Computational Approaches To Understanding Human Behavior From Online Social Interactions

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Computational Social Psychology showcases a new approach to social psychology that enables theorists and researchers to specify social psychological processes in terms of formal rules that can be implemented and tested using the power of high speed computing technology and sophisticated software. This approach allows for previously infeasible investigations of the multi-dimensional nature of human experience as it unfolds in accordance with different temporal patterns on different timescales. In effect, the computational approach represents a rediscovery of the themes and ambitions that launched the field over a century ago. The book brings together social psychologists with varying topical interests who are taking the lead in this redirection of the field. Many present formal models that are implemented in computer simulations to test basic assumptions and investigate the emergence of higher-order properties; others develop models to fit the real-time evolution of people’s inner states, overt behavior, and social interactions. Collectively, the contributions illustrate how the methods and tools of the computational approach can investigate, and transform, the diverse landscape of social psychology.

This SpringerBrief brings order to the wealth of research studies that contribute to shape our understanding of on-line social networks (OSNs) lurking phenomena. This brief also drives the development of computational approaches that can be effectively applied to answer questions related to lurking behaviors, as well as to the engagement of lurkers in OSNs. All large-scale online social networks (OSNs) are characterized by a participation inequality principle, i.e., the crowd of an OSN does not actively contribute, rather it takes on a silent role. Silent users are also referred to as lurkers, since they gain benefit from others' information without significantly giving back to the community. Nevertheless, lurkers acquire knowledge from the OSN, therefore a major goal is to encourage them to more actively participate. Lurking behavior analysis has been long studied in social science and human-computer interaction fields, but it has also matured over the last few years in social network analysis and mining. While the main target audience corresponds to computer, network, and web data scientists, this brief might also help increase the visibility of the topic by bridging different closely related research fields. Practitioners, researchers and students interested in social networks, web search, data mining, computational social science and human-computer interaction will also find this brief useful research material .

In the past two decades, the Internet has come to dominate every aspect of everyday life. This has been a huge change for many of us, and, for the younger generation - born into this situation - there has been no other way of living. How does this new way of life affect our health and happiness, our well-being? How does it affect our relationships, our friendships? Has the definition of friendship changed now that we have hundreds of friends on Facebook? Why is it that some people find it so hard to talk to people in their daily lives but find it so easy on the Internet? People spend so much time on the Internet - so what do we actually do on there? Why are some people so aggressive and others exceptionally helpful? Are these behaviors that we see from the same people offline? How do we take decisions online and which groups would we rather belong to online where nobody knows us, rather than revealing our true identity to the outside world? The new edition of 'The Social Net' provides a comprehensive understanding of the social aspects of the Internet. It contains chapters on topics such as identity manipulation, online romantic relationships, online decision making, the internet and aggression, and online prejudice and discrimination. The book provides the reader with an understanding of both the negative and positive influences of the net and is an exceptionally useful guide for for how to use the net to improve wellbeing. Today, when there is so much negative publicity surrounding the Internet - despite our reliance on it - this book provides a much needed balanced understanding of the Net and its influence.

For scientists, explanations of natural phenomenon based on optimality principles are critical tools for understanding the phenomena that shape the solutions the brain devises for the complex perceptual and motor problems of daily life. The neuroscientist David Marr called this type of analysis the "computational approach''. While the computational approach has been applied with a great deal of success to phenomena such as neural coding and human motor control, the success of the computational approach for studying interactive behavior, particularly social behavior, has been more modest. The purpose of this dissertation is threefold: to make the case for the study of social interaction from the computational perspective; to understand the challenges involved in this study and provide computational tools to address these challenges; and to apply the computational approach to the study of social behavior in the real world. Our principle contributions are : (1) developing a framework for both analyzing and synthesizing behaviors in continuous state, action, and time from the perspective of the intentions that these behaviors appear to be realizing (our approach is well-suited for many motor-control and social-interaction problems), and (2) carrying out two computational studies of early infant social behavior that shed light on the computational forces that shape development. Our empirical results provide a new view of early infant social behavior as intentional, with the surprising intention of maximizing time spent with mother smiling at infant and the infant not smiling herself.

This book provides a broad survey of advanced pattern recognition techniques for human behavior analysis. Clearly structured, the book begins with concise coverage of the major concepts, before introducing the most frequently used techniques and algorithms in detail, and then discussing examples of real applications. Features: contains contributions from an international selection of experts in the field; presents a thorough introduction to the fundamental topics of human behavior analysis; investigates methods for activity recognition, including gait and posture analysis, hand gesture analysis, and semantics of human behavior in image sequences; provides an accessible psychological treatise on social signals for the analysis of social behaviors; discusses voice and speech analysis, combined audiovisual cues, and social interactions and group dynamics; examines applications in different research fields; each chapter concludes with review questions, a summary of the topics covered, and a glossary.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors.

A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.