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With great potential benefit and possible harm, online social media platforms are transforming human society. Based on decades of deep exploration, distinguished scholar William Sims Bainbridge surveys our complex virtual society, harvesting insights about the future of our real world. Many pilot studies demonstrate valuable research methods and explanatory theories. Tracing membership interlocks between Facebook groups can chart the structure of a social movement, like the one devoted to future spaceflight development. Statistical data on the roles played by people in massively multiplayer online games illustrate the Silicon Law: information technology energizes both freedom and control, in a dynamic balance. The significance of open-source software suggests the traditional distinction between professional and amateur may fade, whereas web-based conflicts between religious and political groups imply that chasms are opening in civil society. This analysis of online space and the divergent communities is long overdue.

Digital Diversities is a groundbreaking interdisciplinary study of the social, social-psychological, philosophical and political ramifications of the 'digital turn' in human affairs. Focusing, in particular, on connections between the saturation of everyday life by digital communication technologies and 21st century global mobility, it offers fresh and original accounts of the interface between online communication practices and the negotiation of increasingly complex social experience. It

Cover -- Half Title -- Title -- Copyright -- Contents -- Funding -- Acknowledgments -- Foreword -- Introduction -- Section I Local and digital: the dyadic interaction of social and virtual -- 1 Place and space -- 2 Face-to-face and online communities -- 3 From global village to identity tribes -- Section II Social and spatial networks: the dyadic interaction of virtual and spatial -- 4 Network spillover -- 5 Social networks -- 6 Spatial analysis -- Section III Social networks online and offline: the dyadic interaction of social and spatial -- 7 Spatial and social media data -- 8 Online-offline coordination -- 9 The directionality of homophily -- Section IV Mapping online to offline social networks: bridging geography and geodesy -- 10 Network layouts by geodesy and geography -- 11 Methods in spatial statistics for social networks -- 12 An R package for spatializing social media -- Conclusion -- Index of names -- Index of subjects.

"The book outlines the theoretical and methodological challenges in analyzing social networks that overlap online and offline. It offers an introduction to concepts, theories, and methods that sit at the crossroads between spatial and social network analysis. It explores a range of networks and discusses methods and approaches to plot a social network graph onto a map. The book takes a non-mathematical approach to social networks and spatial statistics. A bespoke R package using igraph and network provides the online tools needed for mapping and graphing. Suitable for postgraduate students in sociology, psychology and the social sciences"--

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.

Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the maintenance of online social network "friends" is developed to demonstrate the applicability of artificial neural networks for virtual community research.

Social Media: Pedagogy and Practice examines how interactive technologies can be applied to teaching, research and the practice of communication. This book demonstrates how social media can be utilized in the classroom to build the skillsets of students going into journalism, public relations, integrated marketing, and other communications fields.

Online social networks have increasing influence on our society, they may play decisive roles in politics and can be crucial for the fate of companies. Such services compete with each other and some may even break down rapidly. Using social network datasets we show the main factors leading to such a dramatic collapse. At early stage mostly the loosely bound users disappear, later collective effects play the main role leading to cascading failures. We present a theory based on a generalised threshold model to explain the findings and show how the collapse time can be estimated in advance using the dynamics of the churning users. Our results shed light to possible mechanisms of instabilities in other competing social processes.

Online social media present unprecedented opportunities and challenges for a range of organizing processes such as information sharing, knowledge creation, collective action, and post‐disaster resource mobilization. Concepts and tools of network research can help highlight key aspects of online interaction. This editorial introduction frames the thematic issue along three themes of networked processes: identity and identification; interaction patterns in online communities; and challenges and cautionary notes concerning social media organizing. A diverse range of country contexts, as well as theoretical and methodological approaches illustrated in this issue, represent the multifaceted research that scholars can undertake to understand networked organizing on social media

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.