Journal of Social Computing

Inaugural Message from Editors-in-Chief

On behalf of the Editorial Board, it is our privilege to present the first issue of the Journal of Social Computing, affectionately shortened JoSoCo.

computing concerns the intersection of social behavior and computational systems. Historically focused on recreating human social conventions and contexts through software and technology, we propose its expansion to the full interface between social interaction and computation.

JoSoCo features social computing work that integrates social data mining, predictive modeling, machine augmentation, and social scientific theorizing. Computational models and machines, which are built to enhance the social world, are typically instantiated with social behavior embedded in data. These models predict and simulate that data recreate environments with social institutions, such as rating systems that convey reputation and quality. Alternatively, researchers may seek to create new machines, platforms or predictions designed to disrupt, complement or short circuit, rather than substitute for existing behavior signals, thereby facilitating novel environments and self-discovery.

Some authors claim that big data represents the end of theory, but we argue that social theory constitutes a critical interface for researchers who seek to obtain new social computing knowledge and know-how. Big social data from the web and distributed sensors can be used to measure variables from theoretical models to test hypotheses. Confirmations strengthen theory; violations provoke change. With big social data, theories can begin with weak assumptions; mining social data provides signals for their development to build strong insights, as argued in James’ paper “Social Computing Unhinged” from this issue.

Conversely, social science theories provide guidance and expectations for mining big social data. Theoretically informed qualitative and quantitative social research—such as systematic observation and population-sampled surveys—can de-bias results from data obtained by convenience. Furthermore, social science theories inspire the expressive capacity and predictive power of models and machines—such as crowdsourcing environments and recommendation systems—that we build and extend with them. Models that tightly fit with data and machines or platforms that increase predicted interactions or generate social values suggest high-order confirmations of the hunches and hypotheses that inspired them. In Fig. 1, the arrows suggest dynamic feedback between new social computing platforms and predictive models that generate new social worlds with new interaction behaviors, which, in turn, inspire new evolutions of social theory, with an improved predictive power capable of generating human goods and values.

In expanding and, indeed, unhinging the definition of social computing, we further welcome and seek to catalyze work that brings social theory into conversation with computational theory, social models into conversation with computational models, and social data into conversation with computational networks and interactions, in the service of understanding, creating, and computing social goods; flourishing; and innovation.

JoSoCo is an open access, peer-reviewed scholarly journal that aims to publish high-quality, original research, which pushes the boundaries of thinking, findings, and designs at the dynamic interface of social interaction and computation, not only including research on traditional social computing and computer supported cooperative work but also computational social sciencethe use of computation to learn from the explosion of social data becoming available today; socially intelligent computing or the analysis of how dynamic, evolving social collectives constitute emergent computers to solve their own problems; socially inspired computer science, human computer interaction, and humancentered computing whereby machines and persons recursively combine to generate unique knowledge and collective intelligence.

James Evans

is Professor of Sociology, Faculty Director of Computational Social Science and Director of Knowledge Lab at the University of Chicago and the Santa Fe Institute. His research uses large-scale data, machine learning and generative models to understand how collectives think and what they know, with a special focus on innovation in science, technology, ideology, and culture.

Jar-Der Luo

is a professor at Department of Sociology, Tsinghua University, president of Chinese Network for Social Network Studies, and chairman of Tsinghua Social Network Research Center. He received the PhD degree from State University of New York at Stony Brook in 1993. He researches numerous topics in social network studies including social capital, trust, social network analysis in big data, self-organization process, and Chinese indigenous management researches, such as guanxi and guanxi circle.

Xiaoming Fu

received the PhD degree in computer science from Tsinghua University, Beijing, China in 2000. He was then a research staff at Technical University of Berlin until joining the University of Gottingen, Germany in 2002, where he has been a professor in computer science and heading the computer networks group since 2007. He has spent research visits at Cambridge, Columbia, UCLA, Tsinghua University, Uppsala, and UPMC, and is an IEEE senior member and distinguished lecturer. His research interests include Internet-based systems, applications, and social networks. He is currently an editorial board member of IEEE Communications Magazine, IEEE Transactions on Network and Service Management, Elsevier Computer Networks, and Computer Communications, and has published over 150 peer-reviewed papers in renowned journals and international conference proceedings.