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/

This book focuses on novel and state-of-the-art scientific work in the area of detection and prediction techniques using information found generally in graphs and particularly in social networks. Community detection techniques are presented in diverse contexts and for different applications while prediction methods for structured and unstructured data are applied to a variety of fields such as financial systems, security forums, and social networks. The rest of the book focuses on graph-based techniques for data analysis such as graph clustering and edge sampling. The research presented in this volume was selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. This book will appeal to practitioners, researchers and students in the field.

This book analyses the ways in which twenty-first century detective fiction provides an understanding of the increasingly complex and often baffling contemporary world — and what sociology, as a discipline, can learn from it. Conventional sociological accounts of fiction generally comprehend its value in terms of the ways in which it can illustrate, enlarge or help to articulate a particular social theory. Evans, Moore, and Johnstone suggest a different approach, and demonstrate that by taking a group of detective novels, we can unveil so far unidentified, but crucial, theoretical ideas about what it means to be an individual in the twenty-first century. More specifically, the authors argue that detective fiction of the last forty years illuminates the effects of urban isolation and separation, the invisibility of institutional power, financial insecurity, and the failure of public authorities to protect people. In doing so, this body of fiction traces out the fault-lines in our social arrangements, rehearses our collective fears, and captures a mood of restless disquiet. By engaging with detective stories in this way, the book revisits ideas about the promise and purpose of sociology.​

This book is an accessible introduction to the study of detecting fake news on social media. 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. 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. This is 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.

This book provides solid, state-of-the-art contributions from both scientists and practitioners working on botnet detection and analysis, including botnet economics. It presents original theoretical and empirical chapters dealing with both offensive and defensive aspects in this field. Chapters address fundamental theory, current trends and techniques for evading detection, as well as practical experiences concerning detection and defensive strategies for the botnet ecosystem, and include surveys, simulations, practical results, and case studies.

Social anxiety disorder is persistent fear of (or anxiety about) one or more social situations that is out of proportion to the actual threat posed by the situation and can be severely detrimental to quality of life. Only a minority of people with social anxiety disorder receive help. Effective treatments do exist and this book aims to increase identification and assessment to encourage more people to access interventions. Covers adults, children and young people and compares the effects of pharmacological and psychological interventions. Commissioned by the National Institute for Health and Clinical Excellence (NICE). The CD-ROM contains all of the evidence on which the recommendations are based, presented as profile tables (that analyse quality of data) and forest plots (plus, info on using/interpreting forest plots). This material is not available in print anywhere else.

These proceedings represent the work of contributors to the 11th European Conference on Social Media (ECSM 2024), hosted by the University of Brighton, UK on 30-31 May 2024. The Conference and Programme Chair is Dr Panagiotis Fotaris from the University of Brighton. ECSM is now a well-established event on the academic research calendar and now in its 11th year the key aim remains the opportunity for participants to share ideas and meet the people who hold them. The scope of papers will ensure an interesting two days. The subjects covered illustrate the wide range of topics that fall into this important and ever-growing area of research.