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Bürgerjournalismus im Web / / Stefan Bosshart
| Bürgerjournalismus im Web / / Stefan Bosshart |
| Autore | Bosshart Stefan |
| Pubbl/distr/stampa | Tübingen : , : UVK Verlag, , 2021 |
| Descrizione fisica | 1 online resource |
| Disciplina | 070.43 |
| Soggetto topico |
Citizen journalism
Digital media Journalism - Data processing Online journalism |
| ISBN |
3-7445-1131-6
3-7398-0109-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ger |
| Altri titoli varianti | Incoming-Tourismus China und Indien |
| Record Nr. | UNINA-9910163138503321 |
Bosshart Stefan
|
||
| Tübingen : , : UVK Verlag, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The data journalism handbook : towards a critical data practice / / edited by Liliana Bounegru and Jonathan Gray [[electronic resource]]
| The data journalism handbook : towards a critical data practice / / edited by Liliana Bounegru and Jonathan Gray [[electronic resource]] |
| Autore | Bounegru Liliana |
| Pubbl/distr/stampa | Amsterdam, : Amsterdam University Press, 2021 |
| Descrizione fisica | 1 online resource (415 pages) : digital, PDF file(s) |
| Disciplina | 070.4 |
| Collana | Digital studies |
| Soggetto topico |
Journalism - Data processing
Data mining Information visualization |
| Soggetto non controllato |
Data journalism
big data and society critical data studies data sociology journalism practice new media and digital culture science and technology studies |
| ISBN | 90-485-4207-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Table of Contents -- Introduction -- Doing Issues With Data -- 1. From Coffee to Colonialism: Data Investigations Into How the Poor Feed the Rich -- 2. Repurposing Census Data to Measure Segregation in the United States -- 3. Multiplying Memories while Discovering Trees in Bogotá -- 4. Behind the Numbers: Home Demolitions in Occupied East Jerusalem -- 5. Mapping Crash Incidents to Advocate for Road Safety in the Philippines -- 6. Tracking Worker Deaths in Turkey -- Assembling Data -- 7. Building Your Own Data Set: Documenting Knife Crime in the United Kingdom -- 8. Narrating a Number and Staying With the Trouble of Value -- 9. Indigenous Data Sovereignty: Implications for Data Journalism -- 10. Alternative Data Practices in China -- 11. Making a Database to Document Land Conflicts Across India -- 12. Reassembling Public Data in Cuba: Collaborations When Information Is Missing, Outdated or Scarce -- 13. Making Data With Readers at La Nación -- 14. Running Surveys for Investigations -- Working With Data -- 15. Data Journalism: What's Feminism Got to Do With I.T.? -- 16. Infrastructuring Collaborations Around the Panama and Paradise Papers -- 17. Text as Data: Finding Stories in Text Collections -- 18. Coding With Data in the Newsroom -- 19. Accounting for Methods: Spreadsheets, Scripts and Programming Notebooks -- 20. Working Openly in Data Journalism -- 21. Making Algorithms Work for Reporting -- 22. Journalism With Machines? From Computational Thinking to Distributed Cognition -- Experiencing Data -- 23. Ways of Doing Data Journalism -- 24. Data Visualizations: Newsroom Trends and Everyday Engagements -- 25. Sketching With Data -- 26. The Web as Medium for Data Visualization -- 27. Four Recent Developments in News Graphics -- 28. Searchable Databases as a Journalistic Product -- 29. Narrating Water Conflict With Data and Interactive Comics -- 30. Data Journalism Should Focus on People and Stories -- Investigating Data, Platforms and Algorithms -- 31. The Algorithms Beat: Angles and Methods for Investigation -- 32. Telling Stories With the Social Web -- 33. Digital Forensics: Repurposing Google Analytics IDs -- 34. Apps and Their Affordances for Data Investigations -- 35. Algorithms in the Spotlight: Collaborative Investigations at Der Spiegel -- Organizing Data Journalism -- 36. The #ddj Hashtag on Twitter -- 37. Archiving Data Journalism -- 38. From The Guardian to Google News Lab: A Decade of Working in Data Journalism -- 39. Data Journalism's Ties With Civic Tech -- 40. Open-Source Coding Practices in Data Journalism -- 41. Data Feudalism: How Platforms Shape Cross-border Investigative Networks -- 42. Data-Driven Editorial? Considerations for Working With Audience Metrics -- Learning Data Journalism Together -- 43. Data Journalism, Digital Universalism and Innovation in the Periphery -- 44. The Datafication of Journalism: Strategies for Data-Driven Storytelling and Industry-Academy Collaboration -- 45. Data Journalism by, about and for Marginalized Communities -- 46. Teaching Data Journalism -- 47. Organizing Data Projects With Women and Minorities in Latin America -- Situating Data Journalism -- 48. Genealogies of Data Journalism -- 49. Data-Driven Gold Standards: What the Field Values as Award-Worthy Data Journalism -- 50. Beyond Clicks and Shares: How and Why to Measure the Impact of Data Journalism Projects -- 51. Data Journalism: In Whose Interests? -- 52. Data Journalism With Impact -- 53. What Is Data Journalism For? Cash, Clicks, and Cut and Trys -- 54. Data Journalism and Digital Liberalism -- Index |
| Record Nr. | UNINA-9910476890003321 |
|
Bounegru Liliana
|
||
