Disinformation, misinformation, and fake news in social media : emerging research challenges and opportunities
著者
書誌事項
Disinformation, misinformation, and fake news in social media : emerging research challenges and opportunities
(Lecture notes in social networks)
Springer, [2020]
機械可読データファイル(リモートファイル)
大学図書館所蔵 全1件
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注記
Includes bibliographical references and index
Description based on online resource; title from digital title page (viewed on June 30, 2020)
収録内容
- Intro
- Acknowledgment
- Contents
- Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements
- 1 Information Disorder
- 1.1 Fake News as an Example of Disinformation
- 2 The Power of Weak Social Supervision
- 2.1 Understanding Disinformation with WSS
- 2.2 Detecting Disinformation with WSS
- 3 Recent Advancements: An Overview of Chapter Topics
- 4 Looking Ahead
- 4.1 Explanatory Methods
- 4.2 Neural Fake News Generation and Detection
- 4.3 Early Detection of Disinformation
- 4.4 Cross Topics Modeling on Disinformation
- References
- Part I User Engagements in the Dissemination of Information Disorder
- Discover Your Social Identity from What You Tweet: A Content Based Approach
- 1 Introduction
- 2 Related Work
- 3 Method
- 3.1 Word Embedding
- 3.2 Bi-LSTM
- 3.3 Attention
- 3.4 Final Classification
- 4 Experiments
- 4.1 Dataset
- 4.2 Hyperparameter Setting
- 4.3 Baselines
- 4.4 Results
- 4.5 Transfer Learning for Fine-Grained Identity Classification
- 4.6 Case Study
- 4.7 Error Analysis
- 5 Discussion and Conclusion
- References
- User Engagement with Digital Deception
- 1 Methods and Materials
- 1.1 Attributing News Sources
- 1.2 Inferring User Account Types: Automated Versus Manual
- 1.3 Predicting User Demographics
- 1.4 Measuring Inequality of User Engagement
- 1.5 Predicting User Reactions to Deceptive News
- 1.6 Data
- 2 Who Engages with (mis) and (dis)information?
- 2.1 The Population Who Engage with Misinformation and Disinformation
- 2.2 Automated Versus Manual Accounts
- 2.3 Sockpuppets: Multiple Accounts for Deception
- 2.4 Demographic Sub-populations
- 3 What Kind of Feedback Do Users Provide?
- 3.1 Across Multiple Platforms
- 3.2 Across User-Account Characteristics
- 4 How Quickly Do Users Engage with (mis) and (dis)information?
- 4.1 Across Multiple Platforms
- 4.2 Across User-Account Characteristics
- 4.3 Demographic Sub-populations
- 5 Discussion and Conclusions
- References
- Characterization and Comparison of Russian and Chinese Disinformation Campaigns
- 1 Introduction
- 2 Literature Review
- 2.1 Russia Internet Research Agency Data
- 2.2 Chinese Manipulation of Hong Kong Narrative
- 3 Data
- 4 Characterization and Comparison
- 4.1 Network
- 4.2 History of Accounts
- 4.3 Geography of Accounts
- 4.4 Calculating Content Marketshare Over Time
- 4.5 Bot Analysis
- 4.6 Multi-media Analysis
- 4.7 State Sponsored Accounts
- 5 How Many Similar Actors are Left?
- 6 Conclusion
- References
- Pretending Positive, Pushing False: Comparing Captain Marvel Misinformation Campaigns
- 1 Introduction
- 2 Related Work
- 3 Data Description and Methods
- 4 Results
- 4.1 Diffusion on Twitter
- 4.2 Originating and Responding Twitter Communities
- 4.3 Types of Actors
- 5 Discussion and Conclusions
- References
- Bots, Elections, and Social Media: A Brief Overview
- 1 Introduction