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Social Computing, Behavioral-Cultural Modeling and Prediction
This book constitutes the proceedings of the 6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013, held in Washington, DC, USA in April 2013. The total of 57 contributions, which consists of papers and posters, included in this volume was carefully reviewed and selected from 137 submissions. This conference is strongly committed to multidisciplinarity, consistent with recent trends in computational social science and related fields. The topics covered are: behavioral science, health sciences, military science and information science. There are also many papers that provide methodological innovation as well as new domain-specific findings.
E-Research
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Social Computing, Behavioral-Cultural Modeling and Prediction
This book constitutes the refereed proceedings of the 5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, held in College Park, MD, USA, in April 2012. The 43 revised papers presented in this volume were carefully reviewed and selected from 76 submissions. The papers cover a wide range of topics including economics, public health, and terrorist activities, as well as utilize a broad variety of methodologies, e.g., machine learning, cultural modeling and cognitive modeling.
Social, Cultural, and Behavioral Modeling
This book constitutes the proceedings of the 14th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2021, which was held online during July 6–9, 2021. The 32 full papers presented in this volume were carefully reviewed and selected from 56 submissions. The papers were organized in topical sections as follows: COVID-related focus; methodologies; social cybersecurity and social networks; and human and agent modeling. They represent a wide number of disciplines including computer science, psychology, sociology, communication science, public health, bioinformatics, political science, and organizational science. Numerous types of computational methods are used including, but not limited to, machine learning, language technology, social network analysis and visualization, agent-based simulation, and statistics.
Big Data, Little Data, No Data
An examination of the uses of data within a changing knowledge infrastructure, offering analysis and case studies from the sciences, social sciences, and humanities. “Big Data” is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data—because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, i...
Scientific Collaboration on the Internet
Modern science is increasingly collaborative, as signaled by rising numbers of coauthored papers, papers with international coauthors, and multi-investigator grants. Historically, scientific collaborations were carried out by scientists in the same physical location--the Manhattan Project of the 1940s, for example, involved thousands of scientists gathered on a remote plateau in Los Alamos, New Mexico. Today, information and communication technologies allow cooperation among scientists from far-flung institutions and different disciplines. Scientific Collaboration on the Internet provides both broad and in-depth views of how new technology is enabling novel kinds of science and engineering c...
Human-Centered Data Science
Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and present...
World Wide Research
Advances in information and communication technology are transforming the way scholarly research is conducted across all disciplines. The use of increasingly powerful and versatile computer-based and networked systems promises to change research activity as profoundly as the mobile phone, the Internet, and email have changed everyday life. This book offers a comprehensive and accessible view of the use of these new approaches-called "e-Research"--And their ethical, legal, and institutional implications. The contributors, leading scholars from a range of disciplines, focus on how e-Research is reshaping not only how research is done but also, and more important, its outcomes. By anchoring their discussion in specific examples and case studies, they identify and analyze a promising set of practical developments and results associated with e-Research innovations.
Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration
Mathematics is becoming increasingly collaborative, but software does not sufficiently support that: Social Web applications do not currently make mathematical knowledge accessible to automated agents that have a deeper understanding of mathematical structures. Such agents exist but focus on individual research tasks, such as authoring, publishing, peer-review, or verification, instead of complex collaboration workflows. This work effectively enables their integration by bridging the document-oriented perspective of mathematical authoring and publishing, and the network perspective of threaded discussions and Web information retrieval. This is achieved by giving existing representations of m...
