Information Diffusion in Online Social Networkssubjectdirs.pdf · Information Diffusion in Online Social Networks Keywords: Social networks (online), diffusion, virality, communities,

InformationDiffusioninOnlineSocialNetworks

Keywords:Socialnetworks(online),diffusion,virality,communities,topology,polarization,fake-news,patterns,Twitter.Context:

Understanding thediffusionmechanismsof speeches, opinions, fakenews, and rumors inonline socialnetworks isa crucial societal issue.Viralityandbotsdetectionhavebeenextensively studied formajorpoliticalevents suchas theAmericanpresidentialelections in2016 [Kollanyi2016], theBrexit [Howard2016]ortheFrenchpresidentialelectionin2017[Ferrara2017].

Understandingthesemechanismsraisesseveralquestions:-Whatistheimpactofthenetworktopologyinthediffusionofaviralmessage(anditsimportancewithregardtothecontentofthemessages)?-Whattypesofrelationshipsinthenetworkaremostlikelytoincreasediffusion?-Howdocommunities,andespeciallypolarizedcommunities,areengagedinthediffusionprocess?-Howdoinfluentialoropinionleadersimpactthediffusion?-Whatistheroleofbots,theirbehavior?- Which typical patterns, structures or processes take part in or govern, control, affect large-scalediffusion?Subject:Toanswer thesequestions, severalalgorithmshavebeendevelopedandvalidatedonspecificdatasets.For examples to detect communities [Drif 2014, Orman 2012], influential users [Riquelme 2016,Ibnoulouafi2018],bots[Ferrara2016],events[Atefeh2015],viralmessages,rumours[Sela2017,Zubiaga2018,Hoang2011],etc.

Inthisworkweplantoaddresstwomainsissues:

-The firstone is related todatamodeling.Weneedtostudyanddevelopdatamodels [Leclercq2018]basedonamulti-layernetworkapproach[DeDomenico2013,Kivela2014].Indeed,itallowsrepresentingthe various types of users and relationships and to give them an appropriate semantic. For example,Twitter,throughtherichnessofrelationshipsproducedbythevarioustypesofoperators(follow,retweet,mention, etc.) generates monolayer networks with a hidden semantic. It is therefore a good field ofexperimentation in order to investigate the efficiency of the multilayer models. Model needs to bevalidatedonvariousreal-worldbigdatasets(from50Goto5To).

- The second issue concerns the design of the multilayer network analyses in order to answer thequestionsraisedbysocialscienceresearchers.Resultsfromdifferentinter-relatedalgorithmsneedtobecombinedinordertoprovideend-users(socialscientist)withthemostcompleteviewofaphenomenon.For example, the studyof influence cannotbedissociated from thenotionof community [Weng2013,Kumar2018,Gupta2016,Gupta2015].Indeed,viralmessagescanspreaddifferentlywithinacommunity,thanoutsideofitTheycanimpactandmodifytheglobalcommunitystructure.Thelinkbetweenthetwoissues is thecomputabilityof suchalgorithmson realdata,algebraicdata structure suchas tensorandgraphembeddingtechniques[Hongyun2017,Goyal2017]arepromisingresearches.

Fromanexperimentalpointofview,youwillhavetostudyandimplementmodelextractionalgorithmsusingdatasetsalreadycollectedbytheteamthathavebeenusedto:

1°)measure the audience/impact of a topic or event [Atefeh 2015]. One point to be addressed is theviralityofthediscourseandtheimpactofbotsinthepropagationofthediscourse[Ferrara2016];2°) show the existence of communities in which the discourse circulates. These communitiesmust becharacterized[Basaille2018,Jebabli2014,Jebabli2015]tohighlighttheirspecificity.Butcommunitiesareintertwined,intersectandtheirmutualinfluenceonthecirculationofdiscoursemustalsobestudied;3°)studytheinfluenceofonecommunitytowardsanotherandthepeoplewhomakethelinks(elasticityof borders) and consequently review the notion of influencers [Azaza 2016, Jebabli 2015a], of opinionleadersaccordingtothecirculationofinformation.

Basedon the experimental results,weplan todevelop appropriatediffusionmodels and test themonnewdatasetsusingtheirpredictiveaspecttovalidatetheirexplanatoryaspect[Jebabli2018].

This thesis focusesonthe fundamentalaspectsofdatamodelandanalysis tools forcomplexnetworks.Fromatheoreticalpointofview,oneoftheissuesaddressedinthisworkdealswiththeadaptationandcombination of traditional monolayer algorithms to multi-layer networks and the extension of graphembeddingtechniques,theproofofthepropertiesoftheproposedalgorithmsshouldbeaddressedsuchasconvergence,qualitymeasurementsinrelationtogroundtruth,etc.

