , Ġcharacteristics Ġof Ġaccounts Ġspreading Ġfake Ġnews Ġand Ġthe Ġtone Ġof Ġthe Ġcontent . ĠThose Ġfindings Ġsuggest Ġthat Ġit Ġis Ġindeed Ġpossible Ġto Ġmodel Ġand Ġautomatically Ġdetect Ġfake Ġnews . ĠWe Ġplan Ġto Ġreplicate Ġand Ġvalidate Ġour Ġexperiments Ġin Ġan Ġextended Ġsample Ġof Ġtweets Ġ( until Ġ4 Ġmonths Ġafter Ġthe ĠUS Ġelection ), Ġand Ġtests Ġthe Ġpredictive Ġpower Ġof Ġthe Ġfeatures Ġwe Ġfound Ġrelevant Ġwithin Ġour Ġsample . </s> Author ĠDisclosure ĠStatement </s> No Ġcompeting Ġfinancial Ġinterest Ġexist .
10pt 1.10pt [ Characterizing Political Fake News in Twitter by its Meta-DataJulio Amador Díaz LópezAxel Oehmichen Miguel Molina-Solana( j.amador, axelfrancois.oehmichen11, mmolinas@imperial.ac.uk ) Imperial College London This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data. In particular, we focus on more than 1.5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America. We use the meta-data embedded within those tweets in order to look for differences between tweets containing fake news and tweets not containing them. Specifically, we perform our analysis only on tweets that went viral, by studying proxies for users' exposure to the tweets, by characterizing accounts spreading fake news, and by looking at their polarization. We found significant differences on the distribution of followers, the number of URLs on tweets, and the verification of the users. ] Introduction While fake news, understood as deliberately misleading pieces of information, have existed since long ago (e.g. it is not unusual to receive news falsely claiming the death of a celebrity), the term reached the mainstream, particularly so in politics, during the 2016 presidential election in the United States BIBREF0 . Since then, governments and corporations alike (e.g. Google BIBREF1 and Facebook BIBREF2 ) have begun efforts to tackle fake news as they can affect political decisions BIBREF3 . Yet, the ability to define, identify and stop fake news from spreading is limited. Since the Obama campaign in 2008, social media has been pervasive in the political arena in the United States. Studies report that up to 62% of American adults receive their news from social media BIBREF4 . The wide use of platforms such as Twitter and Facebook has facilitated the diffusion of fake news by simplifying the process of receiving content with no significant third party filtering, fact-checking or editorial judgement. Such characteristics make these platforms suitable means for sharing news that, disguised as legit ones, try to confuse readers. Such use and their prominent rise has been confirmed by Craig Silverman, a Canadian journalist who is a prominent figure on fake news BIBREF5 : “In the final three months of the US presidential campaign, the top-performing fake election news stories on Facebook generated more engagement than the top stories from major news outlet”. Our current research hence departs from the assumption that social media is a conduit for fake news and asks the question of whether fake news (as spam was some years ago) can be identified, modelled and eventually blocked. In order to do so, we use a sample of more that 1.5M tweets collected on November 8th 2016 —election day in the United States— with the goal of identifying features that tweets containing fake news are likely to have. As such, our paper aims to provide a preliminary characterization of fake news in Twitter by looking into meta-data embedded in tweets. Considering meta-data as a relevant factor of analysis is in line with findings reported by Morris et al. BIBREF6 . We argue that understanding differences between tweets containing fake news and regular tweets will allow researchers to design mechanisms to block fake news in Twitter. Specifically, our goals are: 1) compare the characteristics of tweets labelled as containing fake news to tweets labelled as not containing them, 2) characterize, through their meta-data, viral tweets containing fake news and the accounts from which they originated, and 3) determine the extent to which tweets containing fake news expressed polarized political views. For our study, we used the number of retweets to single-out those that went viral within our sample. Tweets within that subset (viral tweets hereafter) are varied and relate to different topics. We consider that a tweet contains fake news if its text falls within any of the following categories described by Rubin et al. BIBREF7 (see next section for the details of such categories): serious fabrication, large-scale hoaxes, jokes taken at face value, slanted reporting of real facts and stories where the truth is contentious. The dataset BIBREF8 , manually labelled by an expert, has been publicly released and is available to researchers and interested parties. From our results, the following main observations can be made: Our findings resonate with similar work done on fake news such as the one from Allcot and Gentzkow BIBREF9 . Therefore, even if our study is a preliminary attempt at characterizing fake news on Twitter using only their meta-data, our results provide external validity to previous research. Moreover, our work not only stresses the importance of using meta-data, but also underscores which parameters may be useful to identify fake news on Twitter. The rest of the paper is organized as follows. The next section briefly discusses where this work is located within the literature on fake news and contextualizes the type of fake news we are studying. Then, we present our hypotheses, the data, and the