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Visual Network Exploration for Data JournalistsTommaso Venturini, Mathieu Jacomy, Liliana Bounegru, Jonathan Gray

To cite this version:Tommaso Venturini, Mathieu Jacomy, Liliana Bounegru, Jonathan Gray. Visual Network Explorationfor Data Journalists. Scott A. Eldridge II; Bob Franklin. The Routledge Handbook of Developmentsin Digital Journalism Studies, Routledge, 2018, 9781138283053. �hal-01672282�

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VISUALNETWORKEXPLORATIONFORDATAJOURNALISTSTOMMASOVENTURINI,MATHIEUJACOMY,LILIANABOUNEGRU,JONATHANGRAY

Networksareclassicbutunder-acknowledgedfiguresofjournalisticstorytelling.Whoisconnectedtowhomandbywhichmeans?Whichorganizationsreceivesupportfromwhichothers?Whatresourcesorinformationcirculatethroughwhichchannelsandwhichintermediariesenableandregulatetheirflows?Theseareallcustomarystoriesandlinesofinquiryinjournalismandtheyallhavetodowithnetworks.Additionally,therecentspreadofdigitalmediahasincreasinglyconfrontedjournalistswithinformationcomingnotonlyinthetraditionalformofstatistictables,butalsoofrelationaldatabases.Yet,journalistshavesofarmadelittleuseoftheanalyticalresourcesofferedbynetworks.Toaddressthisprobleminthischapterweexaminehow“visualnetworkexploration”maybebroughttobearinthecontextofdatajournalisminordertoexplore,narrateandmakesenseoflargeandcomplexrelationaldatasets.Weborrowthemorefamiliarvocabularyofgeographicalmapstoshowhowkeygraphicalvariablessuchasposition,sizeandhuecanbeusedtointerpretandcharacterisegraphstructuresandproperties.Weillustratethistechniquebytakingasastartingpointarecentexamplefromjournalism,namelyacatalogueofFrenchinformationsourcescompiledbyLeMonde’sTheDecodex.Weestablishthatgoodvisualexplorationofnetworksisaniterativeprocesswherepracticestodemarcatecategoriesandterritoriesareentangledandmutuallyconstitutive.Toenrichinvestigationwesuggestwaysinwhichtheinsightsofthevisualexplorationofnetworkscanbesupplementedwithsimplecalculationsandstatisticsofdistributionsofnodesandlinksacrossthenetwork.Weconcludewithreflectionontheknowledge-makingcapacitiesofthistechniqueandhowthesecomparetotheinsightsandinstrumentsthatjournalistshaveusedintheDecodexproject–suggestingthatvisualnetworkexplorationisafertileareaforfurtherexplorationandcollaborationsbetweendatajournalistsanddigitalresearchers.

INTRODUCTIONFewpeopleknowaswellasjournaliststhattheworldismadeofrelations.Followingalliances,unveilinglinks,unravellingthreadsis,andhaslongbeen,acentralpartoftheirinvestigations.Ifsocialscientistscanspeculateaboutlongstandingstructuresandglobalarrangements,journalistshavenosuchleisure.Theirworkconsistsintracingthespecificassociationsthatconnectindividualsandinstitutionstouncoverhowlumpsofmoney,influenceandknowledgeareexchangedthroughthemandwhereunethicalbehaviour,corruption,fraudorunfairpoliticalinfluencemayoccur.Theadventofdigitaltechnologieshasmadesuchworkbotheasierandmoredifficult.Easier,becauseithasincreasedthetraceabilityofeconomicandpoliticalassociations.Moredifficult,becauseithassubmergedjournalistswithmoreinformationthantheirinvestigativetoolkitisusedtohandling.

When,forexample,thereportersoftheInternationalConsortiumforInvestigativeJournalism(ICIJ)receivedthe2,6terabytesand11,5milliondocumentscomposingtheso-called'PanamaPapers',theyobviouslycouldnotprocessthemmanually(Baruch&Vaudano,2016).Notethatthisisnotjusta‘bigdata’problem.Thetroublewiththeleakwasnotonlyitssize,butthefactthatitsinterestcamefromthelinksitestablishedbetweenspecificindividualsandparticulartax-havens.Extracting“key”figuresthroughstatisticalaggregationorabstractedcomputationalmodelswouldmissthepointofmanyofthestoriesthatjournalistsweremostkeentoexplore.Theinquirycouldnotsimplifythedataset,buthadtoexploreeachandeveryoneoftheconnectionsitexposed.Thiswasdone,amongothersways,throughatoolcalledLinkurious(http://linkurio.us),whoseinterestcomeslessfromitscomputationalpowerthanfromthewayinwhichitallowsitsuserstoseeandfollowtheconnectionsofanetwork.

