Skip to main content
SpringerLink
Log in

A Taxonomic Framework for Social Machines

  • Chapter
  • First Online:
Social Collective Intelligence

Part of the book series: Computational Social Sciences ((CSS))

Abstract

Within the context of the World Wide Web, we have witnessed the emergence of a rich range of technologies that support both collaboration and distributed processing. Applications such as Wikipedia, for instance, have demonstrated the power and potential of the Web to facilitate the pooling of geographically dispersed knowledge assets. The result has been the creation of the world’s largest online encyclopedia, available for free in more than 200 languages for everyone to access and use.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.galaxyzoo.org/.

  2. 2.

    http://www.ushahidi.com/.

  3. 3.

    See http://sociam.org/.

  4. 4.

    http://www.youtube.com/.

  5. 5.

    http://www.flickr.com/.

  6. 6.

    http://www.alexa.com/.

  7. 7.

    For an overview, see http://en.wikipedia.org/wiki/Wikipedia:Bots/Status, retrieved in December 2013.

  8. 8.

    http://picbreeder.org/.

  9. 9.

    The notion of a Web-extended mind draws its inspiration from work that goes under a variety of headings, such as ‘extended cognition’, ‘cognitive extension’ or ‘the extended mind’ [8, 9, 28]. Smart [44] defines a Web-extended mind as a system in which some of the informational and technological elements of the Web can be seen to constitute part of the material supervenience base for (at least some of) a human individual’s mental states and processes.

  10. 10.

    The use of the term ‘mechanistic realization’ in the definition is intended to highlight the importance of this mechanistically-oriented explanatory account [59].

  11. 11.

    Similarly, it is the complementary nature of biological and non-biological resources (in terms of their contrasting representational and computational capabilities) that is often seen as lying at the root of the advanced forms of intelligence exhibited by extended cognitive systems. Sutton [49], for example, writes that “in extended cognitive systems, external states and processes need not mimic or replicate the formats, dynamics, or functions of inner states and processes. Rather, different components of the overall (enduring or temporary) system can play quite different roles and have different properties while coupling in collective and complementary contributions to flexible thinking and acting” (p. 194).

  12. 12.

    Clocks may provide one example of a social machine that is independent of the Web. In their book, ‘Anti-Oedipus’, Gilles and Guattari [16] suggest that clocks are a form of ‘social machine’: “The same machine can be both technical and social, but only when viewed from different perspectives: for example, the clock as a technical machine for measuring uniform time, and as a social machine for reproducing canonic hours and for assuring order in the city” (p. 155). Interestingly, clocks have been seen as providing the technological impetus for the transformation of society. A number of theorists have emphasized the way in which clocks enable the large-scale scheduling and coordination of both individual and collective action, and the way in which the transition from fixed, centralized clock towers to portable wristwatches paved the way for new forms of social interaction and engagement [23]. The invention of portable time-keeping devices, argues Landes [23], made it possible to organize and synchronize activities in a way that had never been possible before, and on the back of this new capability there emerged a new social and economic era. The clock, in this case, can be seen as the technological element of a social machine in the sense that it is influencing social interaction via the delivery of machine-generated temporal representations. These representations serve to structure, sculpt and scaffold forms of social interaction and engagement that progressively shape the contours of the social, economic and cultural landscapes in which we live.

  13. 13.

    https://www.linkedin.com/.

  14. 14.

    http://www.google.com/recaptcha.

  15. 15.

    https://myspace.com/.

  16. 16.

    http://www.reddit.com/.

  17. 17.

    https://www.mturk.com/mturk/.

  18. 18.

    The Web site of the SOCIAM research project lists a large number of additional examples of social machines—see http://www.sociam.org/social-machines.

  19. 19.

    We are grateful to Ségolène Tarte (University of Oxford) for pointing this out.

  20. 20.

    https://diasporafoundation.org/.

  21. 21.

    This corresponds to the tier termed ‘Causes/Groups’ in Fig. 1, which builds on a selection of Web-based systems that, through their large-scale user bases and general character, have reached a level of popularity that turns them into frameworks for the development of more special purpose social machines.

  22. 22.

    It is also possible to imagine one or more social machines being ‘incorporated’ into a larger social machine. In the same way, perhaps, as the neurological subsystems associated with memory, attention and perception merge to form part of the integrated mechanistic substrate that realizes more ‘macrocognitive’ functions such as sensemaking (see [21]).

  23. 23.

    In fact, as we mentioned in Sect. 3, the technological subsystem is only considered to be one part of the social machine; the human participants are also deemed to be part of the social machine.

  24. 24.

    https://www.linkedin.com/.