methodology we follow. Finally, we present our findings, conclusions of this study, and future lines of work. Defining Fake news Our research is connected to different strands of academic knowledge related to the phenomenon of fake news. In relation to Computer Science, a recent survey by Conroy and colleagues BIBREF10 identifies two popular approaches to single-out fake news. On the one hand, the authors pointed to linguistic approaches consisting in using text, its linguistic characteristics and machine learning techniques to automatically flag fake news. On the other, these researchers underscored the use of network approaches, which make use of network characteristics and meta-data, to identify fake news. With respect to social sciences, efforts from psychology, political science and sociology, have been dedicated to understand why people consume and/or believe misinformation BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 . Most of these studies consistently reported that psychological biases such as priming effects and confirmation bias play an important role in people ability to discern misinformation. In relation to the production and distribution of fake news, a recent paper in the field of Economics BIBREF9 found that most fake news sites use names that resemble those of legitimate organizations, and that sites supplying fake news tend to be short-lived. These authors also noticed that fake news items are more likely shared than legitimate articles coming from trusted sources, and they tend to exhibit a larger level of polarization. The conceptual issue of how to define fake news is a serious and unresolved issue. As the focus of our work is not attempting to offer light on this, we will rely on work by other authors to describe what we consider as fake news. In particular, we use the categorization provided by Rubin et al. BIBREF7 . The five categories they described, together with illustrative examples from our dataset, are as follows: Research Hypotheses Previous works on the area (presented in the section above) suggest that there may be important determinants for the adoption and diffusion of fake news. Our hypotheses builds on them and identifies three important dimensions that may help distinguishing fake news from legit information: Taking those three dimensions into account, we propose the following hypotheses about the features that we believe can help to identify tweets containing fake news from those not containing them. They will be later tested over our collected dataset. Exposure. Characterization. Polarization. Data and Methodology For this study, we collected publicly available tweets using Twitter's public API. Given the nature of the data, it is important to emphasize that such tweets are subject to Twitter's terms and conditions which indicate that users consent to the collection, transfer, manipulation, storage, and disclosure of data. Therefore, we do not expect ethical, legal, or social implications from the usage of the tweets. Our data was collected using search terms related to the presidential election held in the United States on November 8th 2016. Particularly, we queried Twitter's streaming API, more precisely the filter endpoint of the streaming API, using the following hashtags and user handles: #MyVote2016, #ElectionDay, #electionnight, @realDonaldTrump and @HillaryClinton. The data collection ran for just one day (Nov 8th 2016). One straightforward way of sharing information on Twitter is by using the retweet functionality, which enables a user to share a exact copy of a tweet with his followers. Among the reasons for retweeting, Body et al. BIBREF15 reported the will to: 1) spread tweets to a new audience, 2) to show one’s role as a listener, and 3) to agree with someone or validate the thoughts of others. As indicated, our initial interest is to characterize tweets containing fake news that went viral (as they are the most harmful ones, as they reach a wider audience), and understand how it differs from other viral tweets (that do not contain fake news). For our study, we consider that a tweet went viral if it was retweeted more than 1000 times. Once we have the dataset of viral tweets, we eliminated duplicates (some of the tweets were collected several times because they had several handles) and an expert manually inspected the text field within the tweets to label them as containing fake news, or not containing them (according to the characterization presented before). This annotated dataset BIBREF8 is publicly available and can be freely reused. Finally, we use the following fields within tweets (from the ones returned by Twitter's API) to compare their distributions and look for differences between viral tweets containing fake news and viral tweets not containing fake news: In the following section, we provide graphical descriptions of the distribution of each of the identified attributes for the two sets of tweets (those labelled as containing fake news and those labelled as not containing them). Where appropriate, we normalized and/or took logarithms of the data for better representation. To gain a better understanding of the significance of those differences, we use the Kolmogorov-Smirnov test with the null hypothesis that both distributions are equal. Results The sample collected consisted on 1 785 855 tweets published by 848 196 different users. Within our sample, we identified 1327 tweets that went viral (retweeted more than 1000 times by the 8th of November 2016) produced by 643 users. Such small subset