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Table of Contents
Intro; Preface; Contents; 1 Introduction; 1.1 Credibility and Relevance of Web Content; 1.1.1 Credibility and Relevance in Human Communication; 1.1.2 Epistemic Similarities of Credibility and Relevance Judgments of Web Content; 1.2 Why Does Credibility Evaluation Support Matter on the Web?; 1.2.1 Examples of Non-credible Medical Web Content; 1.2.1.1 Vaccines and Autism; 1.2.1.2 Consuming Placenta; 1.2.1.3 Colloidal Silver; 1.2.2 Fake News in Web-Based Social Media; 1.2.3 Examples of Credibility Evaluation Support Systems; 1.2.3.1 Health on the Net; 1.2.3.2 WOT; 1.2.3.3 Snopes
1.2.3.4 PolitiFact1.3 Book Organization; 2 Understanding and Measuring Credibility; 2.1 Credibility and Truth; 2.1.1 Post-structuralist Truth; 2.1.2 Scientific Truth; 2.1.3 Semantic Truth Theory; 2.1.4 Incompleteness and Undecidability of Truth; 2.2 What Does It Mean to Support Credibility Evaluation?; 2.3 Definitions of Credibility; 2.3.1 Source Credibility; 2.3.1.1 Credibility Trust and Reputation; 2.3.1.2 Reputation; 2.3.1.3 Normative Trust and Credibility Trust; 2.3.1.4 Source Credibility Defined as Trust; 2.3.2 Media and System Credibility; 2.3.3 Message Credibility
2.3.4 Proposed Definition of Credibility2.3.5 Conclusion of Top-Down Discussion of Credibility; 2.4 Theories of Web Content Credibility; 2.4.1 Credibility Evaluation Checklists; 2.4.2 Iterative Model; 2.4.3 Predictive and Evaluative Model; 2.4.4 Fogg's Prominence-Interpretation Theory (2003); 2.4.5 Dual-Processing Model; 2.4.6 MAIN Model; 2.4.7 Ginsca's Model; 2.5 Measures of Credibility; 2.5.1 Ordinal and Cardinal Scales of Credibility; 2.5.2 Example Credibility Rating Scale; 2.5.3 Consensus Measures; 2.5.4 Distribution Similarity Tests; 2.5.5 The Earth Mover's Distance (EMD)
2.6 Credibility Measurement Experiments2.6.1 Fogg's Study; 2.6.2 Microsoft Credibility Corpus; 2.6.3 Panel Experiment (IIBR); 2.6.4 The Content Credibility Corpus; 2.6.4.1 C3 Dataset Augmentation with Tags; 2.6.5 Fake News Datasets; 2.7 Subjectivity of Credibility Measurements; 2.7.1 Robustness of Credibility Rating Distributions to Sample Composition; 2.8 Classes of Credibility; 2.8.1 Clustering Credibility Rating Distributions Using Earth Mover's Distance; 2.8.1.1 The AFT Dataset of Wikipedia Quality Ratings; 2.8.1.2 Clustering Algorithm; 2.8.1.3 Determining the Number of Clusters
2.8.1.4 Notation for Extreme Distributions2.8.2 Classes of Credibility Based on Distributions; 2.8.2.1 Fitting of Proposed Classes to Distribution Clusters; 2.8.2.2 Fitting the Controversy Class to Discovered Clusters; 2.8.3 Advantage of Defining Classes Using Distributions Over Arithmetic Mean; 2.9 Credibility Evaluation Criteria; 2.9.1 Identifying Credibility Evaluation Criteria from Textual Justifications; 2.9.2 Independence of Credibility Evaluation Criteria; 2.9.3 Modeling Credibility Evaluations Using Credibility Criteria; 3 Supporting Online Credibility Evaluation
1.2.3.4 PolitiFact1.3 Book Organization; 2 Understanding and Measuring Credibility; 2.1 Credibility and Truth; 2.1.1 Post-structuralist Truth; 2.1.2 Scientific Truth; 2.1.3 Semantic Truth Theory; 2.1.4 Incompleteness and Undecidability of Truth; 2.2 What Does It Mean to Support Credibility Evaluation?; 2.3 Definitions of Credibility; 2.3.1 Source Credibility; 2.3.1.1 Credibility Trust and Reputation; 2.3.1.2 Reputation; 2.3.1.3 Normative Trust and Credibility Trust; 2.3.1.4 Source Credibility Defined as Trust; 2.3.2 Media and System Credibility; 2.3.3 Message Credibility
2.3.4 Proposed Definition of Credibility2.3.5 Conclusion of Top-Down Discussion of Credibility; 2.4 Theories of Web Content Credibility; 2.4.1 Credibility Evaluation Checklists; 2.4.2 Iterative Model; 2.4.3 Predictive and Evaluative Model; 2.4.4 Fogg's Prominence-Interpretation Theory (2003); 2.4.5 Dual-Processing Model; 2.4.6 MAIN Model; 2.4.7 Ginsca's Model; 2.5 Measures of Credibility; 2.5.1 Ordinal and Cardinal Scales of Credibility; 2.5.2 Example Credibility Rating Scale; 2.5.3 Consensus Measures; 2.5.4 Distribution Similarity Tests; 2.5.5 The Earth Mover's Distance (EMD)
2.6 Credibility Measurement Experiments2.6.1 Fogg's Study; 2.6.2 Microsoft Credibility Corpus; 2.6.3 Panel Experiment (IIBR); 2.6.4 The Content Credibility Corpus; 2.6.4.1 C3 Dataset Augmentation with Tags; 2.6.5 Fake News Datasets; 2.7 Subjectivity of Credibility Measurements; 2.7.1 Robustness of Credibility Rating Distributions to Sample Composition; 2.8 Classes of Credibility; 2.8.1 Clustering Credibility Rating Distributions Using Earth Mover's Distance; 2.8.1.1 The AFT Dataset of Wikipedia Quality Ratings; 2.8.1.2 Clustering Algorithm; 2.8.1.3 Determining the Number of Clusters
2.8.1.4 Notation for Extreme Distributions2.8.2 Classes of Credibility Based on Distributions; 2.8.2.1 Fitting of Proposed Classes to Distribution Clusters; 2.8.2.2 Fitting the Controversy Class to Discovered Clusters; 2.8.3 Advantage of Defining Classes Using Distributions Over Arithmetic Mean; 2.9 Credibility Evaluation Criteria; 2.9.1 Identifying Credibility Evaluation Criteria from Textual Justifications; 2.9.2 Independence of Credibility Evaluation Criteria; 2.9.3 Modeling Credibility Evaluations Using Credibility Criteria; 3 Supporting Online Credibility Evaluation