Prediction and Inference from Social Networks and Social Media PDF
Preview Prediction and Inference from Social Networks and Social Media
Lecture Notes in Social Networks Jalal Kawash Nitin Agarwal Tansel Özyer Editors Prediction and Inference from Social Networks and Social Media Lecture Notes in Social Networks SeriesEditors RedaAlhajj,UniversityofCalgary,Calgary,AB,Canada UweGlässer,SimonFraserUniversity,Burnaby,BC,Canada AdvisoryBoard CharuC.Aggarwal,IBMT.J.WatsonResearchCenter,Hawthorne,NY,USA PatriciaL.Brantingham,SimonFraserUniversity,Burnaby,BC,Canada ThiloGross,UniversityofBristol,Bristol,UK JiaweiHan,UniversityofIllinoisatUrbana-Champaign,IL,USA HuanLiu,ArizonaStateUniversity,Tempe,AZ,USA RaulManasevich,UniversityofChile,Santiago,Chile AnthonyJ.Masys,CentreforSecurityScience,Ottawa,ON,Canada CarloMorselli,UniversityofMontreal,QC,Canada RafaelWittek,UniversityofGroningen,TheNetherlands DanielZeng,TheUniversityofArizona,Tucson,AZ,USA Moreinformationaboutthisseriesathttp://www.springer.com/series/8768 Jalal Kawash (cid:129) Nitin Agarwal (cid:129) Tansel Özyer Editors Prediction and Inference from Social Networks and Social Media 123 Editors JalalKawash NitinAgarwal DepartmentofComputerScience InformationScienceDepartment UniversityofCalgary UniversityofArkansasatLittleRock Calgary,AB,Canada LittleRock,AR,USA TanselÖzyer DepartmentofComputerEngineering TOBBUniversity Ankara,Turkey ISSN2190-5428 ISSN2190-5436 (electronic) LectureNotesinSocialNetworks ISBN978-3-319-51048-4 ISBN978-3-319-51049-1 (eBook) DOI10.1007/978-3-319-51049-1 LibraryofCongressControlNumber:2017932668 ©SpringerInternationalPublishingAG2017 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade. Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Social networks (SN) have brought an unprecedented revolution in how people interactandsocialize. SN areused notonlyasa lifestyle butalso in variousother domains,includingmedicine,business, education,politics, and activism. SN have aswellgrowninsizestoincludebillionsofusers.Asofthiswriting,Twitterclaims tohave313millionmonthlyactiveusers.Facebookgrewbytheendof2016to1.71 billionuserswith1.13billiondailyactiveusers.Facebooknowhasa“population” thatsurpassedthepopulationofIndia!Onlinesocialmedia(OSM),mediaproduced bySNusers,haveofferedarealandviablealternativetoconventionalmainstream media.OSMarelikelytoprovide“raw”,uneditedinformation,andthedetailscanbe overwhelmingwiththe potentialofmisinformationanddisinformation.Yet, OSM areleadingtothedemocratizationofknowledgeandinformation.OSMareallowing almost any citizen to become a journalist reporting on specific events of interest. This is resulting in unimaginable amounts of information being shared among a huge number of OSM participants. For example, Facebook users are generating severalbillion “likes” andseveralhundredmillion postedpicturesin a single day. Twitterusersareproducing6000tweetspersecond.ThisimmenseamountofOSM posesincreasingchallengestomine,analyse,utilize,andexploitsuchcontent.One grandchallengeinOSMisminingitscontenttomakeusefulinferencesorpredict future behaviourof SN users. This book includesnine contributionsthat examine new approaches that relate to predication and inference in OSM content. What followsisaquicksummaryofthesechaptersincludedinthisbook. Mood prediction in SN is of great utility, especially in the medical field. For instance, predicting a patient’s mood can be crucial to identify signs for depression.Inthisbook,Roshanaeietal.’sapproachistodesignaccurateperson- alized classifiers that can predict a person’s emotions based on features extracted from OSM postings. By developing techniques to mine features such as social engagement, gender, language and linguistic styles, and various psychological featuresin a patient’stweets, theyare able to inferthe patient’smodeaspositive, negative,orneutral.Inadifferentchapter,Kayainvestigatesthepredictionoffuture v vi Preface symptoms of patients from current patients’ records. Kaya’s approach consists of the constructionof a weightedsymptomnetwork and,then, throughunsupervised linkprediction,buildingtheevolvingstructureofsymptomnetworkwithrespectto patients’ ages. Medical SN are also the subject of a chapter by Ayadi et al. Their objective is the automatic inference of indexing medical images. Their approach automatically extracts and analyses information from specialist’s analysis and recommendations.Theapproachismultilingual,applyingtodifferentlanguages. Shahriarietal.studysignedSNinanotherchapter.Theirfocusisthesignificance ofoverlappingmembersinsignednetworks,andtheintensionistodiscoverover- lapping communities in these