Social information access is defined as a stream of research that explores methods for organizing the past interactions of users in a community in order to provide future users with better access to information. Social information access covers a wide range of different technologies and strategies that operate on a different scale, which can range from a small closed corpus site to the whole Web. The 16 chapters included in this book provide a broad overview of modern research on social information access. In order to provide a balanced coverage, these chapters are organized by the main types of information access (i.e., social search, social navigation, and recommendation) and main sources of social information.

Much applied research takes place as if complex social problems--and evaluations of interventions to address them--can be dealt with in a purely technical way. In contrast, this groundbreaking book offers an alternative approach that incorporates sustained, systematic reflection about researchers' values, what values research promotes, how decisions about what to value are made and by whom, and how judging the value of social interventions takes place. The authors offer practical and conceptual guidance to help researchers engage meaningfully with value conflicts and refine their capacity to engage in deliberative argumentation. Pedagogical features include a detailed evaluation case, "Bridge to Practice" exercises and annotated resources in most chapters, and an end-of-book glossary.

This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.

"This book offers insightful articles on the most salient contemporary issues of managing social and human aspects of information security"--Provided by publisher.

This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages.

This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.

Nonconscious Social Information Processing presents a research program concerned with the processing of social information. It cannot be considered a typical social psychological research program, however, because it is not aimed at explaining any specific social psychological phenomena, nor are the cognitive processes studied specific to the processing of social information. The program explores complex or ""high level"" processing of information that is not mediated by conscious awareness, and social cognition seems to be an appropriate area in which to investigate this kind of processing. The research program began with observations which suggest that nonconscious acquisition and processing of information play a major role in human development and adjustment. The first two chapters discuss these observations and present preliminary theoretical assumptions. The subsequent chapters contain reports of 34 experiments on nonconscious information processing. The book is addressed not only to personality and social psychologists, but also to cognitive psychologists concerned with information processing in general. The former may find this research relevant because most of the experiments describe some mechanisms of acquisition and utilization of social information—problems they are working on themselves. The latter may want to ignore the specific stimulus material (i.e., social information) employed in most of the experiments and focus on the general nature of the cognitive mechanisms studied.

This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services. Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2016), which was held in August 2016. The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field.

This book is showcases recent advances in knowledge discovery enhanced with semantic and social information. It includes eight chapters that grew out of joint workshops at ECML/PKDD 2007. The contributions emphasize the vision of the Web as a social medium.