ThePanamaPapercaseisinteresting,butalsointerestinglyisolated.Despitelongstandinginterest,theuseofnetworksinjournalismremainscomparativelymarginal(cf.Bounegruetal.,2016foranoverviewoftheemergingusesofnetworksinjournalism).Thereasonsarenotdifficulttoimagine.Graphmathematicsismoredemandingandlesswidelyknownthantraditionalstatisticalapproachesanddoesnotcomewiththesamereadilyaccessibleandpubliclyrecognisedvocabularyofvisualmotifs.Withallits

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computationalpower,graphmathematicsdoesnotfitjournalisticneedsbecauseittendstobeobscureforbothreportersandtheirreaders.

Inthischapter,weaddressthisdifficultybysuggestingatechniqueforthevisualexplorationofnetworks.Aswewilltrytoshow,whenperformedcorrectly,thevisualrepresentationofnetworktranslatessomeofthemostimportantgraphstructuresintographicalvariables(therebysupportinginvestigativework)andallowingtheinterpretationofnetworkswithconventionssimilartothosedevelopedforgeographicalmaps(therebyremaininglegibleforalargeaudience).Afterhavingintroducedthemathematicalandhistoricalbasesofourapproach,wewillpresentourtechniqueforthevisualexplorationofnetworks.Usingasanexample,athenetworkoftheFrenchinformationsphere,wewillillustratetherecursiveworkofinterpretationandcategorisationthatallowtoreadthenetworkasanorganisedterritory.Visualnetworkexploration,whichisgrowinginprominenceamongstdigitalmethodsresearchersforsocialandculturalresearch,maybeusefulnotonlyforstudyingmedialandscapes,butalsofordigitaljournalismpractitionerswhoareinterestedinexploringandtellingstorieswithnetworksandrelationaldata.

UNDERSTANDINGFORCE-DIRECTEDLAYOUTSFarfrombeingmerelyaesthetic,thegraphicalrepresentationofnetworkshasanintrinsichermeneuticvalue,whichyouwillhaveexperiencedifyouhaveeverusedapublictransportationmap.Suchmapsaredistinctivelydifferentfromroadmapsorcitymaps.Itisnotonlythattransportationmapsaresimpler(thelevelofdetailsdependingonlyontheresolutionofthemap),itisthattheyrepresentanetworkandnotageographicalterritory.AnillustrationofthisdifferencecanbefoundinthefamousmapoftheLondontubeasdesignedbyHarryBeckin1933.BeforeBeck’sredesign,thediagramwasaclassicgeographicalmaplocatingstationsaccordingtotheircoordinates.Aftertheredesign,itbecameanetworkofcorrespondencesinwhichstationsarepositionedaccordingtotheirrelativeproximityandconnectivity.Thegaininlegibilityisevidentasthefunctionofthetransportationmapisnottosituatestationsinurbanspace,butrelativetoeachother,soastohelpuserstomovefromonetoanother(atypeoforientationthatresemblesstrikinglytooneusedbyoftraditionalseanavigators,see,forexample,Turnbull,2000,pp.133-165).

a. b.

Figure1.Londontubemap(a)in1920beforeBeckredesignand(b)in1933afterBeckredesign.

AnotherexampleofsuchmappingapproachcomesfromearlyworksinSocialNetworkAnalysis(Freeman,2000).JacobMoreno,founderofSNA,isexplicitabouttheimportanceofvisualization:'Aprocessofchartinghasbeendevisedbythesociometrists,thesociogram,whichismorethanmerelyamethodofpresentation.Itisfirstofallamethodofexploration'(1953,pp.95-96).InaninterviewreleasedbyMorenototheNewYorkTimesin1933,networkanalysisispresentedasa'newgeography'.Moreimportantthanthetitle,however,isthefigurethataccompaniesthatinterview,depictingfriendshipsamongfourthgradepupils.Thesociogrampresentedbythesefigurespowerfullyrevealshowfriendshipisnotequallydistributedintheclass.Oneonlyneedtoknowthattrianglesrepresentboysandcirclesgirlstoseehowinter-genderrelationshipsarediscouragedatthatspecificage(oratleastthedeclarationofsuchfriendships).Thetrick,ofcourse,onlyworksbecausethenodesarenotpositioned

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randomlyinthespace,butinawaythatminimizesline-crossing(inMoreno’sownwords'thefewerthenumberoflinescrossing,thebetterthesociogram',1953,p.141).Itisbecausetrianglesarepushedononesideandcirclesonanotherthatitiseasytospottheexistenceofasingleinter-genderconnection.

a. b.