| Amsterdam, : Amsterdam University Press, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The data journalism handbook / / edited by Jonathan Gray, Liliana Bounegru, and Lucy Chambers ; illustrator, Kate Hudson
| The data journalism handbook / / edited by Jonathan Gray, Liliana Bounegru, and Lucy Chambers ; illustrator, Kate Hudson |
| Autore | Gray Jonathan |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Sebastopol, California : , : O'Reilly Media, , 2012 |
| Descrizione fisica | 1 online resource (242 pages) : illustrations (chiefly color), maps (chiefly color) |
| Disciplina | 070.4/30285 |
| Altri autori (Persone) |
GrayJonathan
BounegruLiliana ChambersLucy HudsonKate <1979-> |
| Collana | Theory in practice |
| Soggetto topico |
Journalism - Data processing
Data mining Information visualization |
| ISBN |
1-4493-3002-9
1-306-81286-0 1-4493-3004-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | In the newsroom -- Case studies -- Getting data -- Understanding data -- Delivering data. |
| Record Nr. | UNINA-9910139085203321 |
|
Gray Jonathan
|
||
| Sebastopol, California : , : O'Reilly Media, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The data journalism handbook / / edited by Jonathan Gray, Liliana Bounegru, and Lucy Chambers ; illustrator, Kate Hudson
| The data journalism handbook / / edited by Jonathan Gray, Liliana Bounegru, and Lucy Chambers ; illustrator, Kate Hudson |
| Autore | Gray Jonathan |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Sebastopol, California : , : O'Reilly Media, , 2012 |
| Descrizione fisica | 1 online resource (242 pages) : illustrations (chiefly color), maps (chiefly color) |
| Disciplina | 070.4/30285 |
| Altri autori (Persone) |
GrayJonathan
BounegruLiliana ChambersLucy HudsonKate <1979-> |
| Collana | Theory in practice |
| Soggetto topico |
Journalism - Data processing
Data mining Information visualization |
| ISBN |
1-4493-3002-9
1-306-81286-0 1-4493-3004-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | In the newsroom -- Case studies -- Getting data -- Understanding data -- Delivering data. |
| Record Nr. | UNISA-996320234203316 |
|
Gray Jonathan
|
||
| Sebastopol, California : , : O'Reilly Media, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
The data journalism handbook : towards a critical data practice / / edited by Liliana Bounegru and Jonathan Gray [[electronic resource]]
| The data journalism handbook : towards a critical data practice / / edited by Liliana Bounegru and Jonathan Gray [[electronic resource]] |
| Pubbl/distr/stampa | Amsterdam : , : Amsterdam University Press, , 2021 |
| Descrizione fisica | 1 online resource (415 pages) : digital, PDF file(s) |
| Disciplina | 070.4 |
| Collana | Digital studies |
| Soggetto topico |
Journalism - Data processing
Data mining Information visualization |
| Soggetto non controllato |
Data journalism
big data and society critical data studies data sociology journalism practice new media and digital culture science and technology studies |
| ISBN | 90-485-4207-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Table of Contents -- Introduction -- Doing Issues With Data -- 1. From Coffee to Colonialism: Data Investigations Into How the Poor Feed the Rich -- 2. Repurposing Census Data to Measure Segregation in the United States -- 3. Multiplying Memories while Discovering Trees in Bogotá -- 4. Behind the Numbers: Home Demolitions in Occupied East Jerusalem -- 5. Mapping Crash Incidents to Advocate for Road Safety in the Philippines -- 6. Tracking Worker Deaths in Turkey -- Assembling Data -- 7. Building Your Own Data Set: Documenting Knife Crime in the United Kingdom -- 8. Narrating a Number and Staying With the Trouble of Value -- 9. Indigenous Data Sovereignty: Implications for Data Journalism -- 10. Alternative Data Practices in China -- 11. Making a Database to Document Land Conflicts Across India -- 12. Reassembling Public Data in Cuba: Collaborations When Information Is Missing, Outdated or Scarce -- 13. Making Data With Readers at La Nación -- 14. Running Surveys for Investigations -- Working With Data -- 15. Data Journalism: What's Feminism Got to Do With I.T.? -- 16. Infrastructuring Collaborations Around the Panama and Paradise Papers -- 17. Text as Data: Finding Stories in Text Collections -- 18. Coding With Data in the Newsroom -- 19. Accounting for Methods: Spreadsheets, Scripts and Programming Notebooks -- 20. Working Openly in Data Journalism -- 21. Making Algorithms Work for Reporting -- 22. Journalism With Machines? From Computational Thinking to Distributed Cognition -- Experiencing Data -- 23. Ways of Doing Data Journalism -- 24. Data Visualizations: Newsroom Trends and Everyday Engagements -- 25. Sketching With Data -- 26. The Web as Medium for Data Visualization -- 27. Four Recent Developments in News Graphics -- 28. Searchable Databases as a Journalistic Product -- 29. Narrating Water Conflict With Data and Interactive Comics -- 30. Data Journalism Should Focus on People and Stories -- Investigating Data, Platforms and Algorithms -- 31. The Algorithms Beat: Angles and Methods for Investigation -- 32. Telling Stories With the Social Web -- 33. Digital Forensics: Repurposing Google Analytics