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Towards a classification framework for social machines copy
Towards a classificationframework for social machinesSubmission at the SOCM2013 workshop @ WWW2013Elena Simperl, Max Van Kleek13 March 2013
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 asnecessary step towards a principled understanding of the science andengineering of such systems• Objectives of this work– Identify and define the constructs to describe, study and comparesocial machines– Achieve a shared understanding of basic notions and terminologythrough involvement from the broader community• Useful tool for both researchers in social and computer sciences and fordevelopers and operators of existing and future social machines2
Social machines and related areas• Social machines– Interaction amongalgorithmic and socialcomponents– Different notion ofcomputation (cf Turing) w.r.t.problem specification,performance, quality ofoutputs, termination– Incentives and motivation,network effects• Related areas– Computer science: CSCW,social computing, humancomputation– Organizationalmanagement/social sciences:wisdom of the crowds,collective intelligence, openinnovation, crowdsourcing3
The polyarchical relationship of social machines• Platforms/technologies vs social machines created for specificpurposes. 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 inWikipedia articles4
Challenges and research questions• What do specific instances of a social machine have incommon, and how do they differ dynamically?• How do certain design decisions taken at the level of theinfrastructure, frameworks and service propagate intonarrower-focused systems?• How do such decisions affect a broader ecosystem of socialmachines, each with their own, though overlapping,purposes and communities?5
Framework overview• Repertory grid elicitation to derive an initial set ofelements (instances of social machines) and constructs(characteristics of social machines) 10 grids, 56elements, 117 constructs• Consolidation and clustering of constructs 31 constructs,four clusters– Popularity– Tasks and purpose– Participants and roles– Motivation and incentives6
Constructs: purpose of the system and contributions• Purpose of the system, types of contributions, degree towhich these change7
Constructs: people, roles, motivation• Types of audience, autonomy and anonymity, roles and rolehierarchies• Intrinsic vs. extrinsic motivation, rewards8
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 assurance10
Next steps: community engagement and evaluation• Community engagement: building a social machine todefine the SOCIAM classification framework• Evaluation:– Task-independent using criteria from knowledgeengineering (completeness, correctness, readability,redundancy etc)– Task-dependent: Can the framework be used to describeexisting social machines?11