  25. 25.

    http://www.planethunters.org/.

  26. 26.

    See http://gigi.cpsc.ucalgary.ca/.

  27. 27.

    https://www.zooniverse.org/.

  28. 28.

    The constructs identified in the context of the repertory grid exercise ultimately drive the generation of dimensions associated with the taxonomic framework. A construct such as ‘Heterotelic vs. Autotelic Usage’ (see Sect. 4.1), for example, is ultimately used as the basis for the ‘Motivation Type’ and ‘Form of Motivation’ dimensions listed in Table 2.

  29. 29.

    Note that although two dimensions may be similar, they are only regarded as identical if the set of characteristics associated with the dimensions is the same in each case. In the absence of shared characteristics, a dimension mapping is regarded as ‘partial’.

  30. 30.

    Such consistency is evidenced by the way social machines are described in a number of papers. We thus encounter descriptions of social machines as “purposefully designed sociotechnical system[s] comprising machines and people” [10], as systems in which “the human and digital parts…[form] a machine in which the two aspects are seamlessly interwoven” [43], and as systems that involve “the co-constitutional involvement of humans and technologies” [50].

  31. 31.

    Note that in the light of our definition, the ‘engineering’ of a social machine entails more than just software development and deployment; it also includes the assembly of mechanisms that enable and encourage user engagement.

  32. 32.

    The reliability of the framework is indicated by inter-rater reliability metrics. Poor measures of inter-rater reliability may indicate that some dimensions are more difficult to interpret, understand or discern than others. This may call for the dimension to be refined or removed from the framework.

  33. 33.

    The process of identifying categories or classes of social machines is supported by the use of statistical methods that are applied to the social machine morphospace. Cluster analytic techniques are typically used to support these analyses (see Geiger et al. [14] for an example of such techniques applied to crowdsourcing systems).

References

  1. Ali-Hassan, H., Nevo, D.: Identifying social computing dimensions: A multidimensional scaling study. In: Intelligent Conference on Information Systems. Phoenix, Arizona (2009)

    Google Scholar 

  2. Bailey, K.D.: Typologies and Taxonomies: An Introduction to Classification Techniques. Sage, Thousand Oaks (1994)

    Google Scholar 

  3. Berners-Lee, T., Fischetti, M.: Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web. Harper Collins, New York (1999)

    Google Scholar 

  4. Bernstein, A., Klein, M., Malone, T.W.: Programming the global brain. Commun. ACM 55(5), 41–43 (2012)

    Article  Google Scholar 

  5. Bonabeau, E.: Decisions 2.0: The power of collective intelligence. MIT Sloan Manag. Rev. 50(2), 45–52 (2009)

    Google Scholar 

  6. Caputi, P., Reddy, P.: A comparison of triadic and dyadic methods of personal construct elicitation. J. Constr. Psychol. 12(3), 253–264 (1999)

    Google Scholar 

  7. Chi, E.H., Bernstein, M.S.: Leveraging online populations for crowdsourcing. IEEE Internet Comput. 16(5), 10–12 (2012)

    Article  Google Scholar 

  8. Clark, A.: Supersizing the Mind: Embodiment, Action, and Cognitive Extension. Oxford University Press, New York (2008)

    Book  Google Scholar 

  9. Clark, A., Chalmers, D.: The extended mind. Analysis 58(1), 7–19 (1998)

    Article  Google Scholar 

  10. De Roure, D., Hooper, C., Meredith-Lobay, M., Page, K., Tarte, S., Cruickshank, D., De Roure,C.: Observing social machines Part 1: What to observe? In: WWW2013 Workshop: The Theory & Practice of Social Machines. Rio de Janeiro, Brazil (2013)

    Google Scholar 

  11. Doan, A., Ramakrishnan, R., Halevy, A.Y.: Crowdsourcing systems on the World Wide Web. Commun. ACM 54(4), 86–96 (2011)

    Article  Google Scholar 

  12. Evans, M.B., O’Hara, K., Tiropanis, T., Webber, C.: Crime applications and social machines: Crowdsourcing sensitive data. In: WWW2013 Workshop: The Theory & Practice of Social Machines. Rio de Janeiro, Brazil (2013)

    Google Scholar 

  13. Fransella, F., Bell, R., Bannister, D.: A Manual for Repertory Grid Technique, 2nd edn. Wiley, Chichester (2003)

    Google Scholar 

  14. Geiger, D., Seedorf, S., Schulze, T., Nickerson, R.C., Schader, M.: Managing the crowd: Towards a taxonomy of crowdsourcing processes. In: Americas Conference on Information Systems. Detroit, Michigan (2011)

