Detecting The Social

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.

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 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 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.​

eRisk stands for Early Risk Prediction on the Internet. It is concerned with the exploration of techniques for the early detection of mental health disorders which manifest in the way people write and communicate on the internet, in particular in user generated content (e.g. Facebook, Twitter, or other social media). Early detection technologies can be employed in several different areas but particularly in those related to health and safety. For instance, early alerts could be sent when the writing of a teenager starts showing increasing signs of depression, or when a social media user starts showing suicidal inclinations, or again when a potential offender starts publishing antisocial threats on a blog, forum or social network. eRisk has been the pioneer of a new interdisciplinary area of research that is potentially applicable to a wide variety of situations, problems and personal profiles. This book presents the best results of the first five years of the eRisk project which started in 2017 and developed into one of the most successful track of CLEF, the Conference and Lab of the Evaluation Forum.

This book provides a novel method for topic detection and classification in social networks. The book addresses several research and technical challenges that are currently being investigated by the research community, from the analysis of relations and communications between members of a community, to quality, authority, relevance and timeliness of the content, traffic prediction based on media consumption, spam detection, to security, privacy and protection of personal information. Furthermore, the book discusses innovative techniques to address those challenges and provides novel solutions based on information theory, sequence analysis and combinatorics, which are applied on real data obtained from Twitter.

This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edited work will appeal to researchers, practitioners and students interested in the latest developments of social network analysis.

This book presents the state-of-the-art in various aspects of analysis and mining of online social networks. Within the broader context of online social networks, it focuses on important and upcoming topics of social network analysis and mining such as the latest in sentiment trends research and a variety of techniques for community detection and analysis. The book collects chapters that are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2015), which was held in Paris, France in August 2015. All papers have been peer reviewed and checked carefully for overlap with the literature. The book will appeal to students and researchers in social network analysis/mining and machine learning.