IDs -- 34. Apps and Their Affordances for Data Investigations -- 35. Algorithms in the Spotlight: Collaborative Investigations at Der Spiegel -- Organizing Data Journalism -- 36. The #ddj Hashtag on Twitter -- 37. Archiving Data Journalism -- 38. From The Guardian to Google News Lab: A Decade of Working in Data Journalism -- 39. Data Journalism's Ties With Civic Tech -- 40. Open-Source Coding Practices in Data Journalism -- 41. Data Feudalism: How Platforms Shape Cross-border Investigative Networks -- 42. Data-Driven Editorial? Considerations for Working With Audience Metrics -- Learning Data Journalism Together -- 43. Data Journalism, Digital Universalism and Innovation in the Periphery -- 44. The Datafication of Journalism: Strategies for Data-Driven Storytelling and Industry-Academy Collaboration -- 45. Data Journalism by, about and for Marginalized Communities -- 46. Teaching Data Journalism -- 47. Organizing Data Projects With Women and Minorities in Latin America -- Situating Data Journalism -- 48. Genealogies of Data Journalism -- 49. Data-Driven Gold Standards: What the Field Values as Award-Worthy Data Journalism -- 50. Beyond Clicks and Shares: How and Why to Measure the Impact of Data Journalism Projects -- 51. Data Journalism: In Whose Interests? -- 52. Data Journalism With Impact -- 53. What Is Data Journalism For? Cash, Clicks, and Cut and Trys -- 54. Data Journalism and Digital Liberalism -- Index |
| Record Nr. | UNISA-996435448103316 |
| Amsterdam : , : Amsterdam University Press, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
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 | ||
| ||
LENS 2019 : proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News (LENS 2019) : November 5, 2019, Chicago, Illinois, USA / / Association for Computing Machinery
| LENS 2019 : proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News (LENS 2019) : November 5, 2019, Chicago, Illinois, USA / / Association for Computing Machinery |
| Pubbl/distr/stampa | New York, NY : , : The Association for Computing Machinery, , [2019] |
| Descrizione fisica | 1 online resource : illustrations |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Digital media Geospatial data Journalism - Data processing Online social networks |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910412061603321 |
| New York, NY : , : The Association for Computing Machinery, , [2019] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The life informatic [[electronic resource] ] : newsmaking in the digital era / / Dominic Boyer
| The life informatic [[electronic resource] ] : newsmaking in the digital era / / Dominic Boyer |
| Autore | Boyer Dominic |
| Pubbl/distr/stampa | Ithaca, : Cornell University Press, 2013 |
| Descrizione fisica | 1 online resource : illustrations |
| Disciplina | 070.4/30285 |
| Collana | Expertise : cultures and technologies of knowledge |
| Soggetto topico |
Electronic news gathering
Journalism - Data processing Journalism - Computer network resources Journalism - Technological innovations Online journalism Digital media |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-8014-6734-9
1-322-50315-X 0-8014-6735-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction : news journalism today -- The craft of slotting : screenwork, attentional practices and news value at an international news agency -- Click and spin : time, feedback and expertise at an online news portal -- Countdown : professionalism, publicity and political culture in 24/7 news radio -- The news informatic : five reflections on journalism in the era of digital liberalism -- Epilogue : informatic unconscious : on the evolution of digital reason in anthropology. |
| Record Nr. | UNINA-9910465065903321 |
|
Boyer Dominic
|
||
| Ithaca, : Cornell University Press, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The life informatic [[electronic resource] ] : newsmaking in the digital era / / Dominic Boyer
| The life informatic [[electronic resource] ] : newsmaking in the digital era / / Dominic Boyer |
| Autore | Boyer Dominic |
| Pubbl/distr/stampa | Ithaca, : Cornell University Press, 2013 |
| Descrizione fisica | 1 online resource : illustrations |
| Disciplina | 070.4/30285 |
| Collana | Expertise : cultures and technologies of knowledge |
| Soggetto topico |
Electronic news gathering
Journalism - Data processing Journalism - Computer network resources Journalism - Technological innovations Online journalism Digital media |
| ISBN |
0-8014-6734-9
1-322-50315-X 0-8014-6735-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction : news journalism today -- The craft of slotting : screenwork, attentional practices and news value at an international news agency -- Click and spin : time, feedback and expertise at an online news portal -- Countdown : professionalism, publicity and political culture in 24/7 news radio -- The news informatic : five reflections on journalism in the era of digital liberalism -- Epilogue : informatic unconscious : on the evolution of digital reason in anthropology. |
| Record Nr. | UNINA-9910792046803321 |
|
Boyer Dominic
|
||
| Ithaca, : Cornell University Press, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