    Google Scholar 

  15. Gilbert, N., Troitzsch, K.G.: Simulation for the Social Scientist, 2nd edn. Open University Press, Maidenhead (2005)

    Google Scholar 

  16. Gilles, D., Guattari, F.: Anti-Oedipus. Continuum, London (2004)

    Google Scholar 

  17. Hall, W., Tiropanis, T.: Web evolution and web science. Comput. Netw. 56, 3859–3865 (2012)

    Article  Google Scholar 

  18. Hendler, J., Berners-Lee, T.: From the Semantic Web to social machines: A research challenge for AI on the World Wide Web. Artif. Intell. 174, 156–161 (2010)

    Article  MathSciNet  Google Scholar 

  19. Jankowicz, D.: The Easy Guide to Repertory Grids. Wiley, Chichester (2003)

    Google Scholar 

  20. Kelly, G.A.: The Psychology of Personal Constructs. W.W. Norton and Company, New York (1955)

    Google Scholar 

  21. Klein, G., Moon, B., Hoffman, R.R.: Making sense of sensemaking 2: A macrocognitive model. Intell. Syst. 21(5), 88–92 (2006)

    Article  Google Scholar 

  22. Kraut, R., Maher, M.L., Olson, J., Malone, T.W., Pirolli, P., Thomas, J.C.: Scientific foundations: A case for technology-mediated social-participation theory. Computer 43(11), 22–28 (2010)

    Article  Google Scholar 

  23. Landes, D.: Revolution in Time: Clocks and the Making of the Modern World. Viking Press, London (2000)

    Google Scholar 

  24. Law, E., von Ahn, L.: Human computation. Synth. Lect. Artif. Intell. Mach. Learn. 5(3), 1–121 (2011)

    Article  Google Scholar 

  25. Malone, T.W., Laubacher, R., Dellarocas, C.: The collective intelligence genome. MIT Sloan Manag. Rev. 51(3), 21–31 (2010)

    Google Scholar 

  26. McBride, N.: From social machine to social commodity: Redefining the concept of social machine as a precursor to new Web development approaches. In: 3rd International Conference on Web Science, Koblenz (2011)

    Google Scholar 

  27. Meira, S.R.L., Burégio, V.A.A., Nascimento, L.M., Figueiredo, E., Neto, M., Encarnação, B., Garcia, V.C.: The emerging web of social machines. In: 35th Annual Computer Software and Applications Conference (COMPSAC), pp. 26–27. IEEE, Munich (2011)

    Google Scholar 

  28. Menary, R.: The Extended Mind. MIT Press, Cambridge (2010)

    Book  Google Scholar 

  29. Nickerson, R., Muntermann, J., Varshney, U., Isaac, H.: Taxonomy development in information systems: Developing a taxonomy of mobile applications. In: European Conference in Information Systems, Verona (2009)

    Google Scholar 

  30. O’Hara, K.: Trust in social machines: The challenges. In: AISB/IACAP World Congress 2012: Social Computing, Social Cognition, Social Networks and Multiagent Systems, Birmingham (2012)

    Google Scholar 

  31. O’Hara, K.: Social machine politics are here to stay. IEEE Internet Comput. 17(2), 87–90 (2013)

    Google Scholar 

  32. Parameswaran, M., Whinston, A.B.: Research issues in social computing. J. Assoc. Inf. Syst. 8(6), 336–350 (2007)

    Google Scholar 

  33. Potthast, M., Stein, B., Gerling, R.: Automatic vandalism detection in Wikipedia. In: 30th European Conference on Information Retrieval Research. Glasgow, Scotland (2008)

    Google Scholar 

  34. Quinn, A., Bederson, B.: Human computation: A survey and taxonomy of a growing field. In: Annual Conference on Human Factors in Computing Systems (CHI’11). Vancouver, British Columbia (2011)

    Google Scholar 

  35. Rainie, L., Wellman, B.: Networked: The New Social Operating System. MIT Press, Cambridge (2012)

    Google Scholar 

  36. Raup, D.M.: Geometric analysis of shell coiling: General problems. J. Paleontol. 40, 1178–1190 (1966)

    Google Scholar 

  37. Richardson, D., Smart, P.R., Sycara, K., Stone, P., Giammanco, C., Powell, G.: Using ACT-R to model collective sensemaking in military coalition environments. In: Annual Fall Meeting of the International Technology Alliance. Palisades, New York (2013)

    Google Scholar 

  38. Robertson, D., Giunchiglia, F.: Programming the social computer. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 371(1987), 20120379 (2013)