networks. Different features are used to investigate thesignificanceofoverlappingmembers. Alhajjlooksatlinkpredictionasaclassofrecommendationsystems,predicting recommendations(links)betweenusersanditems.Efficientlyfindinghiddenlinks and extracting missing information in a network aid in identifying a set of new interactions. In this chapter, Alhajj approaches the problem by exploiting the benefits of social network analysis tools and algorithms. For better scalability and efficiency, Alhajj utilizes a graph database model, as opposed to a traditional relationaldatabase. ThepredictionofthequalityofWikipediaarticlesisstudiedinachapterbyDe La Robertieetal. Wikipediaisnotimmuneto problemsrelatingto article quality, such as reputability of third-party sources and vandalism. The huge number of articlesandtheintensiveeditratemakeitinfeasibletomanuallyevaluatethecontent quality.DeLaRobertieetal.proposeaqualitymodelthatintegratesbothtemporal and structural features captured from the implicit peer review process enabled by Wikipedia. Two mutually reinforcedfactors are taken into account:article quality andauthorauthority. Charitonidisetal.realizethatmicroblogs,suchasTwitter,canbeusedforagood orabadcause.Inthischapter,theirconcernisthepredictionofcollectivebehaviour beforeithappens.Theirapproachistoanalysesocialmediacontenttodetectwhat theycall“weak”signals;theseareindicatorsthatinitiallyappearisolatedbutcanbe earlyindicatorsoflarge-scale,real-worldphenomena.The 2011Londonriotsand tweetspertainingtothemaretheirtest-bedfortheirstudy. Discussion forums of Massive Open Online Courses (MOOCs) are the sub- ject of a chapter by Hecking et al. Their objective is to infer the structure of knowledge exchange in MOOC forums. The first step is the extraction of dynamic communication networks from forum data. Next is characterizing users accordingtoinformation-seekingandinformation-givingbehavioursandanalysing the development of individual actors. Finally comes the reduction of a dynamic networktoreflectknowledgeexchangebetweenclustersofactorsanditsevaluation. Bouanan argues that the definition of a unique social network is too restrictive since in reality peopleare interlinkedbyseveralrelationships,rather thanby only one relationship. Hence, multidimensionalnetworks are a better representationof relations among humans. They also define distinctive rules for the simulation of Preface vii message diffusion. Hence, Bouanan’s model includes agents interacting through multiple channels or with different relationships, and information disseminates differently on different link categories. The modelling and simulation of multidi- mensionalnetworksarethesubjectofthischapter. Calgary,AB,Canada JalalKawash LittleRock,AR,USA NitinAgarwal Ankara,Turkley TanselÖzyer Contents 1 Having Fun?: Personalized Activity-Based Mood PredictioninSocialMedia.................................................. 1 MahnazRoshanaei,RichardHan,andShivakantMishra 2 Automatic Medical Image Multilingual Indexation ThroughaMedicalSocialNetwork........................................ 19 MouhamedGaith Ayadi, Riadh Bouslimi, Jalel Akaichi, andHanaHedhli 3 The Significant Effect of Overlapping Community StructuresinSignedSocialNetworks ..................................... 51 MohsenShahriari,YingLi,andRalfKlamma 4 ExtractingRelationsBetweenSymptomsbyAge-Frame BasedLinkPrediction....................................................... 85 BuketKayaandMustafaPoyraz 5 LinkPredictionbyNetworkAnalysis ..................................... 97 SalimAfra,AlperAksaç,TanselÕzyer,andRedaAlhajj 6 Structure-Based Featuresfor Predicting the Quality ofArticlesinWikipedia..................................................... 115 BaptistedeLaRobertie,YoannPitarch,andOlivierTeste 7 PredictingCollectiveActionfromMicro-BlogData..................... 141 ChristosCharitonidis,AwaisRashid,andPaulJ.Taylor 8 DiscoveryofStructuralandTemporalPatternsinMOOC DiscussionForums........................................................... 171 TobiasHecking,AndreasHarrer,andH.UlrichHoppe 9 Diffusion Process in a Multi-Dimension Networks: Generating,Modelling,andSimulation................................... 