Data Science for Fake News: Surveys and Perspective
Cham: Springer (2021), xiv, 302 pp.
Series: Information Retrieval Series, 42
ISBN 978-3-030-62696-9 (ebook); 978-3-030-62695-2 (print)
"This book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools." (Publisher description)
A Multifaceted Approach to Fake News, 1
PART I: SURVEY
On Unsupervised Methods for Fake News Detection, 17
Multi-modal Fake News Detection, 41
Deep Learning for Fake News Detection, 71
Dynamics of Fake News Diffusion, 101
Neural LanguageModels for (Fake?) News Generation, 129
Fact Checking on Knowledge Graphs, 149
Graph Mining Meets Fake News Detection, 169
PART II: PERSPECTIVES
Fake News in Health and Medicine, 193
Ethical Considerations in Data-Driven Fake News Detection, 205
A Political Science Perspective on Fake News, 233
Fake News and Social Processes: A Short Review, 245
Misinformation and the Indian Election: Case Study, 257
STS, Data Science, and Fake News: Questions and Challenges, 281
Linguistic Approaches to Fake News Detection, 287
PART I: SURVEY
On Unsupervised Methods for Fake News Detection, 17
Multi-modal Fake News Detection, 41
Deep Learning for Fake News Detection, 71
Dynamics of Fake News Diffusion, 101
Neural LanguageModels for (Fake?) News Generation, 129
Fact Checking on Knowledge Graphs, 149
Graph Mining Meets Fake News Detection, 169
PART II: PERSPECTIVES
Fake News in Health and Medicine, 193
Ethical Considerations in Data-Driven Fake News Detection, 205
A Political Science Perspective on Fake News, 233
Fake News and Social Processes: A Short Review, 245
Misinformation and the Indian Election: Case Study, 257
STS, Data Science, and Fake News: Questions and Challenges, 281
Linguistic Approaches to Fake News Detection, 287
