LEADER 09519nam 2200541 450 001 996464512003316 005 20231110230532.0 010 $a3-030-62696-2 035 $a(CKB)4100000011912065 035 $a(MiAaPQ)EBC6578691 035 $a(Au-PeEL)EBL6578691 035 $a(OCoLC)1249509776 035 $a(PPN)255289634 035 $a(EXLCZ)994100000011912065 100 $a20211210d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData science for fake news $esurveys and perspectives /$fDeepak P. [and three others] 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (308 pages) 225 1 $aThe Information Retrieval ;$vv.42 311 $a3-030-62695-4 320 $aIncludes bibliographical references. 327 $aIntro -- Preface -- Acknowledgments -- Contents -- A Multifaceted Approach to Fake News -- 1 Introduction -- 2 Surveys -- 2.1 Unsupervised Methods for Fake News Detection -- 2.2 Multimodal Fake News Detection -- 2.3 Deep Learning Methods for Fake News Detection -- 2.4 Dynamics of Fake News Diffusion -- 2.5 Neural Text Generation -- 2.6 Fact Checking on Knowledge Graph -- 2.7 Graph Mining Meets Fake News Detection -- 3 Perspectives -- 3.1 Fake News in Health Sciences -- 3.2 Ethical Considerations in Data-Driven Fake News Detection -- 3.3 A Political Science Perspective on Fake News -- 3.4 Fake News and Social Processes -- 3.5 Misinformation and the Indian Election -- 3.6 Science and Technology Studies (STS) and Fake News -- 3.7 Linguistic Approaches to Fake News Detection -- 4 Concluding Remarks -- References -- Part I Survey -- On Unsupervised Methods for Fake News Detection -- 1 Introduction -- 1.1 Paradigms of Machine Learning vis-a?-vis Supervision -- 1.2 Challenges for Unsupervised Learning in Fake News Detection -- 2 Unsupervised Fake News Detection: A Conceptual Analysis -- 2.1 Conceptual Basis for UFND Methods -- 2.2 Critical Analysis of UFND Conceptual Bases -- Truth Discovery -- Differentiating User Types -- Propagandist Patterns -- Inter-user Dynamics -- 2.3 Building Blocks for UFND -- 3 Unsupervised Fake News Detection: A Methodological Analysis -- 3.1 Truth Discovery -- 3.2 Differentiating User Types -- 3.3 Propagandist Patterns -- 3.4 Inter-user Dynamics -- 4 The Road Ahead for Unsupervised Fake News Detection -- 4.1 Specialist Domains and Authoritative Sources -- 4.2 Statistical Data for Fake News Detection -- 4.3 Early Detection -- 4.4 Miscellaneous -- Maligning Brands Through Fake Information -- Explainability in UFND -- 5 Conclusions -- References -- Multi-modal Fake News Detection -- 1 Introduction. 327 $a2 Challenges and Opportunities -- 3 Multi-modal Fake News Datasets -- 3.1 Fake Microblog Datasets -- 3.2 Fake News Datasets -- 4 State-of-the-Art Models -- 5 Unsupervised Approach -- 6 Early Fusion Approaches -- 6.1 JIN -- 6.2 TI-CNN -- 6.3 MKEMN -- 6.4 SpotFake and SpotFake+ -- 6.5 MCE -- 6.6 SAFE -- 7 Late Fusion Approaches -- 7.1 AGARWAL -- 7.2 MVNN -- 8 Hybrid Fusion Approach -- 9 Adversarial Model -- 9.1 SAME -- 10 Autoencoder Model -- 11 Summary of the Chapter -- References -- Deep Learning for Fake News Detection -- 1 Introduction -- 1.1 Fake News Types -- 1.2 Early Works -- 2 Deep Learning Methods -- 2.1 Fake News Detection Using CNN -- 2.2 Fake News Detection Using RNN and Its Variants -- 2.3 Multimodal Methods -- 3 Datasets and Evaluation Metrics -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Discussion -- 4 Trends in Fake News Detection Using Deep Learning -- 4.1 Geometric Deep Learning -- 4.2 Explainable Fake News Detection -- 4.3 Profiling Fake News Spreaders -- 4.4 Neural Fake News Detection -- 4.5 Discussion -- 5 Conclusion -- References -- Dynamics of Fake News Diffusion -- 1 Introduction -- 2 Fake News Diffusion on Facebook -- 3 Fake News Diffusion on Twitter -- 4 Role of Bots in Spreading Fake News -- 5 Trees for Modeling Information Diffusion -- 6 Identifying the Sources of Fake News -- 7 Modeling Fake News Diffusion -- 7.1 Susceptible-Infected-Recovered (SIR) Model -- 7.2 Dynamic Linear Threshold (DLT) -- Network Construction -- Problem Definition -- Component I: Diffusion Dynamics -- Component II: Updating Personal Belief -- Component III: Misinformation Blocking -- 7.3 Percolation Model -- Network Components -- Adoption of Information -- Branching and Size of Cascade -- Parameter Estimation -- 7.4 Spread-Stifle Model -- How the Spread-Stifle Model Differs from Others? -- Mean-Field Approach -- Reachability Probabilities. 