199 Youssef Bouanan, Mathilde Forestier, Judicael Ribault, GregoryZacharewicz,andBrunoVallespir ix Chapter 1 Having Fun?: Personalized Activity-Based Mood Prediction in Social Media MahnazRoshanaei,RichardHan,andShivakantMishra 1 Introduction Positivity and negativity attributes of a person’smood and emotions are reflected in his or her interactions in his/her daily life. The question here is how much a person’s mood and emotions are effected by their personal activates? How much theseactivitiesarepersonalized?Forexample,ifworkingeffectsonperson’smood ashappyorsad. On the otherhand,usage ofsocialnetworkshasexplodedoverthe pastdecade or so. Users now routinely share their thought, opinions, feelings as well as their dailyactivitiesonvarioussocialnetworks.Infact,thereisevidencethatevenpeople leadingotherwiseasecludedlifedoindulgeinonlinesocialactivities.Aninteresting consequenceofthisexplosiveusageofsocialnetworksisthatitispossibletoglean the current mood and emotion of a user from his or her social network postings. A question that arises in this context is: Can we use any differentiating features exhibitedbypeopleontheir onlinesocialactivitiestobuild appropriateclassifiers that can identify the positivity or negativity of users with high accuracy and low falsepositiveandnegativerates? Negative emotions can have disastrous consequencesleading to severe depres- sions,familyneglect,violentbehavior,criminalactivities,andevensuicides. Asa result,itisimportanttoidentifypeoplesufferingfromnegativeemotionsinatimely manner,sothatimportanthelpandsupportcanbeprovidedtothem. Recent work discovered visual, locative, temporal, and social context as four typesofcuestotriggermemoriesofeventsandassociatedemotions[1].In[2],we provideda detailedanalysisof positivity andnegativityattributesof user postings onTwitter.OurstudyisbasedonananalysisofaTwitterdatasetpublishedbythe M.Roshanaei((cid:2))(cid:129)R.Han(cid:129)S.Mishra DepartmentofComputerScience,UniversityofColorado,Boulder,CO80309-0430,USA e-mail:[email protected];[email protected];[email protected] ©SpringerInternationalPublishingAG2017 1 J.Kawashetal.(eds.),PredictionandInferencefromSocialNetworksandSocial Media,LectureNotesinSocialNetworks,DOI10.1007/978-3-319-51049-1_1 2 M.Roshanaeietal. UniversityofIllinois[3].Animportantfindingofthisworkwasthatsocialmedia containsusefulbehavioralcuesto classify users into positive and negativegroups basedonnetworkdensityanddegreeofsocialactivityeitherininformationsharing oremotionalinteractionandsocialawareness. Thesefindingsinspiredustoimplementa personalizedactivity-basedclassifier which can assist individuals in predicting their mood and emotions from their social network postings over time. Personalized classification is important, since one person’s fun hobby may be detested by another person drudgery, e.g., rock- climbing or dancing or shopping, etc. We also considered the effect of temporal natureofuserspositingasdailyorweeklypatternonperson’smoodandemotion. Such a tool can improve people’s emotional memory and their awareness of how dailyactivitiesandtimemayaffecttheiremotion. In thefirststep, wehavedevelopeda generalclassifier byusingseveraltypical featuressuchasn-grams,emoticons,andabbreviationandwordlengths.Then,we include the social network behavioral attributes of users such as the number of tweets,retweets,followers,andfriendsinthecontextofpositiveandnegativeusers. Wealsoanalyzegenderandpsychologicalfeaturesagaininthecontextofpositive, negative, and neutral tweets. All of these features are used to build our general classifierandimprovetheoverallaccuracy.Weshowinthispaperthattrainingand testing a generalizedclassifier based on aggregatingdata from many users results inlimitedperformancewhenusingactivityasanextrainputfeature.Inthesecond step, we discuss the relationship between positive and negative attributes of users and their activities at the time of tweeting. We observethat each user has specific activitiesincorrelationwithhis/hermood.Wealsofindthatthetemporalnatureof tweetingforeachindividualuserisdifferent.We usetheseobservationstobuilda personalizedclassifierthatidentifiesusers’emotionalstatesbasedonthehistoryof theiractivitiesinadditiontopostingtime. Thenovelty/contributionsofthispaperare: (cid:129) Thispaperisthefirsttouseactivityasaninputfeaturetopredictmood, (cid:129) Thispaperisthefirsttoshowthatitisimportanttodesignpersonalizedclassifiers ratherthangeneralizedclassifierstoachieveindividualizedmoodprediction,and (cid:129) Thispaperisthefirsttodemonstratethattemporalinformationisalsoeffective inpredictingmoodinpersonalizedclassifiers. The restofthe paperis organizedas follows.InSect. 2, we discusspriorwork relatedtotheintersectionofonlinesocialmediaandmood.InSect.3,wedescribe theTwitterdatasetusedinouranalysisaswellashowwehavecleanedandlabeled thisdataset.Sect.4describesthefeaturesthatareusedtodesignourclassifier.The methodologydeployedinimplementingtheclassifierandtherelatedresultsforboth generalandpersonalizedare presentedin Sect. 5. Finally,in Sect. 6, we conclude thepaper.
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