From an experimental point of view, the real-data sets definition and interpretation is based well-establishedinstitutionalcollaborations(since2013)withtwolaboratoriesoftheUniversityofBurgundyinHuman and Social Sciences (TIL and CIMEOS) throughmultiple interdisciplinary projects TEE 2014, TEP2017,PEPSCNRSMOMIS,iSiteCOCKTAIL.

QualificationsThe candidate should be able to complete high quality and innovative research. He/She will developresearchwiththegoalofboth:(i)addressingfundamentaltheoreticalproblems,and(ii)designingmodelsandalgorithmsthatcanbeusedtounderstanddiffusionprocessesinmultilayernetworks.

Theidealcandidatehasgoodknowledgeinappliedmathematics, linearalgebra,statisticsandcomputersciences (algorithm). He/she holds aMaster’s degree (or equivalent) in the field of computer science,appliedmathematicsElectricalEngineering,physicsorarelateddisciplineobtainedwithverygoodfinalgrade(withanaveragegradeofBorbetter).

• She/he has experience (or interest) in modeling, quantitative empirical data analysis, andpreferablyworkingwithlarge-scalenetworks.

• Goodcommandofprogramminglanguagesanddatabasesaccessandstorage.• Experience(orinterest)indevelopingefficienttechniquesforlarge-scaledataanalysis.• VerygoodcommandofEnglish(oralandwritten)andexcellentcommunicationskills.• Curiosity,self-reliance,integrity,andcreativity.

DataScienceTeamofthe“Laboratoired’informatiquedeBourgogne”

ThesisDirector:HocineCherifiCo-supervisor:EricLeclercqandMarinetteSavonnetThe Data science team has a strong emphasis on quantitative yet applied research. It has strongcompetenciesinDataManagement,SocialNetworks,DataMining,andNetworkscience.Itoffers:

• TheopportunitytocompleteaPhDintheareaofNetworkScienceandBigDatausing complexsystemstoolsandthemodelingofsocio-economicinteractions.

• Abroad-range,independentworkaspartofadynamicteaminapositiveworkingatmosphere.• Athoroughcareerdevelopmentprogram(participationinsummerschools,conferences,etc.).

Salary

Thedoctoralcontract isthemainformofsupportthatcanbeawardedtoPhDstudents. It isofferedtodoctoralstudents,foraperiodofthreeyears. Itprovidesallthesocialguaranteesofaworkcontract inaccordancewithFrenchpubliclaw.Thedoctoralcontractsetsaminimumremuneration,indexedontheevolution of the remuneration of the public service: It amounts to 1758 euros gross monthly for aresearchactivityalone.Itcanbehigherwithteachingactivities.

Application

Tobeconsidered,applicationsmustbesentbyemail,enclosingthefollowing:• ACurriculumVitae• Transcriptsforhigher-educationstudiesuntilmostrecentavailable• Acoverletterstatingthepurposeoftheapplicationandabriefstatementofwhyyoubelievethat

yourbackgroundandgoalsarewell-matchedwiththegoalsofthisposition• Contactinformationforthreereferencepersons.

Listofqualificationsandotherdocumentsthattheapplicantwishestorefertoshouldbeenclosedwiththeapplication.ApplicationDeadlineJune04,2019Addressyourcorrespondencewithsubject“ApplicationThesisDIRS”toallthefollowingcontacts:Contacts:HocineCherifi-Hocine.Cherifi@u-bourgogne.frMarinetteSavonnet–Marinette.Savonnet@u-bourgogne.frEricLeclercq–Eric.Leclercq@u-bourgogne.frLaboratoired’InformatiquedeBourgogne(LIB)–EA7534ÉquipeSciencedeDonnéesUniversitédeBourgogne9,AvenueAlainSavary21078Dijon–FranceBibliography(inboldpublicationsofthesupervisingteammembers):[Atefeh2015]Atefeh,Farzindar,andWaelKhreich."Asurveyoftechniquesforeventdetectionintwitter."ComputationalIntelligence,31.1(2015):132-164.[Azaza2016]Azaza,Lobna,Kirgizov,Sergey,Savonnet,Marinette,etal.Informationfusion-basedapproachforstudyinginfluenceontwitterusingbelieftheory.ComputationalSocialNetworks,2016,vol.3,no1,p.5-25.[Basaille2018]Basaille Ian,Plateformepour la gestiondesdonnées issuesdes réseaux sociauxdans lecadredelagestiondelarelationclient,Thèsededoctorat,UniversitédeBourgogne,2018.[Jebabli 2018] Jebabli M., Cherifi H., Cherifi C., Hammouda A., “Community detection algorithmevaluation with ground-truth data, Physica A: Statistical Mechanics and its Applications 492, 651-706Elsevier2018[Jebabli2015]JebabliM.,CherifiH.,CherifiC.,HammoudaA.,"Overlappingcommunitydetectionversusground-truth in AMAZON co-purchasing network" in 11th International Conference on Signal Image