of viral tweets were retweeted on 290 841 occasions in the observed time-window. The 1327 `viral' tweets were manually annotated as containing fake news or not. The annotation was carried out by a single person in order to obtain a consistent annotation throughout the dataset. Out of those 1327 tweets, we identified 136 as potentially containing fake news (according to the categories previously described), and the rest were classified as `non containing fake news'. Note that the categorization is far from being perfect given the ambiguity of fake news themselves and human judgement involved in the process of categorization. Because of this, we do not claim that this dataset can be considered a ground truth. The following results detail characteristics of these tweets along the previously mentioned dimensions. Table TABREF23 reports the actual differences (together with their associated p-values) of the distributions of viral tweets containing fake news and viral tweets not containing them for every variable considered. Exposure Figure FIGREF24 shows that, in contrast to other kinds of viral tweets, those containing fake news were created more recently. As such, Twitter users were exposed to fake news related to the election for a shorter period of time. However, in terms of retweets, Figure FIGREF25 shows no apparent difference between containing fake news or not containing them. That is confirmed by the Kolmogorov-Smirnoff test, which does not discard the hypothesis that the associated distributions are equal. In relation to the number of favourites, users that generated at least a viral tweet containing fake news appear to have, on average, less favourites than users that do not generate them. Figure FIGREF26 shows the distribution of favourites. Despite the apparent visual differences, the difference are not statistically significant. Finally, the number of hashtags used in viral fake news appears to be larger than those in other viral tweets. Figure FIGREF27 shows the density distribution of the number of hashtags used. However, once again, we were not able to find any statistical difference between the average number of hashtags in a viral tweet and the average number of hashtags in viral fake news. Characterization We found that 82 users within our sample were spreading fake news (i.e. they produced at least one tweet which was labelled as fake news). Out of those, 34 had verified accounts, and the rest were unverified. From the 48 unverified accounts, 6 have been suspended by Twitter at the date of writing, 3 tried to imitate legitimate accounts of others, and 4 accounts have been already deleted. Figure FIGREF28 shows the proportion of verified accounts to unverified accounts for viral tweets (containing fake news vs. not containing fake news). From the chart, it is clear that there is a higher chance of fake news coming from unverified accounts. Turning to friends, accounts distributing fake news appear to have, on average, the same number of friends than those distributing tweets with no fake news. However, the density distribution of friends from the accounts (Figure FIGREF29 ) shows that there is indeed a statistically significant difference in their distributions. If we take into consideration the number of followers, accounts generating viral tweets with fake news do have a very different distribution on this dimension, compared to those accounts generating viral tweets with no fake news (see Figure FIGREF30 ). In fact, such differences are statistically significant. A useful representation for friends and followers is the ratio between friends/followers. Figures FIGREF31 and FIGREF32 show this representation. Notice that accounts spreading viral tweets with fake news have, on average, a larger ratio of friends/followers. The distribution of those accounts not generating fake news is more evenly distributed. With respect to the number of mentions, Figure FIGREF33 shows that viral tweets labelled as containing fake news appear to use mentions to other users less frequently than viral tweets not containing fake news. In other words, tweets containing fake news mostly contain 1 mention, whereas other tweets tend to have two). Such differences are statistically significant. The analysis (Figure FIGREF34 ) of the presence of media in the tweets in our dataset shows that tweets labelled as not containing fake news appear to present more media elements than those labelled as fake news. However, the difference is not statistically significant. On the other hand, Figure FIGREF35 shows that viral tweets containing fake news appear to include more URLs to other sites than viral tweets that do not contain fake news. In fact, the difference between the two distributions is statistically significant (assuming INLINEFORM0 ). Polarization Finally, manual inspection of the text field of those viral tweets labelled as containing fake news shows that 117 of such tweets expressed support for Donald Trump, while only 8 supported Hillary Clinton. The remaining tweets contained fake news related to other topics, not expressing support for any of the candidates. Discussion As a summary, and constrained by our existing dataset, we made the following observations regarding differences between viral tweets labelled as containing fake news and viral tweets labelled as not containing them: These findings (related to our initial hypothesis in Table TABREF44 ) clearly suggest that there are specific pieces of meta-data about tweets that may allow the identification of fake