Today I'll be presenting a paper at Web Science 2016, one that I wrote with some good folk who like myself have been involved in the construction of social machines. We wanted to reflect on our experiences to draw out overall lessons about factors that are relevant for social machine success and failure. Here's the abstract:

This paper frames social machines as problem solving entities, demonstrating how their ecosystems address multiple stakeholders’ problems. It enumerates aspects relevant to the theory and real-world practice of social machines, based on qualitative observations from our experiences building them. We frame evolving issues including: changing functionality, users, data and context; geographical and temporal scope (considering data granularity and visibility); and social scope. The latter is wide-ranging, including motivation, trust, experience, security, governance, control, provenance, privacy and law. We provide suggestions about building flexibility into social machines to allow for change, and defining social machines in terms of problems and stakeholders.

And here are the slides:

Social Machines in Practice: Solutions, Stakeholders and Scopes from Clare Hooper