327 $aTransition Probabilities -- Mean-Field Rate of Change -- 8 Strategies to Minimize the Spread of Fake News -- 9 Summary of the Chapter -- References -- Neural Language Models for (Fake?) News Generation -- 1 Introduction -- 2 Modeling Approaches -- 2.1 Learning Paradigms for NLG -- 2.2 Language Models -- 2.3 Encoder-Decoder Attention -- 2.4 Autoregression -- 2.5 Seq2Seq Model -- 3 Learning Paradigms -- 3.1 Supervised Learning Techniques -- 3.2 Adversarial Learning Techniques -- 3.3 Reinforcement Learning Techniques -- 3.4 Embedding Techniques -- 4 Pre-trained Language Models -- 4.1 Contextualized Word Vectors (CoVe) -- 4.2 Embeddings from Language Model (ELMo) -- 4.3 BERT -- 4.4 RoBERTa -- 4.5 Transformer-XL -- 4.6 Larger Language Models -- 4.7 XLNet -- 4.8 GROVER -- 4.9 CTRL -- 4.10 Seq2Seq Pre-training Models -- 4.11 Discussion -- 5 (Fake?) News Generation and Future Prospects -- 6 Conclusion -- References -- Fact Checking on Knowledge Graphs -- 1 Introduction -- 2 Preliminaries -- 2.1 Knowledge Graph -- 2.2 RDF -- 3 Models -- 4 Knowledge Linker -- 5 PredPath -- 6 Knowledge Stream -- 7 Conclusion and Future Work -- References -- Graph Mining Meets Fake News Detection -- 1 Characteristics and Challenges -- 2 Graph Models in Fake News Detection -- 2.1 Information -- 2.2 Graph Models -- 3 Unimodal Scenario: Static Graph-Based Methods -- 3.1 Graph Statistics Detection -- 3.2 Dense Subgraph Mining -- 3.3 Benefits and Issues -- 4 Multi-modal Scenario -- 4.1 Dynamic Graph-Based Approaches -- 4.2 Graph-Assisted Learning Approaches -- 4.3 Benefits and Issues -- 5 Summary of the Chapter -- References -- Part II Perspectives -- Fake News in Health and Medicine -- 1 Polish Health Misinformation Study -- 2 Stanford University Study: Cannabis, a Cure for Cancer -- 3 NBC News Study -- 4 Dandelion, the Magical Weed -- 5 Polarised Facts. 327 $a6 Fake News During the Pandemic -- 7 Consequences of Health Misinformation -- 8 Managing Health Misinformation -- References -- Ethical Considerations in Data-Driven Fake News Detection -- 1 Introduction -- 2 Ethical Dimensions of DFND -- 2.1 Mismatch of Values -- 2.2 Nature of Data-Driven Learning -- 2.3 Domain Properties -- 3 Fairness and DFND -- 4 Democratic Values and Uptake of DFND -- 5 Conclusions -- References -- A Political Science Perspective on Fake News -- 1 Introduction -- 2 The Origins of Fake News -- 3 Fake News in the Twenty-First Century -- 4 Fake News and the Study of Politics -- 5 Conclusion -- References -- Fake News and Social Processes: A Short Review -- 1 Introduction -- 2 Sociological Studies of Disinformation -- 3 Vaccine Hesitancy -- 4 Elections -- 5 Other Social Processes -- 6 Conclusions -- References -- Misinformation and the Indian Election: Case Study -- 1 Misinformation and Disinformation in India -- 1.1 Misinformation and Disinformation in India -- 1.2 Closed Networks for Disinformation -- 1.3 Scale, Prevalence, and Complexity of the Problem -- 1.4 Early Solutions and Fact-Checking in India -- 2 Logically's Capabilities -- 2.1 Automation to Augment Value -- 2.2 Credibility vs. Veracity -- 3 Credibility Assessment -- 3.1 Credibility Assessment -- Network Analysis -- Metadata -- Content Analysis -- 3.2 Credibility Assessment Methodology During Indian Elections -- Findings During Indian Elections -- Credibility Assessment: Evaluation -- 4 Veracity Assessment -- 4.1 Methodology: The Life Cycle of a Claim -- 4.2 Methodology During Indian Elections -- Findings During Indian Elections -- Evaluation -- 5 WhatsApp Solution for a Sharing Nation -- 5.1 Long-Standing Questions -- 5.2 Related Work -- 5.3 Exposing Misinformation on Closed Networks -- 5.4 Disseminating Verifications to Audiences Exposed to Mis/Disinformation. 327 $aSTS, Data Science, and Fake News: Questions and Challenges -- 1 Introduction -- 2 Truth, Power, and Knowledge -- 3 Truth Versus Post-truth -- References -- Linguistic Approaches to Fake News Detection -- 1 Introduction -- 1.1 Defining Fake News -- 1.2 Linguistics, Sub-disciplines, and Methods -- 1.3 News in Linguistics -- 1.4 Deception in Linguistics -- 1.5 Different Texts and Contexts -- 2 Linguistic Approaches to Fake News Detection -- 2.1 Bag of Words and LIWC -- 2.2 Readability and Punctuation -- 2.3 Deep Syntax -- 2.4 Rhetorical Structure and Discourse Analysis -- 3 Conclusions -- References. 410 4$aThe Information Retrieval 606 $aFake news 606 $aJournalism$xData processing 606 $aData mining 615 0$aFake news. 615 0$aJournalism$xData processing. 615 0$aData mining. 