news. One such parameter is the time of exposure. Viral tweets containing fake news are shorter-lived than those containing other type of content. This notion seems to resonate with our findings showing that a number of accounts spreading fake news have already been deleted or suspended by Twitter by the time of writing. If one considers that researchers using different data have found similar results BIBREF9 , it appears that the lifetime of accounts, together with the age of the questioned viral content could be useful to identify fake news. In the light of this finding, accounts newly created should probably put under higher scrutiny than older ones. This in fact, would be a nice a-priori bias for a Bayesian classifier. Accounts spreading fake news appear to have a larger proportion of friends/followers (i.e. they have, on average, the same number of friends but a smaller number of followers) than those spreading viral content only. Together with the fact that, on average, tweets containing fake news have more URLs than those spreading viral content, it is possible to hypothesize that, both, the ratio of friends/followers of the account producing a viral tweet and number of URLs contained in such a tweet could be useful to single-out fake news in Twitter. Not only that, but our finding related to the number of URLs is in line with intuitions behind the incentives to create fake news commonly found in the literature BIBREF9 (in particular that of obtaining revenue through click-through advertising). Finally, it is interesting to notice that the content of viral fake news was highly polarized. This finding is also in line with those of Alcott et al. BIBREF9 . This feature suggests that textual sentiment analysis of the content of tweets (as most researchers do), together with the above mentioned parameters from meta-data, may prove useful for identifying fake news. Conclusions With the election of Donald Trump as President of the United States, the concept of fake news has become a broadly-known phenomenon that is getting tremendous attention from governments and media companies. We have presented a preliminary study on the meta-data of a publicly available dataset of tweets that became viral during the day of the 2016 US presidential election. Our aim is to advance the understanding of which features might be characteristic of viral tweets containing fake news in comparison with viral tweets without fake news. We believe that the only way to automatically identify those deceitful tweets (i.e. containing fake news) is by actually understanding and modelling them. Only then, the automation of the processes of tagging and blocking these tweets can be successfully performed. In the same way that spam was fought, we anticipate fake news will suffer a similar evolution, with social platforms implementing tools to deal with them. With most works so far focusing on the actual content of the tweets, ours is a novel attempt from a different, but also complementary, angle. Within the used dataset, we found there are differences around exposure, characteristics of accounts spreading fake news and the tone of the content. Those findings suggest that it is indeed possible to model and automatically detect fake news. We plan to replicate and validate our experiments in an extended sample of tweets (until 4 months after the US election), and tests the predictive power of the features we found relevant within our sample. Author Disclosure Statement No competing financial interest exist.
What are the characteristics of the accounts that spread fake news?
f03df5d99b753dc4833ef27b32bb95ba53d790ee
What Ġare Ġthe Ġcharacteristics Ġof Ġthe Ġaccounts Ġthat Ġspread Ġfake Ġnews ?
Account s Ġthat Ġspread Ġfake Ġnews Ġare Ġmostly Ġun verified , Ġrecently Ġcreated Ġand Ġhave Ġon Ġaverage Ġhigh Ġfriends / follow ers Ġratio
<s> What Ġare Ġthe Ġcharacteristics Ġof Ġthe Ġaccounts Ġthat Ġspread Ġfake Ġnews ? </s> 10 pt </s> 1 . 10 pt </s> [ ĠCharacter izing ĠPolitical ĠFake ĠNews Ġin ĠTwitter Ġby Ġits ĠMeta - Data Jul io ĠAm ador ĠD ÃŃ az ĠL Ã³ pez Ax el ĠO eh mic hen ĠMiguel ĠMol ina - Sol ana ( Ġj . am ador , Ġax elf ran co is . oe h mic hen 11 , Ġmmol inas @ im perial . ac . uk Ġ) ĠImperial ĠCollege ĠLondon ĠThis Ġarticle Ġpresents Ġa Ġpreliminary Ġapproach Ġtowards Ġcharacter izing Ġpolitical Ġfake Ġnews Ġon ĠTwitter Ġthrough Ġthe Ġanalysis Ġof Ġtheir Ġmeta - data . ĠIn Ġparticular , Ġwe Ġfocus Ġon Ġmore Ġthan Ġ1 . 5 M Ġtweets Ġcollected Ġon Ġthe Ġday Ġof Ġthe Ġelection Ġof ĠDonald ĠTrump Ġas Ġ45 th Ġpresident Ġof Ġthe ĠUnited ĠStates Ġof ĠAmerica . ĠWe Ġuse Ġthe Ġmeta - data Ġembedded Ġwithin Ġthose Ġtweets Ġin Ġorder Ġto Ġlook Ġfor Ġdifferences Ġbetween Ġtweets Ġcontaining Ġfake Ġnews Ġand Ġtweets Ġnot Ġcontaining Ġthem . ĠSpecifically , Ġwe Ġperform Ġour Ġanalysis Ġonly Ġon Ġtweets Ġthat Ġwent Ġviral , Ġby Ġstudying Ġproxies Ġfor Ġusers ' Ġexposure Ġto Ġthe Ġtweets , Ġby Ġcharacter izing Ġaccounts Ġspreading Ġfake Ġnews , Ġand Ġby Ġlooking Ġat Ġtheir Ġpolarization . ĠWe Ġfound Ġsignificant Ġdifferences Ġon Ġthe Ġdistribution Ġof Ġfollowers , Ġthe Ġnumber Ġof ĠURLs Ġon Ġtweets , Ġand Ġthe Ġverification Ġof Ġthe Ġusers . </s> ] </s> Introduction </s> While Ġfake Ġnews , Ġunderstood Ġas Ġdeliberately Ġmisleading Ġpieces Ġof Ġinformation , Ġhave Ġexisted Ġsince Ġlong Ġago Ġ( e . g . Ġit Ġis Ġnot Ġunusual Ġto Ġreceive Ġnews Ġfalsely Ġclaiming Ġthe Ġdeath Ġof Ġa Ġcelebrity ), Ġthe Ġterm Ġreached Ġthe Ġmainstream , Ġparticularly Ġso Ġin Ġpolitics , Ġduring Ġthe Ġ2016 Ġpresidential Ġelection Ġin Ġthe