Title Detecting Fake News on Social Media
Author Kai Shu, Huan Liu
ISBN 9781681735825
List price USD 49.95
Price outside India Available on Request
Original price
Binding Paperback
No of pages 130
Book size 191 X 235 mm
Publishing year 2019
Original publisher Morgan & Claypool Publishers (Eurospan Group)
Published in India by .
Exclusive distributors Viva Books Private Limited
Sales territory India, Sri Lanka, Bangladesh, Pakistan, Nepal, .
Status New Arrival
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In the past decade, social media is becoming increasingly popular for news consumption due to its easy access, fast dissemination, and low cost. However, social media also enables the wide propagation of “fake news,” i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. From a data mining perspective, this book introduces the basic concepts and characteristics of fake news across disciplines, reviews representative fake news detection methods in a principled way, and illustrates advanced settings of fake news detection on social media. In particular, the authors discuss the value of news content and social context, as well as important extensions to handle early detection, weakly-supervised detection, and explainable detection. The concepts, algorithms, and methods described in this book can help harness the power of social media to build effective and intelligent fake news detection systems. This book is an accessible introduction to the study of detecting fake news on social media. It is an essential reading for students, researchers, and practitioners to understand, manage, and excel in this area.

The book is supported by additional materials, including lecture slides, the complete set of figures, key references, datasets, tools used in this book, and the source code of representative algorithms.



Chapter 1. Introduction • Motivation • An Interdisciplinary View on Fake News • Fake News in Social Media Age • Characteristics of Social Media • Problem Definition • What News Content Tells • How Social Context Helps • Challenging Problems of Fake News Detection

Chapter 2. What News Content Tells • Textual Features • Linguistic Features • Low-Rank Textual Features • Neural Textual Features • Visual Features • Visual Statistical Features • Visual Content Features • Neural Visual Features • Style Features • Deception Styles • Clickbaity Styles • News Quality Styles • Knowledge-Based Methods • Manual Fact-Checking • Automatic Fact-Checking

Chapter 3. How Social Context Helps • User-Based Detection • User Feature Modeling User Behavior Modeling • Post-Based Detection • Stance-Aggregated Modeling • Emotion-Enhanced Modeling • Credibility-Propagated Modeling • Network-Based Detection • Representative Network Types • Friendship Networking Modeling • Diffusion Network Temporal Modeling • Interaction Network Modeling • Propagation Network Deep-Geometric Modeling • Hierarchical Propagation Network Modeling

Chapter 4. Challenging Problems of Fake News Detection • Fake News Early Detection • A User-Response Generation Approach • An Event-Invariant Adversarial Approach • A Propagation-Path Modeling Approach • Weakly Supervised Fake News Detection • A Tensor Decomposition Semi-Supervised Approach • A Tensor Decomposition Unsupervised Approach • A Probabilistic Generative Unsupervised Approach • Explainable Fake News Detection • A Web Evidence-Aware Approach • A Social Context-Aware Approach

A Data Repository

B Tools

C Relevant Activities


Authors’ Biographies

About the Authors:

Kai Shu is a Ph.D. student and research assistant at the Data Mining and Machine Learning (DMML) Lab at Arizona State University. He received his B.S./M.S. from Chongqing University in 2012 and 2015, respectively. His research interests include fake news detection, social computing, data mining, and machine learning. He was awarded ASU CIDSE Doctorial Fellowship 2015, the 1st place of SBP Disinformation Challenge 2018, University Graduate Fellowship, and various scholarships. He co-presented two tutorials in KDD 2019 and WSDM 2019, and has published innovative works in highly ranked journals and top conference proceedings such as ACM KDD, WSDM, WWW, CIKM, IEEE ICDM, IJCAI, AAAI, and MICCAI. He also worked as a research intern at Yahoo! Research and Microsoft Research in 2018 and 2019, respectively

Huan Liu is a professor of Computer Science and Engineering at Arizona State University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at the National University of Singapore. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating interdisciplinary problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. He is a co-author of Social Media Mining: An Introduction (Cambridge University Press). He is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction, Field Chief Editor of Frontiers in Big Data and its Specialty Chief Editor in Data Mining and Management. He is a Fellow of ACM, AAAI, AAAS, and IEEE. University of Illinois at Urbana-Champaign University of California Santa Cruz.

Target Audience:

From a data mining perspective, this book introduces the basic concepts and characteristics of fake news across disciplines, reviews representative fake news detection methods in a principled way, and illustrates advanced settings of fake news detection on social media.


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