This document proposes a framework for classifying social machines. It defines social machines and related concepts. An initial set of constructs for describing social machines was developed through elicitation grids. The constructs cluster into four groups: popularity, tasks/purpose, participants/roles, and motivation/incentives. The framework is intended to facilitate understanding and comparison of social machines. Further refinement of constructs and community evaluation are needed to improve the framework.

1. Towards a classification
framework for social machines
Submission at the SOCM2013 workshop @ WWW2013
Elena Simperl, Max Van Kleek
13 March 2013

2. Motivation and objectives
• Future ICT systems as sophisticated assemblies of data-intensive,
complex automation and deep community involvement
• Defining social machines and their characteristic properties as
necessary step towards a principled understanding of the science and
engineering of such systems
• Objectives of this work
– Identify and define the constructs to describe, study and compare
social machines
– Achieve a shared understanding of basic notions and terminology
through involvement from the broader community
• Useful tool for both researchers in social and computer sciences and for
developers and operators of existing and future social machines
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3. Social machines and related areas
• Social machines
– Interaction among
algorithmic and social
components
– Different notion of
computation (cf Turing) w.r.t.
problem specification,
performance, quality of
outputs, termination
– Incentives and motivation,
network effects
• Related areas
– Computer science: CSCW,
social computing, human
computation
– Organizational
management/social sciences:
wisdom of the crowds,
collective intelligence, open
innovation, crowdsourcing
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4. The polyarchical relationship of social machines
• Platforms/technologies vs social machines created for specific
purposes. E.g., MediaWiki vs Wikipedia
• Broader vs narrower-scoped social machines. E.g., Twitter vs Obama’12
• Ecosystem of social machines. E.g., results from GalaxyZoo taken up in
Wikipedia articles
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5. Challenges and research questions
• What do specific instances of a social machine have in
common, and how do they differ dynamically?
• How do certain design decisions taken at the level of the
infrastructure, frameworks and service propagate into
narrower-focused systems?
• How do such decisions affect a broader ecosystem of social
machines, each with their own, though overlapping,
purposes and communities?
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6. Framework overview
• Repertory grid elicitation to derive an initial set of
elements (instances of social machines) and constructs
(characteristics of social machines)  10 grids, 56
elements, 117 constructs
• Consolidation and clustering of constructs  31 constructs,
four clusters
– Popularity
– Tasks and purpose
– Participants and roles
– Motivation and incentives
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7. Constructs: purpose of the system and contributions
• Purpose of the system, types of contributions, degree to
which these change
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8. Constructs: people, roles, motivation
• Types of audience, autonomy and anonymity, roles and role
hierarchies
• Intrinsic vs. extrinsic motivation, rewards
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9. Using the constructs
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10. Next steps: refine constructs
• Standard listings for types of contributions, actions, activities
• Relationship between roles, autonomy, and anonymity and motivators
• Motivation and incentives: participation in the definition of the overall purpose,
transparency of purpose
• Missing
– Nature of the good produced
– Existing social structures
– Interaction between algorithmic and social components, workflows
– Consolidation and aggregation of contributions, quality assurance
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11. Next steps: community engagement and evaluation
• Community engagement: building a social machine to
define the SOCIAM classification framework
• Evaluation:
– Task-independent using criteria from knowledge
engineering (completeness, correctness, readability,
redundancy etc)
– Task-dependent: Can the framework be used to describe
existing social machines?
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