ĠUnited ĠStates ĠB IB REF 0 Ġ. ĠSince Ġthen , Ġgovernments Ġand Ġcorporations Ġalike Ġ( e . g . ĠGoogle ĠB IB REF 1 Ġand ĠFacebook ĠB IB REF 2 Ġ) Ġhave Ġbegun Ġefforts Ġto Ġtackle Ġfake Ġnews Ġas Ġthey Ġcan Ġaffect Ġpolitical Ġdecisions ĠB IB REF 3 Ġ. ĠYet , Ġthe Ġability Ġto Ġdefine , Ġidentify Ġand Ġstop Ġfake Ġnews Ġfrom Ġspreading Ġis Ġlimited . </s> Since Ġthe ĠObama Ġcampaign Ġin Ġ2008 , Ġsocial Ġmedia Ġhas Ġbeen Ġpervasive Ġin Ġthe Ġpolitical Ġarena Ġin Ġthe ĠUnited ĠStates . ĠStudies Ġreport Ġthat Ġup Ġto Ġ62 % Ġof ĠAmerican Ġadults Ġreceive Ġtheir Ġnews Ġfrom Ġsocial Ġmedia ĠB IB REF 4 Ġ. ĠThe Ġwide Ġuse Ġof Ġplatforms Ġsuch Ġas ĠTwitter Ġand ĠFacebook Ġhas Ġfacilitated Ġthe Ġdiffusion Ġof Ġfake Ġnews Ġby Ġsimpl ifying Ġthe Ġprocess Ġof Ġreceiving Ġcontent Ġwith Ġno Ġsignificant Ġthird Ġparty Ġfiltering , Ġfact - checking Ġor Ġeditorial Ġjudgement . ĠSuch Ġcharacteristics Ġmake Ġthese Ġplatforms Ġsuitable Ġmeans Ġfor Ġsharing Ġnews Ġthat , Ġdisguised Ġas Ġlegit Ġones , Ġtry Ġto Ġconfuse Ġreaders . </s> Such Ġuse Ġand Ġtheir Ġprominent Ġrise Ġhas Ġbeen Ġconfirmed Ġby ĠCraig ĠSilver man , Ġa ĠCanadian Ġjournalist Ġwho Ġis Ġa Ġprominent Ġfigure Ġon Ġfake Ġnews ĠB IB REF 5 Ġ: ĠâĢ ľ In Ġthe Ġfinal Ġthree Ġmonths Ġof Ġthe ĠUS Ġpresidential Ġcampaign , Ġthe Ġtop - performing Ġfake Ġelection Ġnews Ġstories Ġon ĠFacebook Ġgenerated Ġmore Ġengagement Ġthan Ġthe Ġtop Ġstories Ġfrom Ġmajor Ġnews Ġoutlet âĢ Ŀ . </s> Our Ġcurrent Ġresearch Ġhence Ġdep arts Ġfrom Ġthe Ġassumption Ġthat Ġsocial Ġmedia Ġis Ġa Ġconduit Ġfor Ġfake Ġnews Ġand Ġasks Ġthe Ġquestion Ġof Ġwhether Ġfake Ġnews Ġ( as Ġspam Ġwas Ġsome Ġyears Ġago ) Ġcan Ġbe Ġidentified , Ġmod elled Ġand Ġeventually Ġblocked . ĠIn Ġorder Ġto Ġdo Ġso , Ġwe Ġuse Ġa Ġsample Ġof Ġmore Ġthat Ġ1 . 5 M Ġtweets Ġcollected Ġon ĠNovember Ġ8 th Ġ2016 ĠâĢĶ election Ġday Ġin Ġthe ĠUnited ĠStates âĢĶ Ġwith Ġthe Ġgoal Ġof Ġidentifying Ġfeatures Ġthat Ġtweets Ġcontaining Ġfake Ġnews Ġare Ġlikely Ġto Ġhave . ĠAs Ġsuch , Ġour Ġpaper Ġaims Ġto Ġprovide Ġa Ġpreliminary Ġcharacterization Ġof Ġfake Ġnews Ġin ĠTwitter Ġby Ġlooking Ġinto Ġmeta - data Ġembedded Ġin Ġtweets . ĠConsidering Ġmeta - data Ġas Ġa Ġrelevant Ġfactor Ġof Ġanalysis Ġis Ġin Ġline Ġwith Ġfindings Ġreported Ġby ĠMorris Ġet Ġal . ĠB IB REF 6 Ġ. ĠWe Ġargue Ġthat Ġunderstanding Ġdifferences Ġbetween Ġtweets Ġcontaining Ġfake Ġnews Ġand Ġregular Ġtweets Ġwill Ġallow Ġresearchers Ġto Ġdesign Ġmechanisms Ġto Ġblock Ġfake Ġnews Ġin ĠTwitter . </s> Specifically , Ġour Ġgoals Ġare : Ġ1 ) Ġcompare Ġthe Ġcharacteristics Ġof Ġtweets Ġlabelled Ġas Ġcontaining Ġfake Ġnews Ġto Ġtweets Ġlabelled Ġas Ġnot Ġcontaining Ġthem , Ġ2 ) Ġcharacterize , Ġthrough Ġtheir Ġmeta - data , Ġviral Ġtweets Ġcontaining Ġfake Ġnews Ġand Ġthe Ġaccounts Ġfrom Ġwhich Ġthey Ġoriginated , Ġand Ġ3 ) Ġdetermine Ġthe Ġextent Ġto Ġwhich Ġtweets Ġcontaining Ġfake Ġnews Ġexpressed Ġpolarized Ġpolitical Ġviews . </s> For Ġour Ġstudy , Ġwe Ġused Ġthe Ġnumber Ġof Ġret we ets Ġto Ġsingle - out Ġthose Ġthat Ġwent Ġviral Ġwithin Ġour Ġsample . ĠTwe ets Ġwithin Ġthat Ġsubset Ġ( v iral Ġtweets Ġhereafter ) Ġare Ġvaried Ġand Ġrelate Ġto Ġdifferent Ġtopics . ĠWe Ġconsider Ġthat Ġa Ġtweet Ġcontains Ġfake Ġnews Ġif Ġits Ġtext Ġfalls Ġwithin Ġany Ġof Ġthe Ġfollowing Ġcategories Ġdescribed Ġby ĠRubin Ġet Ġal . ĠB IB REF 7 Ġ( see Ġnext Ġsection Ġfor Ġthe Ġdetails Ġof Ġsuch Ġcategories ): Ġserious Ġfabrication , Ġlarge - scale Ġhoax es , Ġjokes Ġtaken Ġat Ġface Ġvalue , Ġsl anted Ġreporting Ġof Ġreal Ġfacts Ġand Ġstories Ġwhere Ġthe Ġtruth Ġis Ġcontentious . ĠThe Ġdataset ĠB IB REF 8 Ġ, Ġmanually Ġlabelled Ġby Ġan Ġexpert , Ġhas Ġbeen Ġpublicly Ġreleased Ġand Ġis Ġavailable Ġto Ġresearchers Ġand Ġinterested Ġparties . </s> From Ġour Ġresults , Ġthe Ġfollowing Ġmain Ġobservations Ġcan Ġbe Ġmade : </s> Our Ġfindings Ġresonate Ġwith Ġsimilar Ġwork Ġdone Ġon Ġfake Ġnews Ġsuch Ġas Ġthe Ġone Ġfrom ĠAll cot Ġand ĠGent z k ow ĠB IB REF 9 Ġ. ĠTherefore , Ġeven Ġif Ġour Ġstudy Ġis Ġa Ġpreliminary Ġattempt Ġat Ġcharacter izing Ġfake Ġnews Ġon ĠTwitter Ġusing Ġonly Ġtheir Ġmeta - data , Ġour Ġresults Ġprovide Ġexternal Ġvalidity Ġto Ġprevious Ġresearch . ĠMoreover , Ġour Ġwork Ġnot Ġonly Ġstresses Ġthe Ġimportance Ġof Ġusing Ġmeta - data , Ġbut Ġalso Ġunderscores Ġwhich Ġparameters Ġmay Ġbe Ġuseful Ġto Ġidentify Ġfake Ġnews Ġon ĠTwitter . </s> The Ġrest Ġof Ġthe Ġpaper Ġis Ġorganized Ġas Ġfollows . ĠThe Ġnext Ġsection Ġbriefly Ġdiscusses Ġwhere Ġthis Ġwork Ġis Ġlocated Ġwithin Ġthe Ġliterature Ġon Ġfake Ġnews Ġand Ġcontextual izes Ġthe Ġtype Ġof Ġfake Ġnews Ġwe Ġare Ġstudying . ĠThen , Ġwe Ġpresent Ġour Ġhypotheses , Ġthe Ġdata , Ġand Ġthe Ġmethodology Ġwe Ġfollow . ĠFinally , Ġwe Ġpresent Ġour Ġfindings , Ġconclusions Ġof Ġthis Ġstudy , Ġand Ġfuture Ġlines Ġof Ġwork . </s> Def ining ĠFake Ġnews </s> Our Ġresearch Ġis Ġconnected Ġto Ġdifferent Ġstrands Ġof Ġacademic Ġknowledge Ġrelated Ġto Ġthe Ġphenomenon Ġof Ġfake Ġnews . ĠIn Ġrelation Ġto ĠComputer ĠScience , Ġa Ġrecent Ġsurvey Ġby ĠCon roy Ġand Ġcolleagues ĠB IB REF 10 Ġidentifies Ġtwo Ġpopular Ġapproaches Ġto Ġsingle - out Ġfake Ġnews . ĠOn Ġthe Ġone Ġhand , Ġthe Ġauthors Ġpointed Ġto Ġlinguistic Ġapproaches Ġconsisting Ġin Ġusing Ġtext , Ġits Ġlinguistic Ġcharacteristics Ġand Ġmachine Ġlearning Ġtechniques Ġto Ġautomatically Ġflag Ġfake Ġnews . ĠOn Ġthe Ġother , Ġthese Ġresearchers Ġundersc ored Ġthe Ġuse Ġof Ġnetwork Ġapproaches , Ġwhich Ġmake Ġuse Ġof Ġnetwork Ġcharacteristics Ġand Ġmeta - data , Ġto Ġidentify Ġfake Ġnews . </s> With Ġrespect Ġto Ġsocial Ġsciences , Ġefforts Ġfrom Ġpsychology , Ġpolitical Ġscience Ġand Ġsociology , Ġhave Ġbeen Ġdedicated Ġto Ġunderstand Ġwhy Ġpeople Ġconsume Ġand / or Ġbelieve Ġmisinformation ĠB IB REF 11 Ġ, ĠB IB REF 12 Ġ, ĠB IB REF 13 Ġ, ĠB IB REF 14 Ġ. ĠMost Ġof Ġthese Ġstudies Ġconsistently Ġreported Ġthat Ġpsychological Ġbiases Ġsuch Ġas Ġprim ing Ġeffects Ġand Ġconfirmation Ġbias Ġplay Ġan Ġimportant Ġrole Ġin Ġpeople Ġability Ġto Ġdiscern Ġmisinformation . </s> In Ġrelation Ġto Ġthe Ġproduction Ġand Ġdistribution Ġof Ġfake Ġnews , Ġa Ġrecent Ġpaper Ġin Ġthe Ġfield Ġof ĠEconomics ĠB IB REF 9 Ġfound Ġthat Ġmost Ġfake Ġnews Ġsites Ġuse Ġnames Ġthat Ġresemble Ġthose Ġof Ġlegitimate Ġorganizations , Ġand Ġthat Ġsites Ġsupplying Ġfake Ġnews Ġtend Ġto Ġbe Ġshort - lived . ĠThese Ġauthors Ġalso Ġnoticed Ġthat Ġfake Ġnews Ġitems Ġare Ġmore Ġlikely Ġshared Ġthan Ġlegitimate Ġarticles Ġcoming Ġfrom Ġtrusted Ġsources , Ġand Ġthey Ġtend Ġto Ġexhibit Ġa Ġlarger Ġlevel Ġof Ġpolarization . </s> The Ġconceptual Ġissue Ġof Ġhow Ġto Ġdefine Ġfake Ġnews Ġis Ġa Ġserious Ġand Ġunresolved Ġissue . ĠAs Ġthe Ġfocus Ġof Ġour Ġwork Ġis Ġnot Ġattempting Ġto Ġoffer Ġlight Ġon Ġthis , Ġwe Ġwill Ġrely Ġon Ġwork Ġby Ġother Ġauthors Ġto Ġdescribe Ġwhat Ġwe Ġconsider Ġas Ġfake Ġnews . ĠIn Ġparticular , Ġwe Ġuse Ġthe Ġcategor ization Ġprovided Ġby ĠRubin Ġet Ġal . ĠB IB REF 7 Ġ. ĠThe Ġfive Ġcategories Ġthey Ġdescribed , Ġtogether Ġwith Ġillust rative Ġexamples Ġfrom Ġour Ġdataset , Ġare Ġas Ġfollows : </s> Research ĠHyp othes es </s> Previous Ġworks Ġon Ġthe Ġarea Ġ( present ed Ġin Ġthe Ġsection Ġabove ) Ġsuggest Ġthat Ġthere Ġmay Ġbe Ġimportant Ġdetermin ants Ġfor Ġthe Ġadoption Ġand Ġdiffusion Ġof Ġfake Ġnews . ĠOur Ġhypotheses Ġbuilds Ġon Ġthem Ġand Ġidentifies Ġthree Ġimportant Ġdimensions Ġthat Ġmay Ġhelp Ġdistinguishing Ġfake Ġnews Ġfrom Ġlegit Ġinformation : </s> Taking Ġthose Ġthree Ġdimensions Ġinto Ġaccount , Ġwe Ġpropose Ġthe Ġfollowing Ġhypotheses Ġabout Ġthe Ġfeatures Ġthat Ġwe Ġbelieve Ġcan Ġhelp Ġto Ġidentify Ġtweets Ġcontaining Ġfake Ġnews Ġfrom Ġthose Ġnot Ġcontaining Ġthem . ĠThey Ġwill Ġbe Ġlater Ġtested Ġover Ġour Ġcollected Ġdataset . </s> Ex posure . </s> Character ization . </s> P olar ization . </s> Data Ġand ĠMethod ology </s> For Ġthis Ġstudy , Ġwe Ġcollected Ġpublicly Ġavailable Ġtweets Ġusing ĠTwitter 's Ġpublic ĠAPI . ĠGiven Ġthe Ġnature Ġof Ġthe Ġdata , Ġit Ġis Ġimportant Ġto Ġemphasize Ġthat Ġsuch Ġtweets Ġare Ġsubject Ġto ĠTwitter 's Ġterms Ġand Ġconditions Ġwhich Ġindicate Ġthat Ġusers Ġconsent Ġto Ġthe Ġcollection , Ġtransfer , Ġmanipulation , Ġstorage , Ġand Ġdisclosure Ġof Ġdata . ĠTherefore , Ġwe Ġdo Ġnot Ġexpect Ġethical , Ġlegal , Ġor Ġsocial Ġimplications Ġfrom Ġthe Ġusage Ġof Ġthe Ġtweets . ĠOur Ġdata Ġwas Ġcollected Ġusing Ġsearch Ġterms Ġrelated Ġto Ġthe Ġpresidential Ġelection Ġheld Ġin Ġthe ĠUnited ĠStates Ġon ĠNovember Ġ8 th Ġ2016 . ĠParticularly , Ġwe Ġquer ied ĠTwitter 's Ġstreaming ĠAPI , Ġmore Ġprecisely Ġthe Ġfilter Ġendpoint Ġof Ġthe Ġstreaming ĠAPI , Ġusing Ġthe Ġfollowing Ġhasht ags Ġand Ġuser Ġhandles : Ġ# My Vote 2016 , Ġ# E lection Day , Ġ# election night , Ġ@ realDonaldTrump Ġand Ġ@ Hillary Clinton . ĠThe Ġdata Ġcollection Ġran Ġfor Ġjust Ġone Ġday Ġ( Nov Ġ8 th Ġ2016 ). </s> One Ġstraightforward Ġway Ġof Ġsharing Ġinformation Ġon ĠTwitter Ġis Ġby Ġusing Ġthe Ġretweet Ġfunctionality , Ġwhich Ġenables Ġa Ġuser Ġto Ġshare Ġa Ġexact Ġcopy Ġof Ġa Ġtweet Ġwith Ġhis Ġfollowers . ĠAmong Ġthe Ġreasons Ġfor Ġretweet ing , ĠBody Ġet Ġal . ĠB IB REF 15 Ġreported Ġthe Ġwill Ġto : Ġ1 ) Ġspread Ġtweets Ġto Ġa Ġnew Ġaudience , Ġ2 ) Ġto Ġshow Ġone âĢ Ļ s Ġrole Ġas Ġa Ġlistener , Ġand Ġ3 ) Ġto Ġagree Ġwith Ġsomeone Ġor Ġvalidate Ġthe Ġthoughts Ġof Ġothers . ĠAs Ġindicated , Ġour Ġinitial Ġinterest Ġis Ġto Ġcharacterize Ġtweets Ġcontaining Ġfake Ġnews Ġthat Ġwent Ġviral Ġ( as Ġthey Ġare Ġthe Ġmost Ġharmful Ġones , Ġas Ġthey Ġreach Ġa Ġwider Ġaudience ), Ġand Ġunderstand Ġhow Ġit Ġdiffers Ġfrom Ġother Ġviral Ġtweets Ġ( that Ġdo Ġnot Ġcontain Ġfake Ġnews ). ĠFor Ġour Ġstudy , Ġwe Ġconsider Ġthat Ġa Ġtweet Ġwent Ġviral Ġif Ġit Ġwas Ġretweet ed Ġmore Ġthan Ġ1000 Ġtimes . </s> Once Ġwe Ġhave Ġthe Ġdataset Ġof Ġviral Ġtweets , Ġwe Ġeliminated Ġdupl icates Ġ( some Ġof Ġthe Ġtweets Ġwere Ġcollected Ġseveral Ġtimes Ġbecause Ġthey Ġhad Ġseveral Ġhandles ) Ġand Ġan Ġexpert Ġmanually Ġinspected Ġthe Ġtext Ġfield Ġwithin Ġthe Ġtweets Ġto Ġlabel Ġthem Ġas Ġcontaining Ġfake Ġnews , Ġor Ġnot Ġcontaining Ġthem Ġ( according Ġto Ġthe Ġcharacterization Ġpresented Ġbefore ). ĠThis Ġannot ated Ġdataset ĠB IB REF 8 Ġis Ġpublicly Ġavailable Ġand Ġcan Ġbe Ġfreely Ġreused . </s> Finally , Ġwe Ġuse Ġthe Ġfollowing Ġfields Ġwithin Ġtweets Ġ( from Ġthe Ġones Ġreturned Ġby ĠTwitter 's ĠAPI ) Ġto Ġcompare Ġtheir Ġdistributions Ġand Ġlook Ġfor Ġdifferences Ġbetween Ġviral Ġtweets Ġcontaining Ġfake Ġnews Ġand Ġviral Ġtweets Ġnot Ġcontaining Ġfake Ġnews : </s> In Ġthe Ġfollowing Ġsection , Ġwe Ġprovide Ġgraphical Ġdescriptions Ġof Ġthe Ġdistribution Ġof Ġeach Ġof Ġthe Ġidentified Ġattributes Ġfor Ġthe Ġtwo Ġsets Ġof Ġtweets Ġ( those Ġlabelled Ġas Ġcontaining Ġfake Ġnews Ġand Ġthose Ġlabelled Ġas Ġnot Ġcontaining Ġthem ). ĠWhere Ġappropriate , Ġwe Ġnormalized Ġand / or Ġtook Ġlog ar ith ms Ġof Ġthe Ġdata Ġfor Ġbetter Ġrepresentation . ĠTo Ġgain Ġa Ġbetter Ġunderstanding Ġof Ġthe Ġsignificance Ġof Ġthose Ġdifferences , Ġwe Ġuse Ġthe ĠKol m og or ov - Sm ir nov Ġtest Ġwith Ġthe Ġnull Ġhypothesis Ġthat Ġboth Ġdistributions Ġare Ġequal . </s> Results </s> The Ġsample Ġcollected Ġconsisted Ġon Ġ1 Ġ7 85 Ġ8 55 Ġtweets Ġpublished Ġby Ġ8 48 Ġ196 Ġdifferent Ġusers . ĠWithin Ġour Ġsample , Ġwe Ġidentified Ġ13 27 Ġtweets Ġthat Ġwent Ġviral Ġ( ret weet ed Ġmore Ġthan Ġ1000 Ġtimes Ġby Ġthe Ġ8 th Ġof ĠNovember Ġ2016 ) Ġproduced Ġby Ġ6 43 Ġusers . ĠSuch Ġsmall Ġsubset Ġof Ġviral Ġtweets Ġwere Ġretweet ed Ġon Ġ290 Ġ8 41 Ġoccasions Ġin Ġthe Ġobserved Ġtime - window . </s> The Ġ13 27 Ġ` v iral ' Ġtweets Ġwere Ġmanually Ġannot ated Ġas Ġcontaining Ġfake Ġnews Ġor Ġnot . ĠThe Ġannotation Ġwas Ġcarried Ġout Ġby Ġa Ġsingle Ġperson Ġin Ġorder Ġto Ġobtain Ġa Ġconsistent Ġannotation Ġthroughout Ġthe Ġdataset . ĠOut Ġof Ġthose Ġ13 27 Ġtweets , Ġwe Ġidentified Ġ136 Ġas Ġpotentially Ġcontaining Ġfake Ġnews Ġ( according Ġto Ġthe Ġcategories Ġpreviously Ġdescribed ), Ġand Ġthe Ġrest Ġwere Ġclassified Ġas Ġ` non Ġcontaining Ġfake Ġnews '. ĠNote Ġthat Ġthe Ġcategor ization Ġis Ġfar Ġfrom Ġbeing Ġperfect Ġgiven Ġthe Ġambiguity Ġof Ġfake Ġnews Ġthemselves Ġand Ġhuman Ġjudgement Ġinvolved Ġin Ġthe Ġprocess Ġof Ġcategor ization . ĠBecause Ġof Ġthis , Ġwe Ġdo Ġnot Ġclaim Ġthat Ġthis Ġdataset Ġcan Ġbe Ġconsidered Ġa Ġground Ġtruth . </s> The Ġfollowing Ġresults Ġdetail Ġcharacteristics Ġof Ġthese Ġtweets Ġalong Ġthe Ġpreviously Ġmentioned Ġdimensions . ĠTable ĠT AB REF 23 Ġreports Ġthe Ġactual Ġdifferences Ġ( together Ġwith Ġtheir Ġassociated Ġp - values ) Ġof Ġthe Ġdistributions Ġof Ġviral Ġtweets Ġcontaining Ġfake Ġnews Ġand Ġviral Ġtweets Ġnot Ġcontaining Ġthem Ġfor Ġevery Ġvariable Ġconsidered . </s> Ex posure </s> Figure ĠFIG REF 24 Ġshows Ġthat , Ġin Ġcontrast Ġto Ġother Ġkinds Ġof Ġviral Ġtweets , Ġthose Ġcontaining Ġfake Ġnews Ġwere Ġcreated Ġmore Ġrecently . ĠAs Ġsuch , ĠTwitter Ġusers Ġwere Ġexposed Ġto Ġfake Ġnews Ġrelated Ġto Ġthe Ġelection Ġfor Ġa Ġshorter Ġperiod Ġof Ġtime . </s> However , Ġin Ġterms Ġof Ġret we ets , ĠFigure ĠFIG REF 25 Ġshows Ġno Ġapparent Ġdifference Ġbetween Ġcontaining Ġfake Ġnews Ġor Ġnot Ġcontaining Ġthem . ĠThat Ġis Ġconfirmed Ġby Ġthe ĠKol m og or ov - Sm ir n off Ġtest , Ġwhich Ġdoes Ġnot Ġdiscard Ġthe Ġhypothesis Ġthat Ġthe Ġassociated Ġdistributions Ġare Ġequal . </s> In Ġrelation Ġto Ġthe Ġnumber Ġof Ġfavourites , Ġusers Ġthat Ġgenerated Ġat Ġleast Ġa Ġviral Ġtweet Ġcontaining Ġfake Ġnews Ġappear Ġto Ġhave , Ġon Ġaverage , Ġless Ġfavourites Ġthan Ġusers Ġthat Ġdo Ġnot Ġgenerate Ġthem . ĠFigure ĠFIG REF 26 Ġshows Ġthe Ġdistribution Ġof Ġfavourites . ĠDespite Ġthe Ġapparent Ġvisual Ġdifferences , Ġthe Ġdifference Ġare Ġnot Ġstatistically Ġsignificant . </s> Finally , Ġthe Ġnumber Ġof Ġhasht ags Ġused Ġin Ġviral Ġfake Ġnews Ġappears Ġto Ġbe Ġlarger Ġthan Ġthose Ġin Ġother Ġviral Ġtweets . ĠFigure ĠFIG REF 27 Ġshows Ġthe Ġdensity Ġdistribution Ġof Ġthe Ġnumber Ġof Ġhasht ags Ġused . ĠHowever , Ġonce Ġagain , Ġwe Ġwere Ġnot Ġable Ġto Ġfind Ġany Ġstatistical Ġdifference Ġbetween Ġthe Ġaverage Ġnumber Ġof Ġhasht ags Ġin Ġa Ġviral Ġtweet Ġand Ġthe Ġaverage Ġnumber Ġof Ġhasht ags Ġin Ġviral Ġfake Ġnews . </s> Character ization </s> We Ġfound Ġthat Ġ82 Ġusers Ġwithin Ġour Ġsample Ġwere Ġspreading Ġfake Ġnews Ġ( i . e . Ġthey Ġproduced Ġat Ġleast Ġone Ġtweet Ġwhich Ġwas Ġlabelled Ġas Ġfake Ġnews ). ĠOut Ġof Ġthose , Ġ34 Ġhad Ġverified Ġaccounts , Ġand Ġthe Ġrest Ġwere Ġun verified . ĠFrom Ġthe Ġ48 Ġun verified Ġaccounts , Ġ6 Ġhave Ġbeen Ġsuspended Ġby ĠTwitter Ġat Ġthe Ġdate Ġof Ġwriting , Ġ3 Ġtried Ġto Ġimitate Ġlegitimate Ġaccounts Ġof Ġothers , Ġand Ġ4 Ġaccounts Ġhave Ġbeen Ġalready Ġdeleted . ĠFigure ĠFIG REF 28 Ġshows Ġthe Ġproportion Ġof Ġverified Ġaccounts Ġto Ġun verified Ġaccounts Ġfor Ġviral Ġtweets Ġ( containing Ġfake Ġnews Ġvs . Ġnot Ġcontaining Ġfake Ġnews ). ĠFrom Ġthe Ġchart , Ġit Ġis Ġclear Ġthat Ġthere Ġis Ġa Ġhigher Ġchance Ġof Ġfake Ġnews Ġcoming Ġfrom Ġun verified Ġaccounts . </s> Turn ing Ġto Ġfriends , Ġaccounts Ġdistributing Ġfake Ġnews Ġappear Ġto Ġhave , Ġon Ġaverage , Ġthe Ġsame Ġnumber Ġof Ġfriends Ġthan Ġthose Ġdistributing Ġtweets Ġwith Ġno Ġfake Ġnews . ĠHowever , Ġthe Ġdensity Ġdistribution Ġof Ġfriends Ġfrom Ġthe Ġaccounts Ġ( Figure ĠFIG REF 29 Ġ) Ġshows Ġthat Ġthere Ġis Ġindeed Ġa Ġstatistically Ġsignificant Ġdifference Ġin Ġtheir Ġdistributions . </s> If Ġwe Ġtake Ġinto Ġconsideration Ġthe Ġnumber Ġof Ġfollowers , Ġaccounts Ġgenerating Ġviral Ġtweets Ġwith Ġfake Ġnews Ġdo Ġhave Ġa Ġvery Ġdifferent Ġdistribution Ġon Ġthis Ġdimension , Ġcompared Ġto Ġthose Ġaccounts Ġgenerating Ġviral Ġtweets Ġwith Ġno Ġfake Ġnews Ġ( see ĠFigure ĠFIG REF 30 Ġ). ĠIn Ġfact , Ġsuch Ġdifferences Ġare Ġstatistically Ġsignificant . </s> A Ġuseful Ġrepresentation Ġfor Ġfriends Ġand Ġfollowers Ġis Ġthe Ġratio Ġbetween Ġfriends / follow ers . ĠFigures ĠFIG REF 31 Ġand ĠFIG REF 32 Ġshow Ġthis Ġrepresentation . ĠNotice Ġthat Ġaccounts Ġspreading Ġviral Ġtweets Ġwith Ġfake Ġnews Ġhave , Ġon Ġaverage , Ġa Ġlarger Ġratio Ġof Ġfriends / follow ers . ĠThe Ġdistribution Ġof Ġthose Ġaccounts Ġnot Ġgenerating Ġfake Ġnews Ġis Ġmore Ġevenly Ġdistributed . </s> With Ġrespect Ġto Ġthe Ġnumber Ġof Ġmentions , ĠFigure ĠFIG REF 33 Ġshows Ġthat Ġviral Ġtweets Ġlabelled Ġas Ġcontaining Ġfake Ġnews Ġappear Ġto Ġuse Ġmentions Ġto Ġother Ġusers Ġless Ġfrequently Ġthan Ġviral Ġtweets Ġnot Ġcontaining Ġfake Ġnews . ĠIn Ġother Ġwords , Ġtweets Ġcontaining Ġfake Ġnews Ġmostly Ġcontain Ġ1 Ġmention , Ġwhereas Ġother Ġtweets Ġtend Ġto Ġhave Ġtwo ). ĠSuch Ġdifferences Ġare Ġstatistically Ġsignificant . </s> The Ġanalysis Ġ( Figure ĠFIG REF 34 Ġ) Ġof Ġthe Ġpresence Ġof Ġmedia Ġin Ġthe Ġtweets Ġin Ġour Ġdataset Ġshows Ġthat Ġtweets Ġlabelled Ġas Ġnot Ġcontaining Ġfake Ġnews Ġappear Ġto Ġpresent Ġmore Ġmedia Ġelements Ġthan Ġthose