Figure2.Sociogramrepresentingfriendshipamongschoolpupils(originaltitleandimageaccompanyingMoreno’s1933NewYorkTimeinterview)(a)intheoriginalversionand(b)inthemodernforce-directedspatialisation.

Moreno’sruleofspatialisationiseasytofollowonagraphofafewdozennodesandedgesbutimpracticableonlargernetworks.Graphswiththousandsofnodesandedgesaresointricatethatthedirectcountingofline-crossingsbecomesprohibitivelytime-consuming.Anindirectapproachconsistsofdrawingclosertheconnectednodestominimizethelengthoftheedgesandthereforethepossibilityofcrossings.Buteveninthiscase,sinceeachnodemaybeconnectedtoseveralothernodeswhicharethemselvesconnectedmanyothernodes,minimizingthelengthoftheedgesisfarfromatrivialexercise.

Thuswemightexplorethenetworkusingatechniquecalled'force-directedspatialisation'.Suchspatialisationfollowsaphysicalanalogy:nodesarechargedwitharepulsiveforcethatdrivesthemapart,whileedgesactasspringsbindingthenodesthattheyconnect.Oncethealgorithmislauncheditchangesthedispositionofnodesuntilitreachesabalancesuchofforces(Jacomyetal.,2014).Suchequilibriumreducesline-crossingsandimprovesthelegibilityofthegraph.FrüchtermanandReingold(1991),whoproposedthefirstefficientforce-directedalgorithm,citeline-crossingasthesecondoftheiraestheticcriteria.

Yet,scholarsworkingwithnetworkssoonrealisedthatavoidingline-crossingisnotthemostinterestingeffectofforce-directedlayouts.Atequilibrium,thevisualdensityofnodesandedgesbecomesanapproximatebutreliableproxyofthemathematicalstructureofthegraph(foradetailedmathematicalproof,seeVenturinietal.,forthcoming).Groupsofnodesgatheringinthelayouttendtocorrespondtotheclustersidentifiedbycommunity-detectiontechniques(Noack,2009);structuralholes(Burt,1995)tendtolooklikesparserzones;centralnodesmovetowardsmiddlepositions;andbridgesarepositionedsomewaybetweendifferentregions(Jensenetal.,2015).

Thetrickofforce-directedalgorithmsisallthemoreremarkable,giventhatthespaceofnetworksisrelativeratherthanabsolute(itcanberotatedormirroredwithoutdistortionofinformation)andthatitisaconsequenceandnotaconditionofelementpositioning.Intraditionalgeographicalrepresentation,thespaceisdefinedaprioribythewaythehorizontalandverticalaxesareconstructed.Pointsareprojectedonsuchpre-existingspaceaccordingtoasetofrulesthatassignaunivocalpositiontoapairofcoordinates.ThesameistrueforanyCartesiandiagram(scatterplotsforinstance),butnotfornetworks,inwhichthespaceisdefinedbythepositionofthenodesandnottheotherwayaround.

Despitesuchdifferences(whichshouldnotbeforgotten),force-directedalgorithmsallowreadingnetworksasgeographicalmaps,translatingcomplicatedmathematicalconceptsintomoreconventionalvocabularyofregionsandmargins,pathandlandmarks,centresandperipheries(Lynch,1960).Thisisacrucialadvantagethatexplainswhyforce-directedalgorithmshavebecomethede-factostandardofnetworkvisualisation:theyfacilitatetheexplorationofnetworksandrelationsbymeansofmorefamiliarandintuitivespatialmetaphors,aswellasthroughlessfamiliarcomputationalandstatisticalmetrics.

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THEDÉCODEX:ACONTROVERSIALCASESTUDYInthefollowingpages,wewillillustratethetechniqueofvisualnetworkexplorationdrawingonaconcreteexample.OurcasestudyisanetworkofwebsitesextractedfromalistingcompiledbytheFrenchjournalLeMonde.Since2009,agroupofjournalistsgatheredunderthenameofLesDécodeurs(www.lemonde.fr/les-decodeurs/article/2014/02/12/l-equipe-des-decodeurs_4365082_4355770.html)hasverifiedtheaccuracyofthousandsofstoriescirculatingintheFrenchblogosphereandinsocialmedia.InJanuary2017(atthebeginningtheFrenchpresidentialcampaign),LesDécodeurshavelaunchedanonlinetoolcalledtheDécodex(www.lemonde.fr/verification),allowingreaderstosearchforthemostimportantsourcesofonlineinformationrelevanttoFrenchpublicdebates(thoughnotnecessaryinFrench).Eachsourceisaccompaniedbyashortdescriptionand,morecrucially,byanevaluationofitstrustworthinessaccordingtothejournalistsofLeMonde.