    Google Scholar 

  39. Secretan, J.: Stigmergic dimensions of online creative interaction. Cogn. Syst. Res. 21, 65–74 (2013)

    Article  Google Scholar 

  40. Secretan, J., Beato, N., D’Ambrosio, D., Rodriguez, A., Campbell, A., Folsom-Kovarik, J., Stanley, K.: Picbreeder: A case study in collaborative evolutionary exploration of design space. Evol. Comput. 19(3), 373–403 (2011)

    Article  Google Scholar 

  41. Shadbolt, N.R.: Knowledge acquisition and the rise of social machines. Int. J. Hum. Comput. Stud. 71(2), 200–205 (2013)

    Article  Google Scholar 

  42. Shadbolt, N.R., Smart, P.R.: Knowledge elicitation: Methods, tools and techniques. In: Wilson, J.R., Sharples, S. (eds.) Evaluation of Human Work, 4th edn. CRC Press, Boca Raton (2015) (in press)

    Google Scholar 

  43. Shadbolt, N., Smith, D.A., Simperl, E., Van Kleek, M., Yang, Y., Hall, W.: Towards a classification framework for social machines. In: WWW2013 Workshop: The Theory & Practice of Social Machines, Rio de Janeiro (2013)

    Google Scholar 

  44. Smart, P.R.: The web-extended mind. Metaphilosophy 43(4), 426–445 (2012)

    Article  Google Scholar 

  45. Smart, P.R., Shadbolt, N.R.: Social machines. In: Khosrow-Pour, M. (ed.) Encyclopedia of Information Science and Technology. IGI Global, Hershey, 3rd edn. (2014)

    Google Scholar 

  46. Steiner, I.D.: Group Processes and Productivity. Academic, New York (1972)

    Google Scholar 

  47. Sun, R.: Cognitive social simulation incorporating cognitive architectures. Intell. Syst. 22(5), 33–39 (2007)

    Article  Google Scholar 

  48. Surowiecki, J.: The Wisdom of Crowds: Why the Many are Smarter than the Few. Random House, New York (2005)

    Google Scholar 

  49. Sutton, J.: Exograms and interdisciplinarity: History, the extended mind, and the civilizing process. In: Menary, R. (ed.) The Extended Mind. MIT Press, Cambridge (2010)

    Google Scholar 

  50. Tinati, R., Carr, L.: Understanding social machines. In: ASE/IEEE International Conference on Social Computing and International Conference on Privacy, Security, Risk and Trust, Amsterdam (2012)

    Google Scholar 

  51. Tiropanis, T., Hall, W., Shadbolt, N., De Roure, D., Contractor, N., Hendler, J.: The web science observatory. IEEE Intell. Syst. 28(2), 100–104 (2013)

    Google Scholar 

  52. Tong, S., Van Der Heide, B., Langwell, L., Walther, J.: Too much of a good thing? The relationship between number of friends and interpersonal impressions on Facebook. J. Comput. Mediat. Commun. 13(3), 531–549 (2008)

    Google Scholar 

  53. Tyszka, J.: Morphospace of foraminiferal shells: Results from the moving reference model. Lethaia 39(1), 1–12 (2006)

    Article  Google Scholar 

  54. Van Kleek, M., Smith, D.A., Hall, W., Shadbolt, N.R.: “The crowd keeps me in shape”: Social psychology and the present and future of health social machines. In: WWW2013 Workshop: The Theory & Practice of Social Machines, Rio de Janeiro, Brazil (2013)

    Google Scholar 

  55. von Ahn, L., Dabbish, L.: Designing games with a purpose. Comm. ACM 51(8), 58–67 (2008)

    Google Scholar 

  56. Vrandečić, D.: Ontology evaluation. In: Staab, S., Studer, R. (eds.) Handbook of Ontologies. International Handbook on Information Systems, 2nd edn., pp. 293–314. Springer, Berlin (2009)

    Google Scholar 

  57. Westerman, D., Spence, P., Van Der Heide, B.: A social network as information: The effect of system generated reports of connectedness on credibility on Twitter. Comput. Hum. Behav. 28, 199–206 (2012)

    Google Scholar 

  58. Wheeler, M., Clark, A.: Genic representation: Reconciling content and causal complexity. Br. J. Philos. Sci. 50(1), 103–135 (1999)

    Google Scholar 

  59. Wilson, R.A., Craver, C.F.: Realization: Metaphysical and scientific perspectives. In: Thagard, P. (ed.) Philosophy of Psychology and Cognitive Science. North-Holland, Oxford (2007)

    Google Scholar 

  60. Zook, M., Graham, M., Shelton, T., