Detecting Fake News on Social Media PDF
Preview Detecting Fake News on Social Media
Series ISSN: 2151-0067 S H U • L I U Series Editors: Jiawei Han, University of Illinois at Urbana-Champaign Lise Getoor, University of California Santa Cruz Wei Wang, University of North Carolina, Chapel Hill Johannes Gerke, Cornell University Robert Grossman, University of Chicago D Detecting Fake News on Social Media E T E C Kai Shu, Arizona State University T I N Huan Liu, Arizona State University G F In the past decade, social media has become increasingly popular for news consumption due to its A K easy access, fast dissemination, and low cost. However, social media also enables the wide propagation E N of “fake news,” i.e., news with intentionally false information. Fake news on social media can have E significant negative societal effects. Therefore, fake news detection on social media has recently W S become an emerging research area that is attracting tremendous attention. This book, from a data O mining perspective, introduces the basic concepts and characteristics of fake news across disciplines, N S reviews representative fake news detection methods in a principled way, and illustrates challenging O issues of fake news detection on social media. In particular, we discussed the value of news content C I A and social context, and important extensions to handle early detection, weakly-supervised detection, L and explainable detection. The concepts, algorithms, and methods described in this lecture can help M E harness the power of social media to build effective and intelligent fake news detection systems. This D I book is an accessible introduction to the study of detecting fake news on social media. It is an essential A reading for students, researchers, and practitioners to understand, manage, and excel in this area. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, datasets, tools used in this book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information: http://dmml.asu.edu/dfn/ About SYNTHESIS M This volume is a printed version of a work that appears in the Synthesis Digital Library of Engineering O R and Computer Science. Synthesis books provide concise, original presentations of important research G A and development topics, published quickly, in digital and print formats. N & C L A Y store.morganclaypool.com P O O L Detecting Fake News on Social Media Synthesis Lectures on Data Mining and Knowledge Discovery Editors JiaweiHan,UniversityofIllinoisatUrbana-Champaign JohannesGehrke,CornellUniversity LiseGetoor,UniversityofCalifornia,SantaCruz RobertGrossman,UniversityofChicago WeiWang,UniversityofNorthCarolina,ChapelHill SynthesisLecturesonDataMiningandKnowledgeDiscoveryiseditedbyJiaweiHan,Lise Getoor,WeiWang,JohannesGehrke,andRobertGrossman.Theseriespublishes50-to150-page publicationsontopicspertainingtodatamining,webmining,textmining,andknowledge discovery,includingtutorialsandcasestudies.Potentialtopicsinclude:dataminingalgorithms, innovativedataminingapplications,dataminingsystems,miningtext,webandsemi-structured data,highperformanceandparallel/distributeddatamining,dataminingstandards,datamining andknowledgediscoveryframeworkandprocess,dataminingfoundations,miningdatastreams andsensordata,miningmulti-mediadata,miningsocialnetworksandgraphdata,miningspatial andtemporaldata,pre-processingandpost-processingindatamining,robustandscalable statisticalmethods,security,privacy,andadversarialdatamining,visualdatamining,visual analytics,anddatavisualization. DetectingFakeNewsonSocialMedia KaiShuandHuanLiu 2019 MultidimensionalMiningofMassiveTextData ChaoZhangandJiaweiHan 2019 ExploitingthePowerofGroupDifferences:UsingPatternstoSolveDataAnalysis Problems GuozhuDong 2019 