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Bias, Belief, and Conviction in an Age of Fake Facts / / edited by Anke Finger and Manuela Wagner
| Bias, Belief, and Conviction in an Age of Fake Facts / / edited by Anke Finger and Manuela Wagner |
| Pubbl/distr/stampa | London, United Kingdom : , : Taylor & Francis, , 2023 |
| Descrizione fisica | 1 online resource (226 pages) |
| Disciplina | 121.6 |
| Collana | Routledge Research in Cultural and Media Studies |
| Soggetto topico |
Belief and doubt
Fake news Truth |
| ISBN |
1-00-318793-5
1-000-80120-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910595083003321 |
| London, United Kingdom : , : Taylor & Francis, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Critical media literacy and fake news in post-truth America / / edited by C.Z. Goering and P.L. Thomas
| Critical media literacy and fake news in post-truth America / / edited by C.Z. Goering and P.L. Thomas |
| Pubbl/distr/stampa | Leiden ; ; Boston : , : Brill Sense, , 2018 |
| Descrizione fisica | 1 online resource (165 pages) |
| Disciplina | 070.43 |
| Altri autori (Persone) |
GoeringChristian Z
ThomasP. L <1961-> (Paul Lee) |
| Collana | Critical media literacies series |
| Soggetto topico |
Media literacy - United States
Fake news - United States Fake news Media literacy |
| Soggetto genere / forma | Electronic books. |
| ISBN | 90-04-36536-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Matter -- Copyright page -- Foreword -- Acknowledgments -- An Introduction / Christian Z. Goering and P. L. Thomas -- An Educator’s Primer / P. L. Thomas -- Reconsidering Evidence in Real World Arguments / Troy Hicks and Kristen Hawley Turner -- What is the Story? / Sharon A. Murchie and Janet A. Neyer -- Fighting “Fake News” in an Age of Digital Disorientation / Rob Williams -- Educating the Myth-LED / Robert Williams and Daniel Woods -- Teaching Critical Media Literacy as a Social Process in Writing Intensive Classrooms / Joanne Addison -- Before You Click “Share” / Jason L. Endacott , Matthew L. Dingler , Seth D. French and John P. Broome -- Engaging the Storied Mind / Erin O’Neill Armendarez -- Supporting Media-Savvy Youth-Activists / Mark A. Lewis -- Creating Wobble in a World of Spin / Sarah Bonner , Robyn Seglem and Antero Garcia -- Back Matter -- Author Biographies. |
| Record Nr. | UNINA-9910511761703321 |
| Leiden ; ; Boston : , : Brill Sense, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data science for fake news : surveys and perspectives / / Deepak P. [and three others]
| Data science for fake news : surveys and perspectives / / Deepak P. [and three others] |
| Autore | P Deepak |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (308 pages) |
| Disciplina | 070.4 |
| Collana | The Information Retrieval |
| Soggetto topico |
Fake news
Journalism - Data processing Data mining |
| ISBN | 3-030-62696-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- 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-à-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.
2 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. Transition 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. 6 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. STS, 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. |
| Record Nr. | UNINA-9910483306903321 |
P Deepak
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data science for fake news : surveys and perspectives / / Deepak P. [and three others]
| Data science for fake news : surveys and perspectives / / Deepak P. [and three others] |
| Autore | P Deepak |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (308 pages) |
| Disciplina | 070.4 |
| Collana | The Information Retrieval |
| Soggetto topico |
Fake news
Journalism - Data processing Data mining |
| ISBN | 3-030-62696-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- 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-à-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.
