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E-raamat: Web Content Credibility

  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Jun-2018
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319777948
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Web Content CredibilitySuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Jun-2018
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319777948
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This book introduces readers to Web content credibility evaluation and evaluation support. It highlights empirical research and establishes a solid foundation for future research by presenting methods of supporting credibility evaluation of online content, together with publicly available datasets for reproducible experimentation, such as the Web Content Credibility Corpus. 

The book is divided into six chapters. After a general introduction in Chapter 1, including a brief survey of credibility evaluation in the social sciences, Chapter 2 presents definitions of credibility and related concepts of truth and trust. Next, Chapter 3 details methods, algorithms and user interfaces for systems supporting Web content credibility evaluation. In turn, Chapter 4 takes a closer look at the credibility of social media, exemplified in sections on Twitter, Q&A systems, and Wikipedia, as well as fake news detection. In closing, Chapter 5 presents mathematical and simulation models of credibility evaluation, before a final round-up of the book is provided in Chapter 6.

Overall, the book reviews and synthesizes the current state of the art in Web content credibility evaluation support and fake news detection. It provides researchers in academia and industry with both an incentive and a basis for future research and development of Web content credibility evaluation support services.


1 Introduction
1(20)
1.1 Credibility and Relevance of Web Content
2(6)
1.1.1 Credibility and Relevance in Human Communication
4(2)
1.1.2 Epistemic Similarities of Credibility and Relevance Judgments of Web Content
6(2)
1.2 Why Does Credibility Evaluation Support Matter on the Web?
8(10)
1.2.1 Examples of Non-credible Medical Web Content
9(2)
1.2.2 Fake News in Web-Based Social Media
11(1)
1.2.3 Examples of Credibility Evaluation Support Systems
12(6)
1.3 Book Organization
18(3)
2 Understanding and Measuring Credibility
21(60)
2.1 Credibility and Truth
23(5)
2.1.1 Post-structuralist Truth
24(1)
2.1.2 Scientific Truth
25(2)
2.1.3 Semantic Truth Theory
27(1)
2.1.4 Incompleteness and Undecidability of Truth
27(1)
2.2 What Does It Mean to Support Credibility Evaluation?
28(4)
2.3 Definitions of Credibility
32(12)
2.3.1 Source Credibility
33(5)
2.3.2 Media and System Credibility
38(1)
2.3.3 Message Credibility
39(2)
2.3.4 Proposed Definition of Credibility
41(2)
2.3.5 Conclusion of Top-Down Discussion of Credibility
43(1)
2.4 Theories of Web Content Credibility
44(5)
2.4.1 Credibility Evaluation Checklists
44(1)
2.4.2 Iterative Model
45(1)
2.4.3 Predictive and Evaluative Model
46(1)
2.4.4 Fogg's Prominence-Interpretation Theory (2003)
46(1)
2.4.5 Dual-Processing Model
47(1)
2.4.6 MAIN Model
48(1)
2.4.7 Ginsca's Model
48(1)
2.5 Measures of Credibility
49(5)
2.5.1 Ordinal and Cardinal Scales of Credibility
50(1)
2.5.2 Example Credibility Rating Scale
51(1)
2.5.3 Consensus Measures
51(1)
2.5.4 Distribution Similarity Tests
52(1)
2.5.5 The Earth Mover's Distance (EMD)
52(2)
2.6 Credibility Measurement Experiments
54(4)
2.6.1 Fogg's Study
54(1)
2.6.2 Microsoft Credibility Corpus
54(1)
2.6.3 Panel Experiment (IIBR)
54(1)
2.6.4 The Content Credibility Corpus
55(2)
2.6.5 Fake News Datasets
57(1)
2.7 Subjectivity of Credibility Measurements
58(6)
2.7.1 Robustness of Credibility Rating Distributions to Sample Composition
61(3)
2.8 Classes of Credibility
64(9)
2.8.1 Clustering Credibility Rating Distributions Using Earth Mover's Distance
65(4)
2.8.2 Classes of Credibility Based on Distributions
69(3)
2.8.3 Advantage of Defining Classes Using Distributions Over Arithmetic Mean
72(1)
2.9 Credibility Evaluation Criteria
73(8)
2.9.1 Identifying Credibility Evaluation Criteria from Textual Justifications
74(3)
2.9.2 Independence of Credibility Evaluation Criteria
77(1)
2.9.3 Modeling Credibility Evaluations Using Credibility Criteria
77(4)
3 Supporting Online Credibility Evaluation
81(50)
3.1 Design of a Credibility Evaluation Support System
