Table Of ContentSeries ISSN: 2151-0067
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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
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Detecting Fake News on Social Media E
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Kai Shu, Arizona State University T
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Huan Liu, Arizona State University
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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
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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
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and Computer Science. Synthesis books provide concise, original presentations of important research G
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and development topics, published quickly, in digital and print formats.
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store.morganclaypool.com P
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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
