Social Computing

“Social computing is the use of technology in networked communication systems by communities of people for one or more goals.”

In the spirit of social computing itself, the above definition was agreed upon by a consensus building process at the Social Computing Group & Transliteracies Project at the University of California. As with a Wikipedia article, ‘Social Computing’ definitions are malleable – they may change with time, with technology and its new application, within changing socio-cultural contexts, in sync with the society that defines them. While the term ‘social computing’ has more than one definition, and is constantly evolving in context and scope, it is useful to begin with the very roots of the concept:

Social computing begins with the observation that humans are profoundly social.

From birth humans orient to one another, and as they grow they develop abilities for interacting with one another ranging from expression and gesture to spoken and written language. Different fields of scientific enquiry, ranging from sociology to anthropology to philosophy have addressed the large issue of how these diverse interactions shape - and are shaped by – our perception of - knowledge of - reality. Technology – such as today’s computer tools - may be seen to act as an intermediary, as a moderator, as an enabler, as a tool. Fundamentally though, the process by which humans ‘make sense’ of the world, construct knowledge of the world around them and within them, is social in nature. This activity through which collective human actions organize knowledge has often been referred to as ‘Social Information Processing’ – It is the creation and processing of information into ‘knowledge’ by a group of people. One particular characteristic of this activity is the emergence of what has been termed as ‘collective intelligence’, from the information processing power of networked social systems. This ‘collective intelligence’ emphasizes that as a whole, the networked social system creates its own ‘knowledge’ in a way that no single isolated unit within it could, and proceeds to collectively act on it (Levy, 2001). Tribes of hunter-gatherers, nations, and modern corporations all act collectively with varying degrees of intelligence. And, from some perspectives, even collections of bacteria, bees, or baboons can also be viewed as collectively intelligent (Bloom 1995).

From this perspective, Social Computing research focuses on methods for harvesting the collective intelligence of groups of people (“wisdom of the crowd”) in order to realize greater value from the interaction between users and information. In recent times, the social potential of computer networks has come to the fore through the rapid development of such network applications as wikis, blogs, social networking sites, social bookmarking sites, and online collaborative editing suites that encourage people to engage in collective resource-building, action, and work. People are interacting more often and in more ways than ever before in human history (Malone 2004). These developments of the last decade have aroused tremendous interest in this field. Are we seeing merely an amplification, an acceleration of activities that have existed since time immemorial, or are we witnessing the birth of something completely new? Is this an ancient phenomenon now occurring in dramatically new forms, or is it something fundamentally different? As the underlying technologies continue to advance forward, it is more important than ever for us to understand collective intelligence at a deep level so we can create and take advantage of these new possibilities.

Research on Social Computing is by its nature, interdisciplinary. Along with the broad canvas of sociology, psychology, anthropology and cultural studies, social computing researchers have been studying the dynamics of interaction mediated typically by computer tools such as:

* Authoring tools: e.g., blogs

* Collaboration tools: e.g., wikis, in particular, e.g., Wikipedia

* Tagging systems (social bookmarking): e.g., del.icio.us, Flickr, CiteULike

* Social networking: e.g., Facebook, MySpace, Essembly

* Collaborative filtering: e.g., Digg, the Amazon Mechanical Turk, Yahoo answers

* Social Information Aggregation: e.g., scratchmysoul.com

While these tools and the ‘knowledge’ artifacts they have helped produce (large, complex encyclopedias to diverse, fine-grained folksonomies) are certainly impressive, it must be remembered that although computers are often used to facilitate networking and collaboration, they are not always required. For example the “Trictionary”, a collaboratively generated 400-page trilingual English/Spanish/Chinese translation wordbook, in 1982 was entirely paper and pen based, relying on neighborhood social networks and libraries. The creation of the Oxford English Dictionary in the 19th century was done largely with the help of anonymous volunteers organized by help wanted advertisements in newspapers and slips of paper sent through the postal mail. That having been said, it is notable that modern societies, with information and communication technologies, are vastly better at collective cognition than earlier ones. The degree of organization, and its precision, which we take for granted would have been astonishing for even the in- habitants of the most advanced societies of previous centuries. Historians have explored some of the technical and institutional underpinnings of these organizational revolutions (McNeill 1982; Beniger 1986; Yates 1989), but at a deeper level we have little idea of the mechanics, or why what we do works (when it does work!), and what role information and communication technologies play. On large scales, market economies, corporations and other bureaucracies, scientific disciplines, and democratic polities all have something of this collective information-processing character. Knowing how they accomplish this would be deeply rewarding, and, if that understanding can be used to make them work better, of profound economic and political importance. A frontal assault on this problem, as represented by one of those grand institutions, is unlikely to succeed (though, as Shalizi, 2007 points out- it may be a magnificent failure!) Fortunately, social information processing also occurs in much humbler institutions, such as tagging systems and collaborative filtering, where issues of data collection and even experimental manipulation are much more manageable, and where we might hope to learn more, before tackling the fundamental problems of social science.

The research focus of my thesis, therefore, is predominantly on the ‘social’ aspect of social computing, and on the interplay between social interactions and technology, rather than the technology alone.