Ġlabelled Ġas Ġfake Ġnews . ĠHowever , Ġthe Ġdifference Ġis Ġnot Ġstatistically Ġsignificant . </s> On Ġthe Ġother Ġhand , ĠFigure ĠFIG REF 35 Ġshows Ġthat Ġviral Ġtweets Ġcontaining Ġfake Ġnews Ġappear Ġto Ġinclude Ġmore ĠURLs Ġto Ġother Ġsites Ġthan Ġviral Ġtweets Ġthat Ġdo Ġnot Ġcontain Ġfake Ġnews . ĠIn Ġfact , Ġthe Ġdifference Ġbetween Ġthe Ġtwo Ġdistributions Ġis Ġstatistically Ġsignificant Ġ( assuming ĠIN LINE FORM 0 Ġ). </s> P olar ization </s> Finally , Ġmanual Ġinspection Ġof Ġthe Ġtext Ġfield Ġof Ġthose Ġviral Ġtweets Ġlabelled Ġas Ġcontaining Ġfake Ġnews Ġshows Ġthat Ġ117 Ġof Ġsuch Ġtweets Ġexpressed Ġsupport Ġfor ĠDonald ĠTrump , Ġwhile Ġonly Ġ8 Ġsupported ĠHillary ĠClinton . ĠThe Ġremaining Ġtweets Ġcontained Ġfake Ġnews Ġrelated Ġto Ġother Ġtopics , Ġnot Ġexpressing Ġsupport Ġfor Ġany Ġof Ġthe Ġcandidates . </s> Discussion </s> As Ġa Ġsummary , Ġand Ġconstrained Ġby Ġour Ġexisting Ġdataset , Ġwe Ġmade Ġthe Ġfollowing Ġobservations Ġregarding Ġdifferences Ġbetween Ġviral Ġtweets Ġlabelled Ġas Ġcontaining Ġfake Ġnews Ġand Ġviral Ġtweets Ġlabelled Ġas Ġnot Ġcontaining Ġthem : </s> These Ġfindings Ġ( related Ġto Ġour Ġinitial Ġhypothesis Ġin ĠTable ĠT AB REF 44 Ġ) Ġclearly Ġsuggest Ġthat Ġthere Ġare Ġspecific Ġpieces Ġof Ġmeta - data Ġabout Ġtweets Ġthat Ġmay Ġallow Ġthe Ġidentification Ġof Ġfake Ġnews . ĠOne Ġsuch Ġparameter Ġis Ġthe Ġtime Ġof Ġexposure . ĠVir al Ġtweets Ġcontaining Ġfake Ġnews Ġare Ġshorter - lived Ġthan Ġthose Ġcontaining Ġother Ġtype Ġof Ġcontent . ĠThis Ġnotion Ġseems Ġto Ġresonate Ġwith Ġour Ġfindings Ġshowing Ġthat Ġa Ġnumber Ġof Ġaccounts Ġspreading Ġfake Ġnews Ġhave Ġalready Ġbeen Ġdeleted Ġor Ġsuspended Ġby ĠTwitter Ġby Ġthe Ġtime Ġof Ġwriting . ĠIf Ġone Ġconsiders Ġthat Ġresearchers Ġusing Ġdifferent Ġdata Ġhave Ġfound Ġsimilar Ġresults ĠB IB REF 9 Ġ, Ġit Ġappears Ġthat Ġthe Ġlifetime Ġof Ġaccounts , Ġtogether Ġwith Ġthe Ġage Ġof Ġthe Ġquestioned Ġviral Ġcontent Ġcould Ġbe Ġuseful Ġto Ġidentify Ġfake Ġnews . ĠIn Ġthe Ġlight Ġof Ġthis Ġfinding , Ġaccounts Ġnewly Ġcreated Ġshould Ġprobably Ġput Ġunder Ġhigher Ġscrutiny Ġthan Ġolder Ġones . ĠThis Ġin Ġfact , Ġwould Ġbe Ġa Ġnice Ġa - pri ori Ġbias Ġfor Ġa ĠBay esian Ġclass ifier . </s> Account s Ġspreading Ġfake Ġnews Ġappear Ġto Ġhave Ġa Ġlarger Ġproportion Ġof Ġfriends / follow ers Ġ( i . e . Ġthey Ġhave , Ġon Ġaverage , Ġthe Ġsame Ġnumber Ġof Ġfriends Ġbut Ġa Ġsmaller Ġnumber Ġof Ġfollowers ) Ġthan Ġthose Ġspreading Ġviral Ġcontent Ġonly . ĠTogether Ġwith Ġthe Ġfact Ġthat , Ġon Ġaverage , Ġtweets Ġcontaining Ġfake Ġnews Ġhave Ġmore ĠURLs Ġthan Ġthose Ġspreading Ġviral Ġcontent , Ġit Ġis Ġpossible Ġto Ġhypothes ize Ġthat , Ġboth , Ġthe Ġratio Ġof Ġfriends / follow ers Ġof Ġthe Ġaccount Ġproducing Ġa Ġviral Ġtweet Ġand Ġnumber Ġof ĠURLs Ġcontained Ġin Ġsuch Ġa Ġtweet Ġcould Ġbe Ġuseful Ġto Ġsingle - out Ġfake Ġnews Ġin ĠTwitter . ĠNot Ġonly Ġthat , Ġbut Ġour Ġfinding Ġrelated Ġto Ġthe Ġnumber Ġof ĠURLs Ġis Ġin Ġline Ġwith Ġintu itions Ġbehind Ġthe Ġincentives Ġto Ġcreate Ġfake Ġnews Ġcommonly Ġfound Ġin Ġthe Ġliterature ĠB IB REF 9 Ġ( in Ġparticular Ġthat Ġof Ġobtaining Ġrevenue Ġthrough Ġclick - through Ġadvertising ). </s> Finally , Ġit Ġis Ġinteresting Ġto Ġnotice Ġthat Ġthe Ġcontent Ġof Ġviral Ġfake Ġnews Ġwas Ġhighly Ġpolarized . ĠThis Ġfinding Ġis Ġalso Ġin Ġline Ġwith Ġthose Ġof ĠAl cott Ġet Ġal . ĠB IB REF 9 Ġ. ĠThis Ġfeature Ġsuggests Ġthat Ġtextual Ġsentiment Ġanalysis Ġof Ġthe Ġcontent Ġof Ġtweets Ġ( as Ġmost Ġresearchers Ġdo ), Ġtogether Ġwith Ġthe Ġabove Ġmentioned Ġparameters Ġfrom Ġmeta - data , Ġmay Ġprove Ġuseful Ġfor Ġidentifying Ġfake Ġnews . </s> Con clusions </s> With Ġthe Ġelection Ġof ĠDonald ĠTrump Ġas ĠPresident Ġof Ġthe ĠUnited ĠStates , Ġthe Ġconcept Ġof Ġfake Ġnews Ġhas Ġbecome Ġa Ġbroadly - known Ġphenomenon Ġthat Ġis Ġgetting Ġtremendous Ġattention Ġfrom Ġgovernments Ġand Ġmedia Ġcompanies . ĠWe Ġhave Ġpresented Ġa Ġpreliminary Ġstudy Ġon Ġthe Ġmeta - data Ġof Ġa Ġpublicly Ġavailable Ġdataset Ġof Ġtweets Ġthat Ġbecame Ġviral Ġduring Ġthe Ġday Ġof Ġthe Ġ2016 ĠUS Ġpresidential Ġelection . ĠOur Ġaim Ġis Ġto Ġadvance Ġthe Ġunderstanding Ġof Ġwhich Ġfeatures Ġmight Ġbe Ġcharacteristic Ġof Ġviral Ġtweets Ġcontaining Ġfake Ġnews Ġin Ġcomparison Ġwith Ġviral Ġtweets Ġwithout Ġfake Ġnews . </s> We Ġbelieve Ġthat Ġthe Ġonly Ġway Ġto Ġautomatically Ġidentify Ġthose Ġdeceit ful Ġtweets Ġ( i . e . Ġcontaining Ġfake Ġnews ) Ġis Ġby Ġactually Ġunderstanding Ġand Ġmodelling Ġthem . ĠOnly Ġthen , Ġthe Ġautomation Ġof Ġthe Ġprocesses Ġof Ġtagging Ġand Ġblocking Ġthese Ġtweets Ġcan Ġbe Ġsuccessfully Ġperformed . ĠIn Ġthe Ġsame Ġway Ġthat Ġspam Ġwas Ġfought , Ġwe Ġanticipate Ġfake Ġnews Ġwill Ġsuffer Ġa Ġsimilar Ġevolution , Ġwith Ġsocial Ġplatforms Ġimplementing Ġtools Ġto Ġdeal Ġwith Ġthem . ĠWith Ġmost Ġworks Ġso Ġfar Ġfocusing Ġon Ġthe Ġactual Ġcontent Ġof Ġthe Ġtweets , Ġours Ġis Ġa Ġnovel Ġattempt Ġfrom Ġa Ġdifferent , Ġbut Ġalso Ġcomplementary , Ġangle . </s> Within Ġthe Ġused Ġdataset , Ġwe Ġfound Ġthere Ġare Ġdifferences Ġaround Ġexposure , Ġcharacteristics Ġof Ġaccounts Ġspreading Ġfake Ġnews Ġand Ġthe Ġtone Ġof Ġthe Ġcontent . ĠThose Ġfindings Ġsuggest Ġthat Ġit Ġis Ġindeed Ġpossible Ġto Ġmodel Ġand Ġautomatically Ġdetect Ġfake Ġnews . ĠWe Ġplan Ġto Ġreplicate Ġand Ġvalidate Ġour Ġexperiments Ġin Ġan Ġextended Ġsample Ġof Ġtweets Ġ( until Ġ4 Ġmonths Ġafter Ġthe ĠUS Ġelection ), Ġand Ġtests Ġthe Ġpredictive Ġpower Ġof Ġthe Ġfeatures Ġwe Ġfound Ġrelevant Ġwithin Ġour Ġsample . </s> Author ĠDisclosure ĠStatement </s> No Ġcompeting Ġfinancial Ġinterest Ġexist .