MiningStructuresofFactualKnowledgefromText XiangRenandJiaweiHan 2018 iv IndividualandCollectiveGraphMining:Principles,Algorithms,andApplications DanaiKoutraandChristosFaloutsos 2017 PhraseMiningfromMassiveTextandItsApplications JialuLiu,JingboShang,andJiaweiHan 2017 ExploratoryCausalAnalysiswithTimeSeriesData JamesM.McCracken 2016 MiningHumanMobilityinLocation-BasedSocialNetworks HuijiGaoandHuanLiu 2015 MiningLatentEntityStructures ChiWangandJiaweiHan 2015 ProbabilisticApproachestoRecommendations NicolaBarbieri,GiuseppeManco,andEttoreRitacco 2014 OutlierDetectionforTemporalData ManishGupta,JingGao,CharuAggarwal,andJiaweiHan 2014 ProvenanceDatainSocialMedia GeoffreyBarbier,ZhuoFeng,PritamGundecha,andHuanLiu 2013 GraphMining:Laws,Tools,andCaseStudies D.ChakrabartiandC.Faloutsos 2012 MiningHeterogeneousInformationNetworks:PrinciplesandMethodologies YizhouSunandJiaweiHan 2012 PrivacyinSocialNetworks ElenaZheleva,EvimariaTerzi,andLiseGetoor 2012 CommunityDetectionandMininginSocialMedia LeiTangandHuanLiu 2010 v EnsembleMethodsinDataMining:ImprovingAccuracyThroughCombining Predictions GiovanniSeniandJohnF.Elder 2010 ModelingandDataMininginBlogosphere NitinAgarwalandHuanLiu 2009 Copyright©2019byMorgan&Claypool Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin anyformorbyanymeans—electronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotations inprintedreviews,withoutthepriorpermissionofthepublisher. DetectingFakeNewsonSocialMedia KaiShuandHuanLiu www.morganclaypool.com ISBN:9781681735825 paperback ISBN:9781681735832 ebook ISBN:9781681735849 hardcover DOI10.2200/S00926ED1V01Y201906DMK018 APublicationintheMorgan&ClaypoolPublishersseries SYNTHESISLECTURESONDATAMININGANDKNOWLEDGEDISCOVERY Lecture#18 SeriesEditors:JiaweiHan,UniversityofIllinoisatUrbana-Champaign JohannesGehrke,CornellUniversity LiseGetoor,UniversityofCalifornia,SantaCruz RobertGrossman,UniversityofChicago WeiWang,UniversityofNorthCarolina,ChapelHill SeriesISSN Print2151-0067 Electronic2151-0075 Detecting Fake News on Social Media Kai Shu and Huan Liu ArizonaStateUniversity SYNTHESISLECTURESONDATAMININGANDKNOWLEDGE DISCOVERY#18 M &C Morgan &cLaypool publishers ABSTRACT Inthepastdecade,socialmediahasbecomeincreasinglypopularfornewsconsumptiondueto its easy access, fast dissemination, and low cost. However, social media also enables the wide propagationof“fakenews,”i.e.,newswithintentionallyfalseinformation.Fakenewsonsocial media can have significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area that is attracting tremendous attention. Thisbook,fromadataminingperspective,introducesthebasicconceptsandcharacteristicsof fakenewsacrossdisciplines,reviewsrepresentativefakenewsdetectionmethodsinaprincipled way,andillustrateschallengingissuesoffakenewsdetectiononsocialmedia.Inparticular,we discussedthevalueofnewscontentandsocialcontext,andimportantextensionstohandleearly detection, weakly-supervised detection, and explainable detection. The concepts, algorithms, andmethodsdescribedinthislecturecanhelpharnessthepowerofsocialmediatobuildeffective andintelligentfakenewsdetectionsystems.Thisbookisanaccessibleintroductiontothestudy of detecting fake news on social media. It is an essential reading for students, researchers, and practitionerstounderstand,manage,andexcelinthisarea. Thisbookissupportedbyadditionalmaterials,includinglectureslides,thecompleteset offigures,keyreferences,datasets,toolsusedinthisbook,andthesourcecodeofrepresentative algorithms.Thereadersareencouragedtovisitthebookwebsiteforthelatestinformation: http://dmml.asu.edu/dfn/ KEYWORDS fake news, misinformation, disinformation, social computing, social media, data mining,socialcybersecurity,machinelearning
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