81(11)
3.1.1 Architecture
82(8)
3.1.2 Motivating Users of the Credibility Evaluation Support System
90(2)
3.2 Adversaries of Credibility Evaluation
92(1)
3.3 Classifiers of Webpage Credibility
93(6)
3.3.1 Webpage Features
94(4)
3.3.2 Performance of Classifiers of Webpage Credibility
98(1)
3.4 Recommending Content for Evaluation
99(4)
3.4.1 Robustness of CS Recommendation Algorithm to Imitating Adversary
101(2)
3.5 Evaluating Users' Reputation
103(6)
3.5.1 Reputation Systems and Algorithms for Web Content Quality Evaluation
105(2)
3.5.2 Role of Webpage Recommendations in Combating Adversaries
107(1)
3.5.3 Resistance of the Reputation System to Sybil Attacks
108(1)
3.5.4 Tackling the Cold-Start Problem of the Reputation System
109(1)
3.6 Aggregation Algorithms for Credibility Evaluations
109(6)
3.6.1 Fusion Algorithms for Crowdsourcing
110(1)
3.6.2 Fusion Algorithms for Computing Credibility Distributions
111(4)
3.7 Using Statement Credibility Evaluations
115(3)
3.7.1 Systems Supporting Statement Credibility Evaluation on the Web
115(1)
3.7.2 Statement Analyzer of the CS System
116(2)
3.8 Automatic Prediction of Topic Controversy
118(4)
3.8.1 Defining Web Content and Topic Controversy
119(1)
3.8.2 Supporting Controversy Detection on the Web
120(1)
3.8.3 Controversy Tagging in the Credibility Evaluation System
121(1)
3.9 Presentation of Credibility Evaluations and Its Influence on Users
122(6)
3.9.1 CS System Persuasiveness
125(3)
3.10 Essential Elements of a CS System's Design
128(3)
4 Credibility of Social Media
131(24)
4.1 Credibility of Tweets and Twitter Users
132(7)
4.1.1 Characterizing Credibility Evaluations on Twitter
133(1)
4.1.2 Supporting Credibility Evaluation on Twitter
134(4)
4.1.3 Controversy on Twitter
138(1)
4.2 Credibility and Quality of Answers in Q&A Systems
139(7)
4.2.1 Ground Truth of Credibility Evaluation Support in Q&A Systems
140(2)
4.2.2 Answer Credibility Evaluation and Ranking
142(1)
4.2.3 Finding the Most Relevant and Credible Answers
143(1)
4.2.4 Automatic Answering of Questions
144(1)
4.2.5 Summary of State of the Art in Q&A Credibility Evaluation
145(1)
4.3 Quality, Credibility, and Controversy of Wikipedia Articles
146(4)
4.3.1 Ground Truth for Wikipedia Article Credibility
146(1)
4.3.2 Modeling Wikipedia Article Credibility
147(1)
4.3.3 Modeling Wikipedia Article Controversy
148(2)
4.4 Fake News Detection in Social Media
150(5)
5 Theoretical Models of Credibility
155(50)
5.1 Signal-Based Credibility Models
157(6)
5.1.1 Normalized Random Utility Signal Model
158(3)
5.1.2 NRUM Evaluation
161(2)
5.2 The Credibility Game
163(16)
5.2.1 Model Requirements
165(1)
5.2.2 Basic Model
166(2)
5.2.3 Economic Model
168(1)
5.2.4 Equilibrium Analysis
169(1)
5.2.5 Analysis of Credibility Game Without Signal
170(1)
5.2.6 Analysis of Credibility Game with Signal
171(3)
5.2.7 Studying Impact of Consumer Expertise
174(5)
5.3 Credibility Game with Reputation
179(10)
5.3.1 Reputation System for Credibility Game
180(1)
5.3.2 Role of Reputation in Strategies of Content Consumers
181(1)
5.3.3 Signal-Based and Payoff-Based Reputation
182(1)
5.3.4 Impact of Reputation on Credibility Evaluation
182(7)
5.4 Sensitivity Analysis to Changing Payoffs
189(1)
5.5 Modeling Web 2.0 Content Credibility Evaluation Using Symmetric Credibility Game
190(13)
5.5.1 Payoffs of Symmetric Credibility Game
192(1)
5.5.2 Modeling Agent Learning and Knowledge
193(1)
5.5.3 Consumer Acceptance in the Symmetric Credibility Game with Learning
194(1)
5.5.4 Simulation of Symmetric Credibility Game with Learning
195(1)
5.5.5 Hypotheses Regarding Learning Knowledge Communities and Credibility Evaluation
195(1)
5.5.6 Speed of Community Learning
196(4)
5.5.7 Community Robustness
200(3)
5.6 Conclusions
203(2)
6 Conclusions
205(2)
References 207(12)
Index 219
Adam Wierzbicki is Professor and Vice-President of the Polish-Japanese Institute for Information Technology. His current research interests focus on social informatics, in particular on credibility, trust management, collective intelligence and fairness in distributed systems, and he has published numerous papers in these areas. In 2011-2015, he has led the Reconcile project which concerned the development of tools for the evaluation of Web content credibility. He is also the Steering Committee Chair of the International Conference on Social Informatics.
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