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Networking online is here to stay. It's one of the quickest ways to connect with others who wish to be found. Just as with every form of communication, online social networking has its own rules and etiquette. This book informs readers on the ways to use networking sites properly and to their advantage, and how to avoid making common mistakes

While some social scientists may argue that we have always been networked, the increased visibility of networks today across economic, political, and social domains can hardly be disputed. Social networks fundamentally shape our lives and social network analysis has become a vibrant, interdisciplinary field of research. In this handbook, Ryan Light and James Moody have gathered 40 leading scholars in sociology, archaeology, economics, statistics, and information science, among others, to provide an overview of the theory, methods, and contributions in the field of social networks. Each of the chapters moves through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically.

"Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others"--

International audience ; To increase awareness about privacy threats, we have designed a tool, SONSAI, for Facebook users to audit their own profiles. SONSAI predicts values of sensitive attributes by machine learning and identifies user public attributes that have guided the learning algorithm towards these sensitive attribute values. Here, we present new aspects of the system such as the automatic combination of link disclosure attacks and attribute prediction. We explain how we defined sensitive subjects from a survey. We also show how the extended tool is fully interfaced with Facebook along different scenarios. In each case a dataset was built from real profiles collected in the user neighbourhood network. The whole analysis process is performed online, mostly automatically and with accuracy of 0.79 in AUC when inferring the political orientation.

International audience ; To increase awareness about privacy threats, we have designed a tool, SONSAI, for Facebook users to audit their own profiles. SONSAI predicts values of sensitive attributes by machine learning and identifies user public attributes that have guided the learning algorithm towards these sensitive attribute values. Here, we present new aspects of the system such as the automatic combination of link disclosure attacks and attribute prediction. We explain how we defined sensitive subjects from a survey. We also show how the extended tool is fully interfaced with Facebook along different scenarios. In each case a dataset was built from real profiles collected in the user neighbourhood network. The whole analysis process is performed online, mostly automatically and with accuracy of 0.79 in AUC when inferring the political orientation.

Based on multiple surveys, the present book gives valuable insights into the factors driving Social Network Site usage behavior for both practitioners and academics. By empirically evaluating multiple influence factors, it contributes to the current body of knowledge on Social Network Site usage behavior and provides multiple practical implications for Social Network Site service providers, advertisers, etc

1. Introduction -- 2. Theories of learning in online communities -- 3. Tools for online learning communities -- 4. Benefits and problems of online learning communities -- 5. Too much information -- 6. Feeling connected -- 7. In real time -- 8. Assessment for learning in online communities -- 9. Supporting online learning communities.

This book focuses on recent technical advancements and state-of-the art technologies for analyzing characteristic features and probabilistic modelling of complex social networks and decentralized online network architectures. Such research results in applications related to surveillance and privacy, fraud analysis, cyber forensics, propaganda campaigns, as well as for online social networks such as Facebook. The text illustrates the benefits of using advanced social network analysis methods through application case studies based on practical test results from synthetic and real-world data. This book will appeal to researchers and students working in these areas.

"Covers cutting edge research topics of utmost real-world importance in the specific domain of social networks. Addresses what leads to participation and effective collaboration in online communities. Presents studies from leading researchers and practitioners in the field."--Publisher's website