2 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. Transition 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. 6 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. STS, 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. |
| Record Nr. | UNISA-996464512003316 |
|
P Deepak
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Disinformation and fake news / / edited by Shashi Jayakumar, Benjamin Ang, Nur Diyanah Anwar
| Disinformation and fake news / / edited by Shashi Jayakumar, Benjamin Ang, Nur Diyanah Anwar |
| Pubbl/distr/stampa | Singapore : , : Palgrave Macmillan, , [2021] |
| Descrizione fisica | 1 online resource (xii, 158 pages) : illustrations (some color), charts, map |
| Disciplina | 320.6 |
| Collana |
Palgrave pivot
Gale eBooks |
| Soggetto topico | Fake news |
| ISBN | 981-15-5876-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Part I: Overview of Disinformation -- Chapter 2. How news audiences think about misinformation across the world -- Chapter 3. Tools of Disinformation: How fake news gets to deceive -- Chapter 4. Disinformation as a threat to national security -- Part II. Disinformation in Context -- Chapter 5. Building digital resilience ahead of elections and beyond -- Chapter 6. Hate speech in Myanmar: The perfect storm -- Chapter 7. Fighting information manipulation: The French experience -- Chapter 8. Disinformation and cultural practice in Southeast Asia -- Part III. Countering Disinformation -- Chapter 9. NATO amidst hybrid warfare threats- effective strategic communications as a tool against disinformation and propaganda -- Chapter 10. Lithuanian Elves and countermeasures -- Chapter 11. Fake News and Disinformation: Singapore perspectives. |
| Record Nr. | UNINA-9910484960303321 |
| Singapore : , : Palgrave Macmillan, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Dismantling cultural borders through social media and digital communications : how networked communities compromise identity / / edited by Emmanuel K. Ngwainmbi
| Dismantling cultural borders through social media and digital communications : how networked communities compromise identity / / edited by Emmanuel K. Ngwainmbi |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource |
| Disciplina | 305 |
| Soggetto topico | Fake news |
| ISBN |
9783030922122
9783030922115 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910547293003321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The epistemology of deceit in a postdigital era : dupery by design / / edited by Alison MacKenzie, Jennifer Rose, Ibrar Bhatt
| The epistemology of deceit in a postdigital era : dupery by design / / edited by Alison MacKenzie, Jennifer Rose, Ibrar Bhatt |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (312 pages) : illustrations (some color) |
| Disciplina | 121 |
| Collana | Postdigital Science and Education |
| Soggetto topico |
Fake news
Knowledge, Theory of Social sciences |
| ISBN | 3-030-72154-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction: The Genesis of Dupery by Design -- Part 1: Epistemology of Deceit -- Chapter 1 Bad Faith, Bad Politics, Bad Consequences: The Epistemic Harms of Online Deceit -- Chapter 2 An Epistemology of False Beliefs: The Role of Truth, Trust, And Technology In Postdigital Deception -- Chapter 3 Towards A Response to Epistemic Nihilism -- Chapter 4 Duperation: Deliberate Lying in Postdigital, Postmodern Political Rhetoric -- Chapter 5 The Right to Freedom of Expression versus Legal Actions against Fake News: A Case Study of Singapore -- Chapter 6 US Digital Nationalism: A Habermasean Critical Discourse Analysis of Trump’s ‘Fake News’ Approach to The First Amendment -- Chapter 7 A Project of Mourning: Attuning to the Impact ‘Anthropocentric-Noise Disorder’ on Non-Human Kin -- Chapter 8 Someone is Wrong on the Internet: Is There an Obligation to Correct False and Oppressive Speech on Social Media? -- Chapter 9 Writing Against the ‘Epistemology of Deceit’ on Wikipedia: A Feminist New Materialist Perspective Toward Critical Media Literacy and Wikipedia-based Education -- Chapter 10 The Neoliberal Colonization of Discourses: Gentrification, Discursive Markets and Zombemes -- Chapter 11 Social Memes and Depictions of Refugees in The EU – Challenging Irrationality and Misinformation with A Media Literacy Intervention -- Chapter 12 Scallywag Pedagogy -- Chapter 13 Learning from the Dupers: Showing the Workings -- Chapter 14 Ghosting Inside the Machine: Student Cheating, Online Education and the Omertà of Institutional Liars -- Chapter 15 'Choice is Yours': Anatomy of a Lesson Plan from University V -- Conclusion. |
| Record Nr. | UNINA-9910495202803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fake News Is Bad News : Hoaxes, Half-truths and the Nature of Today's Journalism / / Ján Višňovsky, Jana Radošinská, editors
| Fake News Is Bad News : Hoaxes, Half-truths and the Nature of Today's Journalism / / Ján Višňovsky, Jana Radošinská, editors |
| Pubbl/distr/stampa | London : , : IntechOpen, London, , 2021 |
| Descrizione fisica | 1 online resource (260 pages) |
| Disciplina | 070.43 |
| Soggetto topico |
Fake news
Fake news - History |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Fake News Is Bad News |
| Record Nr. | UNINA-9910688219103321 |
| London : , : IntechOpen, London, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Freedom of expression during COVID-19
| Freedom of expression during COVID-19 |
| Pubbl/distr/stampa | [Washington, D.C.] : , : The Law Library of Congresss, Global Legal Research Directorate, , 2020 |
| Descrizione fisica | 1 online resource (58 pages) : color map |
| Soggetto topico |
Freedom of expression
Mass media - Law and legislation Mass media - Censorship Fake news COVID-19 (Disease) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Freedom of Expression during COVID-19 |
| Record Nr. | UNINA-9910714914403321 |
| [Washington, D.C.] : , : The Law Library of Congresss, Global Legal Research Directorate, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Frontiers in fake media generation and detection / / edited by Mahdi Khosravy, Isao Echizen, Noboru Babaguchi
| Frontiers in fake media generation and detection / / edited by Mahdi Khosravy, Isao Echizen, Noboru Babaguchi |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (278 pages) |
| Disciplina | 303.375 |
| Collana | Studies in Autonomic, Data-Driven and Industrial Computing |
| Soggetto topico | Fake news |
| ISBN | 981-19-1524-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910574062003321 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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