676 $a070.4 700 $aP$b Deepak$01060962 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464512003316 996 $aData science for fake news$92555173 997 $aUNISA LEADER 05608nam 2200685 450 001 9910788098403321 005 20200520144314.0 010 $a1-78217-171-1 035 $a(CKB)2670000000573752 035 $a(EBL)1706430 035 $a(SSID)ssj0001411772 035 $a(PQKBManifestationID)11882739 035 $a(PQKBTitleCode)TC0001411772 035 $a(PQKBWorkID)11405024 035 $a(PQKB)11625028 035 $a(Au-PeEL)EBL1706430 035 $a(CaPaEBR)ebr10967768 035 $a(CaONFJC)MIL655545 035 $a(OCoLC)894628630 035 $a(CaSebORM)9781782171706 035 $a(MiAaPQ)EBC1706430 035 $a(PPN)228022312 035 $a(EXLCZ)992670000000573752 100 $a20141111d2014 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMicrosoft System center 2012 R2 compliance management cookbook $eover 40 practical recipes that will help you plan, build, implement, and enhance IT compliance policies using Microsoft Security Compliance Manager and Microsoft System center 2012 R2 /$fAndreas Baumgarten, Ronnie Isherwood, Susan Roesner 205 $a1st edition 210 1$aBirmingham :$cPackt Publishing,$d2014. 215 $a1 online resource (284 p.) 225 0 $aQuick answers to common problems 300 $aIncludes index. 311 $a1-78217-170-3 311 $a1-322-24265-8 327 $aCover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Starting the Compliance Process for Small Businesses; Introduction; Planning the scope of a basic compliance program; Understanding possible controls for compliance; Evaluating the efforts of controls; Bringing it all together into a basic compliance program; Chapter 2: Implementing the First Steps of Basic Compliance; Introduction; Preparing for the creation of a compliance baseline; Installing Security Compliance Manager 327 $aCreating a compliance baseline using GPO to ensure system securityImplementing the GPO baseline in Active Directory; Chapter 3: Enhancing the Basic Compliance Program Using Microsoft System Center 2012 Configuration Manager; Introduction; Configuring Microsoft System Center 2012 Configuration Manager for compliance; Creating a baseline to monitor for unapproved software; Creating a baseline to monitor for unapproved hardware and virtual systems; Using Security Compliance Manager baselines in Microsoft System Center 2012 Configuration Manager; Chapter 4: Monitoring the Basic Compliance Program 327 $aIntroductionPlanning a compliance program for ; Microsoft System Center 2012 Operations Manager; Adding a compliance program monitor in ; Microsoft System Center 2012 Operations Manager; Installing Microsoft System Center 2012 Operations Manager Audit Collection Services to support the compliance program; Configuring a compliance program in Microsoft System Center 2012 Operations Manager Audit Collection Services; Chapter 5: Starting an Enterprise Compliance Program; Introduction; Using project management in your compliance approach; Understanding management support 327 $aDefining your communication approachPlanning the risk assessment approach; Planning documentation requirements; Defining your test approach; Chapter 6: Planning a Compliance Program in Microsoft System Center 2012; Introduction; Understanding the responsibilities of the System Center 2012 tools; Planning the implementation of Microsoft System Center 2012 Service Manager; Planning the connection of the System Center 2012 components; Planning and defining the responsibilities for a compliance program; Planning System Center Service Manager 2012 related settings and configuration 327 $aPlanning and defining compliance reports Chapter 7: Configuring a Compliance Program in Microsoft System Center 2012 Service Manager; Introduction; Configuring connectors in System Center 2012 Service Manager to support a compliance program; Adding configuration items manually in System Center 2012 Service Manager to support a compliance program; Configuring compliance process Incident Classification Categories in System Center 2012 Service Manager; Adding support groups in System Center 2012 Service Manager to support the compliance program 327 $aCreating compliance program Incident templates in System Center 2012 Service Manager 330 $aWhether you are an IT manager, an administrator, or security professional who wants to learn how Microsoft Security Compliance Manager and Microsoft System Center can help fulfil compliance and security requirements, this is the book for you. Prior knowledge of Microsoft System Center is required. 517 3 $aMicrosoft System center 2012 R2 compliance management cookbook :$eover forty practical recipes that will help you plan, build, implement, and enhance IT compliance policies using Microsoft Security Compliance Manager and Microsoft System center 2012 R2 606 $aSoftware configuration management$xComputer programs 606 $aCloud computing 615 0$aSoftware configuration management$xComputer programs. 615 0$aCloud computing. 676 $a004.6068 700 $aBaumgarten$b Andreas$01578544 702 $aIsherwood$b Ronnie 702 $aRoesner$b Susan 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910788098403321 996 $aMicrosoft System center 2012 R2 compliance management cookbook$93857984 997 $aUNINA