Figure3.UserinterfaceoftheDécodextoolbyLeMonde

Notsurprisingly,theclassificationprovidedbyLesDécodeurshasstirredmuchdebateintheFrenchmediaspheres.Severalofthesourcescategorizedasimpreciseorunreliable,alongwithothernewspapersandblogs,havecontestedtheDécodex,withcritiquespanningfromchallengingthewayinwhichwebsitesareover-simplisticallyclassified;toquestioningtherightofLeMonde(whichisitselfarivalsourceofinformation)tonotethereliabilityofotherwebsites;todisputingthelegitimacyandinterestofsuchclassificationingeneral(arguingthatsomeofthewebsitesinthelistmeanstocirculateopinionsratherthaninformation).LesDécodeursthemselvesadmittedthedifficultyoftheirexercise,themanyambiguitiesthattheywereobligedtodecideonandtheerrorsandinaccuraciesthatmayhavederivedfromthem.Atthesametime,theydefendedtheirworkbypointingattheincreasingquantityoffalseorpartisaninformationcirculatingonlineandbyaffirmingtheiropennesstodiscussingtheirclassificationandrevisingitifnecessary.

ThecontroversyaroundtheDécodexisagoodexampleofdifficultiesconnectedtothedetectionoffakenewsonline(Bounegruetal.,2017),butalsoofthemoregeneraldebatessurroundingallkindofclassifications.Categorizingthingsisneveraself-evidentorinnocentpractice(Bowker&Star,1999)andshouldalwaysbecarriedoutwiththegreatestcaution.ThisistruefortheinitialclassificationoftheDécodex,butitisalsotrueforthenetworkextractedfromit.Aswewillseeinthefollowingpages,thevisualexplorationofnetworkinvolvesaconstanttoingandfroingofcategorizationandobservation,typologyandtopology.

Tobuildourexamplenetwork,wehaveextracted,incollaborationwithLesDécodeurs,allthewebsites

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containedintheDécodexandinvestigatedthewayinwhichtheyciteeachother.Todoso,weemployedHyphe(http://hyphe.medialab.sciences-po.fr)awebcrawlerdevelopedbythemédialabofSciencesPo,whichfacilitatestheexplorationofwebsitesandfollowingthehyperlinkspresentintheirpages.AllthewebsitescomprisingtheDécodexcorpushavebeencrawledatadepthofoneclickstartingfromthehomepage.Wesoobtainedanetworkwith653nodesand5943edges.WhilstLesDécodeursfocusoneditorialjudgementsabouthowtoclassifywebsitesintheFrenchmedialandscape,ournetworkexplorationexaminestherelationsbetweenthemandotherwebsitesbymeansoftheirlinkingpractices.Whilesomeresearchersfocusonhownetworksareheldtogetherthroughfinancialties,organisationalaffiliations,businessrelationshipsandfamilyandsocialrelations–weconsidertheirrelationsaccordingtothehyperlink,inaccordancewithalongertraditionofdigitalmethods,digitalsociologyandnewmediastudiesresearch(see,e.g.Marres&Rogers,2005;Rogers,2013)

Thetreatmentofsocialplatforms(suchasFacebook,Twitter,YouTube…)inourcrawlrequiressomeadditionalexplanation.Theseplatformsarebothsourcesofinformationasawholeandcontainersofmultipleindividualsourcesintheformofpagesoraccounts.SinceextractingallthehyperlinksfromasiteaslargeasFacebookwouldhavebeenimpossible,weonlycrawledtheaccountsthatwerespecificallymentionedintheDécodex.Wehave,however,keptarecordofallthelinkspointingtowardthemainsocialmediaplatformtoinvestigatehowtheyarecitedbytheotherwebsitesofourcorpus.

AVISUALEXPLORATIONOFTHEDÉCODEXNETWORKThevisualexplorationofnetworksexploitsthreevisualvariablestographicallyrepresenttheirfeatures:position,sizeandhue(foradefinitionofthesevariablesandtheirsemioticaffordances,seeBertin,1967).Forthereasonsdiscussedabove,positioniscrucialintranslatingthemathematicalcharacteristicsofthegraphs.Force-directedlayoutscreateregionswherenumerousnodesaredenselyassembledandregionsthatarelesscrowded.Thesedifferencesofdensity,determinedbytheunevendistributionoflinks,revealtheunevenassociationbetweentheentitiesofthenetwork.Everythingmaybeconnectedinthisworld,butnoteverythingisequallyconnected.

