Research Themes

The core objective of SOCIAM is to establish the knowledge and skills required to design and deploy social machines that are sustainable and effective. The research is complex, as the 'components' of social machines are both human and technological.

Therefore, the core objective is decomposed into six themes, each of which has its own narrower set of objectives. Each of our projects is associated with one or more of the themes.

This theme focuses on the theory, infrastructure and tools necessary to support social computation.

On the engineering side, this involves extending conventional algorithmic components with novel elements specific to social computation. On the social side, it involves relating human interaction to computational interaction in social architectures.

Data is essential to any social machine. It may originate from humans or machines, individuals or groups, government or private companies, consumers or producers. It can be medical records or transport behaviour, crime reports or Tweet hash tags, or a combination thereof.

The Data Curation theme explores new means to repurpose, annotate, structure, generate and discover data on the Web, so as to turn the deluge of data into useable information.

This theme researches how and why trust in data, processes, and participants is established or breaks down on the web. The aim is to understand, at all stages of development, the requirements for data, processes and users to be transparent and accountable.

We need ways to ensure that appropriate levels of privacy are available with data having different privacy policies associated with it, and we must understand how to establish and associate trust or accountability in data and web-scale problem-solving.

The objective of this theme is to design interaction models that support users' defining, requesting and coordinating computation in social machines.

In engineering terms, this involves development of interfaces/workflows as well as of evaluation methods that allows validation across various social machines. In social terms, it involves supporting lightweight, "natural" interaction with social machines that enables people to engage with information in ways that support creativity and discovery.


The objective of this theme is to build social machines that embody the insights from our theoretical work, and to feed back practical results to work in other themes.

The theme is particularly focused on the areas of Health Care, Transport and Policing, where the UK is in a unique position to explore social machines that mix open and private, national and individual data sets.

This theme monitors, classifies, and models social machines as they evolve. The purpose is to support design of new social machines as well as provide an early warning facility for new disruptive social machines on the Web.

The theme involves data collection from case studies, analysis of data, simulation, and constructing an environment that support extensions of existing machines and development of new ones.

Longitudinal data is essential to understand how social machines grow once launched, reach tipping points, and whether they coalesce into larger machines or fragment into micro machines that still have utility.