In the past decade, social media has become 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 area that is attracting tremendous attention. This book, from a data mining perspective, introduces the basic concepts and characteristics of fake news across disciplines, reviews representative fake news detection methods in a principled way, and illustrates challenging issues of fake news detection on social media. In particular, we discussed the value of news content and social context, and important extensions to handle early detection, weakly-supervised detection, and explainable detection. The concepts, algorithms, and methods described in this lecture 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. This 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. The readers are encouraged to visit the book website for the latest information: http://dmml.asu.edu/dfn/

Social media has revolutionized how individuals, communities, and organizations create, share, and consume information. Similarly, social media offers numerous opportunities as well as enormous social and economic ills for individuals, communities, and organizations. Despite the increase in popularity of social networking sites and related digital media, there are limited data and studies on consumption patterns of the new media by different global communities. Analyzing Global Social Media Consumption is an essential reference book that investigates the current trends, practices, and newly emerging narratives on theoretical and empirical research on all aspects of social media and its global use. Covering topics that include fake news detection, social media addiction, and motivations and impacts of social media use, this book is ideal for big data analysts, media and communications experts, researchers, academicians, and students in media and communications, information systems, and information technology study programs.

This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly identify new research problems in their domains. The contributors to this volume describe the recent advancements in three related parts: (1) user engagements in the dissemination of information disorder; (2) techniques on detecting and mitigating disinformation; and (3) trending issues such as ethics, blockchain, clickbaits, etc. This edited volume will appeal to students, researchers, and professionals working on disinformation, misinformation and fake news in social media from a unique lens.

New perspectives on the misinformation ecosystem that is the production and circulation of fake news. What is fake news? Is it an item on Breitbart, an article in The Onion, an outright falsehood disseminated via Russian bot, or a catchphrase used by a politician to discredit a story he doesn't like? This book examines the real fake news: the constant flow of purposefully crafted, sensational, emotionally charged, misleading or totally fabricated information that mimics the form of mainstream news. Rather than viewing fake news through a single lens, the book maps the various kinds of misinformation through several different disciplinary perspectives, taking into account the overlapping contexts of politics, technology, and journalism. The contributors consider topics including fake news as “disorganized” propaganda; folkloric falsehood in the “Pizzagate” conspiracy; native advertising as counterfeit news; the limitations of regulatory reform and technological solutionism; Reddit's enabling of fake news; the psychological mechanisms by which people make sense of information; and the evolution of fake news in America. A section on media hoaxes and satire features an oral history of and an interview with prankster-activists the Yes Men, famous for parodies that reveal hidden truths. Finally, contributors consider possible solutions to the complex problem of fake news—ways to mitigate its spread, to teach students to find factually accurate information, and to go beyond fact-checking. Contributors Mark Andrejevic, Benjamin Burroughs, Nicholas Bowman, Mark Brewin, Elizabeth Cohen, Colin Doty, Dan Faltesek, Johan Farkas, Cherian George, Tarleton Gillespie, Dawn R. Gilpin, Gina Giotta, Theodore Glasser, Amanda Ann Klein, Paul Levinson, Adrienne Massanari, Sophia A. McClennen, Kembrew McLeod, Panagiotis Takis Metaxas, Paul Mihailidis, Benjamin Peters, Whitney Phillips, Victor Pickard, Danielle Polage, Stephanie Ricker Schulte, Leslie-Jean Thornton, Anita Varma, Claire Wardle, Melissa Zimdars, Sheng Zou

This volume examines the phenomenon of fake news by bringing together leading experts from different fields within psychology and related areas, and explores what has become a prominent feature of public discourse since the first Brexit referendum and the 2016 US election campaign. Dealing with misinformation is important in many areas of daily life, including politics, the marketplace, health communication, journalism, education, and science. In a general climate where facts and misinformation blur, and are intentionally blurred, this book asks what determines whether people accept and share (mis)information, and what can be done to counter misinformation? All three of these aspects need to be understood in the context of online social networks, which have fundamentally changed the way information is produced, consumed, and transmitted. The contributions within this volume summarize the most up-to-date empirical findings, theories, and applications and discuss cutting-edge ideas and future directions of interventions to counter fake news. Also providing guidance on how to handle misinformation in an age of “alternative facts”, this is a fascinating and vital reading for students and academics in psychology, communication, and political science and for professionals including policy makers and journalists.

This book begins with a brief introduction to social computing and a review of classic graph theory and game theory. It then examines the data used to construct social networks, focusing on emerging Web 2.0 technologies and social networking websites, such as Facebook and MySpace. The book also explores data mining for social network extraction and analysis, presenting link and graph mining algorithms, such as subgraph discovery and clustering. In the last section, the authors provide case studies to illustrate concepts and principles as well as to demonstrate how to integrate components in order to solve real-world problems.

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. The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news. The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research.