Discerningthespatialstructureofnetworks,however,isnotalwaysstraightforward.Intheeasiestcases,thedifferenceinthedensityofassociationissuchthatclustersappearaswelldefinedknotsofnodesandedgesseparatedbyempty(oralmostempty)zones.Thesezonesarecalled'structuralholes'(Burt,1995)and,whentheyexist,theyprovideacrucialguidancefortheinterpretationofthenetwork.Thankstotherupturescreatedbystructuralholes,theboundariesofclusterscanbeeasilydetected,likecliffsseparatingaplateaufromavalley.Mostofnaturalandsocialnetworks,however,donotexhibitsuchaclearseparationandthebordersoftheirclustertendtobegradualasthehillsideslopes.Thefuzzinessofclusters’frontiersisnotnecessarilyanobstacletotheirrecognition(onecanpointatahillevenwhenitisimpossibletosayexactlywhereitstartsandends),butitcertainlymaketheiridentificationmoredifficult.Thisiswhyvisualnetworkanalysisisoftenmorelikeanexploratoryexpedition-wheremeaningsandfindingsareprogressivelyandhermeneuticallygenerated-thantothestatisticalconfirmationofasetofpre-existinghypotheses(onthedifferencebetweenexploratoryandconfirmatoryanalysisseeTuckey,1997andBehrensandChong-Ho,2003).

ThisiscertainlythecaseforourDécodexnetwork,which,atafirstlook,doesnotpresentanymanifeststructuralholeoranyclearspatialstructure.Tovisualiseournetworkweusedtwomaintools:Gephi(https://gephi.org)forfilteringandspatializingthenetwork(usinginparticulartheforce-drivenalgorithmForceAtlas2)andGraphRecipes(http://tools.medialab.sciences-po.fr/graph-recipes)totweakthevisualrenderingofthenetwork.ThoughnostructuralholesareevidentintheDécodexnetwork,lookingcloselyatthelayoutmakesitispossibletonoticethatthenetworkdoesnotspatializeasaperfectcircle,butratherinanavocado-likeshapewithasmallertopandandalargerbottom.Theseirregularities(asweakandsubtleastheycanbe)oftensuggestthepresenceofpolarisingeffectswhichcanbeinterestingtoinvestigatefurther.

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Figure4.TheDécodexnetworkspatializedbyForceAtlas2.Thesizeofnodesisproportionaltoin-degree.

Thefirstandmostcrucialwaytoexploreournetworkistolookattheidentityofthenodesthatoccupyitsdifferentregions.Thismayseemtrivial,butitisnot.Itisadistinctadvantageofvisualexplorationcomparedtootherformofstatisticalanalysis,thatitdoesnotaggregatetheindividualentitiesthatcomposeitscorpus:eachandeverynodeisvisibleinthelayoutandcanbeinterrogatedbytheresearcher.Evenonasmallnetworkastheoneinourexample,however,thequantityofnodescanmakeitdifficult(andtimeconsuming)tolookatallofthem.

Thisiswherethesecondvariableofourvisualexploration,size,comesinhandy.Since,innetworks,nodesaredefinedfirstandforemostbytheirconnections,wehaverankedthenodesaccordingtothenumberofedgespointingtothem.Inthejargonofnetworkanalysisthisnumberiscalled'in-degree'andnodeswithanelevatedin-degreearecalled'authorities',becausetheyarerecognisedandreferredtobymanyothers.Inthepreviousfigureandinallfollowing,wehavesizedthenodesaccordingtotheirin-degreesothatagreaterauthorityliterallytranslatesintoincreasedvisualprominence.

Readingthenamesofwebsitesthatoccupythetwopolesofouravocado,itseemsnaturaltosupposethattheirseparationderivesfromalinguisticfracture.ThewebsitesinthelowerpartarepredominantlyFrench,whilethoseintheupperpartaremoreinternational.AwaytohighlightthisistoshowtheunevendistributionofTLD(TopLevelDomain)inthenetwork.

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Fig.5.DistributionofTDLintheDécodexnetwork.

Thelinguisticseparationwejusthighlighted,however,isnotparticularlysurprisingorinteresting.Thiskindofdivisionisregularlyobservedinnetworkofwebsitesandhyperlinks.Detectingitisimportant,butratherinanegativeway-itmakesusawarethatinordertogeneratemoreinterestingfindings,wewillhavetolookbeyondit.

Furtherexploringthenetwork,wemaynoticetheroleofnotjustlanguages,butalsosocialnetworkplatforms,suchasYouTube,Facebook,Twitter,InstagramandDailymotion.WiththeremarkableexceptionofWikipedia,allthemainsocialmediaplatformsarelocatedinthemiddlerightofthelayout-somewherein-betweentheEnglishandtheFrenchwebsites(asonewouldexpectgiventhemultilinguality),butalsoseparatedfrombothbytheirdistinctivenature(andpossiblybythedifferentwayinwhichtheyhavebeentreatedinthecrawl).

Moreover,byfocussingonthelowerandlargerpartofthenetwork,wecanrecognisetwodifferentsub-poles,withnationalsources(suchasLeMonde,LeFigaro,FranceInfo,Libération...)occupyingmostofthelowerregionandtheregionalpressclusteringatthebottom-rightofthelayout.

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Fig.6.ZoomontheFrenchregionalpress

Thedistinctivepositionoftheplatformsandthenational/regionalpressarebothinterestingandnontrivialfindings,butwecanpushouranalysisfurther.Thewaytodosoisbyplayingwiththethirdvisualvariableexploitedbyvisualexplorationofnetwork:thehueofthenode.Thisisalaboriousbutrevealingpartofourvisualexploration.Itconsistsincategorizingthenodesofthenetworkaccordingtomultipleclassificationsandvisualizingtheseclassesonthenetworkasdifferentcolorsor(asinthispaper)asdifferentshadesofgrey.Itisimportanttonoticethattheoperationofclassifyingthenodesandofreadingthedispositionofclassesarenotseparated,butperformedatthesametime.Asitwillbecomeclearinthenextpages,ourtechniquedoesnotconsistsimplyintheprojectionofasetofpre-existingcategoriesonaconnectivity-basedlayout,butonrecursivelyusingthecategoriestomakesenseofthelayoutandthelayouttodefinethecategories.Itisimportanttorememberthatthecolorisa‘non-mixable’visualvariable.Anodecanberedorblue,forexample,butnotthetwoatthesametime.Whencategorizingnodes,itisthereforenecessarytoemployexclusivecategories.Awebsite,forexample,canbeclassedinthecategory'news'or'satire',butnotinboth.Inthe(notuncommon)caseofnodesresistingauniqueclassification,researchercanintroducearesidualcategorysuchas'multiple'or'misc'.

Asafirststepinourcombinedexplorationoftopologyandtypology,wewillcolorthenodesofthenetworkaccordingtotheoriginalcategoriesoftheDécodex.ThesecategoriesrefertothetrustworthinessofthesourcesasmanuallyassessedbythejournalistsofLeMondeinthefourcategoriesare'reliable','imprecise','unreliable'and'satirical'.Preciselybecausethesecategorieshavebeendefinedbeforeandindependentlyfromtheextractionofthenetwork,theirdispositiondoesnotfollowthespatialarticulationofthenetwork.Rather,itispossibletofindnodesofeverycategoriesinalmostofregionsofthenetwork.Aremarkableexceptionarethesatiricalwebsitesthataretobefoundontherightsideofthelayoutbothinitsupperandlowerpart.Arguably,thispositionisnotduetothehyperlinksbetweenthesatiricalwebsites(whichdonotciteeachotherverymuch),butbytheirstrongconnectionwithsocialmediaplatformstowhichallthesesitesextensivelylink.

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Fig.7.The'satirical'websitesaccordingtotheoriginalDecodexclassification(nodehavebeenemphasizedbytheblack

colorandbydoublingtheirradiusdespitetheirlowdegree)

Theotherclassesaredistributedmoreevenlybutnotrandomly.The'reliable'websitestendtooccupythecenterofbothintheinternationalandFrenchpole,whilethe'imprecise'and'unreliable'takeamoremarginalposition.Moreinterestingly,lookingatthelowerpartofthenetwork,weobservetwogroupsof'imprecise'and'unreliable'sources-whileamajorityofthesenodesarepositionedabovethecoreofnationalandreliablewebsites(andhencein-betweentheFrenchandtheinternationalwebsite),asignificantminorityislocatedbelowthem.

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Fig.8.Highlightofthe'reliable'websites(left)and'unreliable'and'imprecise'websites(right)

Toaccountforthisseparation,weintroduceanadditionalcategorisationbasedonthepoliticalleaningofthewebsites.Inparticular,wedistinguishthewebsitesthatdisseminateunreliableorimpreciseinformationbecausetheypursuearight-wingorextreme-rightagenda(whichoccupythecenterofthenetwork)andthewebsitesexhibitingamoregeneralconspiritorialattitude(whichoccupythebottomofthenetwork).

Fig.9.Highlightofthe‘conspiritorial’websites(left)and'right'and'extremeright'websites(right)

Throughouriterativeexplorationoftypologyandtopologywehaveeventuallyrevealedapartitioningof

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thenetworkthat,whileinvisibleatfirstglance,allowstointerpretsomeofthemaincontoursoftheFrenchmedialandscape.Thoughtheseterritoriesarenotseparatedbyclearstructuralholes,thenodesthattheycontainarefairlyconsistent.Interestingly,ourfinalclassificationproducesahomogeneouspartitionofthelayoutnotinspite,butbecauseofitsheterogeneity,whichmixeslinguisticcategories,trustworthinessclassesandpoliticalleanings.Thefactthatanon-homogenouscategorizationturnsuptoofferthebestcharacterizationofthestructureofournetworkshouldnotcomeasasurprise.Networksarecomplexobjectswhicharticulatediverseelementsthroughdisparatelogics.Inthis,theyremindusofapassagebyJorgeLuisBorgescitedbyFoucaultasaperfectexampleofaheterogenousclassificationthat,whiledefyingourtraditionalcategories,isnonethelesshighlyefficienttodescribethecultureinwhichithasbeenelaborated:

“[Borges] quotes a ‘certain Chinese encyclopaedia’ in which it is written that ‘animals are divided into: (a) belonging to the Emperor, (b) embalmed, (c) tame, (d) suckling pigs, (e) sirens, (f) fabulous, (g) stray dogs, (h) included in the present classification, (i) frenzied, (j) innumerable, (k) drawn with a very fine camelhair brush, (l) et cetera, (m) having just broken the water pitcher, (n) that from a long way off look like flies’. In the wonderment of this taxonomy, the thing we apprehend in one great leap, the thing that, by means of the fable, is demonstrated as the exotic charm of another system of thought, is the limitation of our own”. (Foucault,1970p.XV).

Fig.10.TheheterogenousterritoriesoftheDécodexnetwork.

LINKINGPATTERNSINTHEDÉCODEXNETWORKNowthat,bymeansofvisualexploration,wehavedefinedaheterogenousbuthermeneuticallyrobustpartitioningofournetwork,wecanuseitasabasisforastatisticalanalysis.Whilepraisingtheadvantagesofthevisualinterpretation,wearealsoawarethatnotallstructuralpropertiescanberenderedvisually.Thedirectionofedgesortheconnectionbetweendifferentclasses,inparticular,arenoteasilyreadinnetworkimages.Thesequestions,however,canbeinvestigatedbyothermeansoncethepartitioningofthenetworkhasbeendefined.

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Fig.11.Distributionofthenumberofnodespercategory

Fig.11showsthedistributionofnodesintheregionsidentifiedinourfinalclassification(seefigure10),towhichwehaveaddedthe‘satirical’websites(whichwediscussedabovebutnotincludedinfigure10forthesakeoflegibility)aswellas“otherreliable”and‘otherunreliable’.Thesetworesidualcategoriescomprisetogetheraboutonefifthofthenodesofthenetwork.Thisrelativelyhighfigureisnotuncommon.Giventheheterogeneityofthenetworkstheyworkwith,socialscientistsandjournalistsshouldaimatclassificationsthatarerobustandinsightful(capableofdelineatinghomogenouszonesinthegraph)ratherthancomprehensive.

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Fig.12.Connectivitybetweenthecategoriesofourfinalclassification.Rowsconveyhowmanytimethenodesofagivencategorycitesthenodesofothercategories.

Columnsconveyhowmanytimethenodeofagivencategoryarecitedbythenodesofothercategories.

Ourempiricalcategoriesarepowerfultoolstounveildifferentlinkingstrategiesinthenetwork.Figure12abovepresentsthelinksinthecorpusaggregatedbycategories.Aswecansee,notallcategoriesciteorarecitedthesameway.‘Frenchnationalmedia’and‘platforms’aremuchcitedandbyvariousactors(theircolumnscontainlargercircles),while‘satirical’websitesarescarcelycited(theircolumnisalmostempty).Platformsdonotcitemuch,butthisismerelyaconsequenceofourmethodsince(asexplainedabove)mostofthemhadnotbeennotcrawled.‘Right-wing’,conspiracytheoristandother‘unreliable’websitesareonthecontrarytheoriginsofthehighestnumberofcitationsand,veryinterestingly,theyseemtofavour“reliable”sourcesover“unreliable”ones.Asexpected,thereliablewebsitesdonotlinkbacktothem,andthisasymmetryrevealsanimportanthierarchy.Toinvestigatethislinkingpattern,wewillcomparetheincomingandoutgoinglinksofsomeofthemostinterestingcategories.