Technology&Internet-BasedSystems,ProceedingsofIEEE2015[Jebabli2015a]JebabliM.,CherifiH.,CherifiC.,HammoudaA.,“UserandgroupnetworksonYouTube:Acomparativeanalysis, in12th InternationalConferenceonComputerSystemsandApplications (AICCSA),ProceedingsofIEEE2015[Jebabli2014]JebabliM.,CherifiH.,HammoudaA.,"OverlappingCommunityStructure inCo-authorshipNetworks:ACaseStudy," in7th InternationalConferenceonu-ande-Service, ScienceandTechnology(UNESST),ProceedingsofIEEEpp.26,29,2014[Leclercq2018]Leclercq,Eric,Savonnet,Marinette,"Modèletensorielpourl’entreposageetl’analysedesréseauxsociaux–Applicationàl’étudedelaviralitésurTwitter",INFORSID2018.[DeDomenico 2013] De Domenico, Manlio, et al. "Mathematical formulation of multilayer networks."PhysicalReviewX3.4(2013):041022.[Drif2014]Drif,Ahlem,andAbdallahBoukerram."Taxonomyandsurveyofcommunitydiscoverymethodsincomplexnetworks."InternationalJournalofComputerScienceandEngineeringSurvey5.4(2014):1.[Ferrara2016]Ferrara,Emilio,etal."Theriseofsocialbots."CommunicationsoftheACM59.7(2016):96-104.[Ferrara2017]Ferrara,Emilio."Disinformationandsocialbotoperationsintherunuptothe2017Frenchpresidentialelection."(2017).[Goyal2017]PGoyal,EFerrara,"Graphembeddingtechniques,applications,andperformance:Asurvey",arXivpreprintarXiv:1705.02801,(2017).[Gupta 2016] Gupta N., Singh A., Cherifi H. , “Centrality measures for networks with communitystructure”,PhysicaA:StatisticalMechanicsanditsApplications452,46-59,Elsevier2016[Gupta 2015] Gupta N., Singh A., Cherifi H., “Community-based Immunization Strategies for EpidemicControl” in International Conference on Communication Systems and Networks, Proceedings of IEEE,2015[Hoang2011]Hoang,Tuan-Anh,etal."Onmodelingviralityoftwittercontent."InternationalConferenceonAsianDigitalLibraries.Springer,Berlin,Heidelberg,2011.[Hongyun2017]Cai,Hongyun,VincentW.Zheng,andKevinChen-ChuanChang."AComprehensiveSurveyofGraphEmbedding:Problems,TechniquesandApplications."arXivpreprintarXiv:1709.07604(2017).[Howard 2016] Howard, Philip N., and Bence Kollanyi. "Bots,# strongerin, and# brexit: Computationalpropagandaduringtheuk-eureferendum."BrowserDownloadThisPaper(2016).[Ibnoulouafi2018] IbnoulouafiA.ElHassouniM.,Cherifi,“M-Centrality: Identifyingkeynodesbasedonglobal position and local degree variation” In revision for Journal of StatisticalMechanics: Theory andExperiment[Kivela2014]Kivelä,Mikko,etal."Multilayernetworks."Journalofcomplexnetworks2.3(2014):203-271.[Kollanyi 2016] Kollanyi, Bence, Philip N. Howard, and Samuel C. Woolley. "Bots and automation overTwitterduringthefirstUSPresidentialdebate."Compropdatamemo1(2016):1-4.[Kumar2018]KumarM.SinghA.,CherifiH.,“AnefficientImmunizationStrategyusingOverlappingNodesand its neighborhoods” to appear in the proceedings of the Web Conference 2018 (WWW18), Lyon,France[Orman 2012]OrmanG.K, Labatut V., and Cherifi H., “Comparative evaluation of community detectionalgorithms: a topological approach”, Journal of Statistical Mechanics: Theory and Experiment, P08001,august2012.[Riquelme2016]Riquelme,Fabián,andPabloGonzález-Cantergiani."MeasuringuserinfluenceonTwitter:Asurvey."InformationProcessing&Management52.5(2016):949-975.[Sela 2017] Sela, Alon, et al. "Increasing the Flowof Rumors in SocialNetworks by SpreadingGroups."arXivpreprintarXiv:1704.02095(2017).[Varol2018]Varol,Onur.AnalyzingSocialBigDatatoStudyOnlineDiscourseandItsManipulation.Diss.IndianaUniversity,2017.[Weng 2013] Weng, Lilian, Filippo Menczer, and Yong-Yeol Ahn. "Virality prediction and communitystructureinsocialnetworks."Scientificreports3(2013):2522.[Zubiaga2018]Zubiaga,Arkaitz,etal."DetectionandResolutionofRumours inSocialMedia:ASurvey."ACMComputingSurveys(CSUR)51.2(2018):32.