As people spend increasing proportions of their daily lives using social media, such as Twitter and Facebook, they are being invited to support myriad political causes by sharing, liking, endorsing, or downloading. Chain reactions caused by these tiny acts of participation form a growing part of collective action today, from neighborhood campaigns to global political movements. Political Turbulence reveals that, in fact, most attempts at collective action online do not succeed, but some give rise to huge mobilizations--even revolutions. Drawing on large-scale data generated from the Internet and real-world events, this book shows how mobilizations that succeed are unpredictable, unstable, and often unsustainable. To better understand this unruly new force in the political world, the authors use experiments that test how social media influence citizens deciding whether or not to participate. They show how different personality types react to social influences and identify which types of people are willing to participate at an early stage in a mobilization when there are few supporters or signals of viability. The authors argue that pluralism is the model of democracy that is emerging in the social media age--not the ordered, organized vision of early pluralists, but a chaotic, turbulent form of politics. This book demonstrates how data science and experimentation with social data can provide a methodological toolkit for understanding, shaping, and perhaps even predicting the outcomes of this democratic turbulence.

The two-volume set LNCS 10539 and 10540 constitutes the proceedings of the 9th International Conference on Social Informatics, SocInfo 2017, held in Oxford, UK, in September 2017. The 37 full papers and 43 poster papers presented in this volume were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections named: economics, science of success, and education; network science; news, misinformation, and collective sensemaking; opinions, behavior, and social media mining; proximity, location, mobility, and urban analytics; security, privacy, and trust; tools and methods; and health and behaviour.

The two-volume set LNCS 10539 and 10540 constitutes the proceedings of the 9th International Conference on Social Informatics, SocInfo 2017, held in Oxford, UK, in September 2017.The 37 full papers and 43 poster papers presented in this volume were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections named: economics, science of success, and education; network science; news, misinformation, and collective sensemaking; opinions, behavior, and social media mining; proximity, location, mobility, and urban analytics; security, privacy, and trust; tools and methods; and health and behaviour.

As people spend increasing proportions of their daily lives using social media, such as Twitter and Facebook, they are being invited to support myriad political causes by sharing, liking, endorsing, or downloading. Chain reactions caused by these tiny acts of participation form a growing part of collective action today, from neighborhood campaigns to global political movements. Political Turbulence reveals that, in fact, most attempts at collective action online do not succeed, but some give rise to huge mobilizations--even revolutions. Drawing on large-scale data generated from the Internet and real-world events, this book shows how mobilizations that succeed are unpredictable, unstable, and often unsustainable. To better understand this unruly new force in the political world, the authors use experiments that test how social media influence citizens deciding whether or not to participate. They show how different personality types react to social influences and identify which types of people are willing to participate at an early stage in a mobilization when there are few supporters or signals of viability. The authors argue that pluralism is the model of democracy that is emerging in the social media age--not the ordered, organized vision of early pluralists, but a chaotic, turbulent form of politics. This book demonstrates how data science and experimentation with social data can provide a methodological toolkit for understanding, shaping, and perhaps even predicting the outcomes of this democratic turbulence.

Self-organized, nano-scale structures appear on solid surfaces under ion beam irradiation with ion energies in the keV range. Within the last decade, surface engineering by ion beam sputtering (IBS) has become a very promising candidate for bottom-up production techniques of nano-devices. Morphologies like ripples, and regular arrays of dots, pyramids and pits as well as ultra-smooth surfaces have been obtained on a wide variety of substrates, including important semiconductor materials like Si, Ge, GaAs and InP. In spite of many substantial improvements of experimental surface structuring by IBS, the physical mechanisms underlying the pattern formation are still poorly understood. In this book we report the results obtained by usage of a Kinetic Monte Carlo simulations and continuum theory to study the physical mechanisms in atomic scale, which lead to pattern formation in general, as well as further possibilities to improve and fine-tune the properties of emerging patterns.