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fig.13.Hierarchicalstructureinthecorpus,basedonourfinalcategories.Blackarrowsontherightsidesummarizethe

linksstructurebetweenthesecategories.

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fig.14.Simplifiedversionofthestatisticalanalysispresentedinfigure13.

Thiskindofhierarchicalstructureiscommononthewebandhasbeenexplainedasaconsequenceofpreferentialattachment(Barabási&Albert,1999):actorstendtolinktootherwebsitesthattheyperceiveashigherinthehierarchyandavoidlinkingtothosethattheyperceiveaslower.Thisstyleofpreferentialattachmentwherebysmalleractorslinktoestablishmentactorswithoutreciprocationofthelinkingacthaselsewherebeencalled“aspirationallinking”(Rogers,2013).Linksinanetworkdonotalwaysproduceahierarchyofcategoriesbutthisbehaviourdoes.Thislinkingpatternandthewayitfitsourempiricalcategories,maysuggestanalternativewaytocharacterisethetrustworthinessbeinginvestigatedbyLeDécodeurs:reliablesourcesarecitedbyalltypesofwebsites,whileunreliablesourcesareonlycitedbyfewothertypes(ifany).

Thisobservationisinmanywaysatoddswithwhatisoftenaffirmedabout“post-truthera”inwhichwehavesupposedlylanded.Whilefakenewsissaidtoleveragethehorizontalityofdigitalmediatoblurtheboundariesbetweentrueandfalse,thelinkingpatternsofthe(French)informationspheressuggestadifferentpicture.Despitetheirdifferentideologicalleanings,allwebsitesagreeontheoverallhierarchyofreliabilitybycitinginonesenseandnotintheother.The‘right-wing’websites,forexample,trytoblurthelinesbycitingboththeirpeersandmorereliablesources,buttheyalsotrytodrawalinebetweenthemandtheevenlessreliable‘conspiracytheorist’websites.Whateveritspositioninthepyramidofhyperlinking,everyactortriestoimproveitssituationbylinkingupwardstoauthoritiesabove,andnotlinkingtolessreputablewebsitesbelow,thusreinforcingthehierarchy.

CONCLUSIONThischapterdiscussedthevisualexplorationofnetworkswiththeaimofimprovingtheunderstandingofoneofthedominantvisual-analyticalformsofourdigitalage–thenetworkdiagram–anditspotentialroleinrelationtothestudyandpracticeofdigitaljournalism.Drawingongraphsemioticsandtraditional

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cartography,thischapterproposedamodelwherebytheinterpretationofnetworktopologywithitsregions,paths,coresandperipheries,isguidedbythreevisualvariables:position,sizeandhue.Theprocessthatwedescribedisonethatemphasizestheexploratoryanditerativecharacteroftheinvestigation.Whilecounter-intuitiveatfirst,weemphasisedthatinordertosurfacethemultiplelogicsthatplayoutinthestructureofanetworkgraph,analysisshouldnotlimititselftooneclassificatoryprinciple.Multipleheterogeneouscriteriaofclassificationareoftennecessarytocharacterizethetopologyofanetworkmap.Finally,weadvocatedformixingmethods,complementingvisualnetworkexplorationwithstatisticalanalysesinordertofurthercharacterisenetworkproperties.ThroughthecasestudyofFrenchmediahyperlinkmap,wetriedtoshownhowthevisualexplorationofnetworksrevealsnewangleswhichotheranalysesmayleaveunexplored.Inthiscasethechapterillustratedanalternativewaytoassesswebsites’reliabilitythatcomplementsthetraditionalfact-checkingapproachofqualifyingcontentwithanexaminationofthelinkingpatternsbetweendifferentregionsofthenetworkasreputationalmarkers(Rogers,2013).InthisanalysisthuswehavecombinedthemanualclassificationofreliabilityundertakenbyLeMonde’sjournalistswiththestandingofasourceaccordingtothehyperlinksthatitreceivesandgives.Thisapproachenabledustobringfreshfindingstocurrentdebatesaroundfakenews.Inspiteoftheproliferationoffabricatedcontentofvariousshades,reputationhierarchiesonthewebseemtobemaintained(atleasttosomeextent),asfakeandhyper-partisansitesdeployaspirationalhyperlinkingstyleswhichfavour,perhapssurprisingly,authoritativesources.

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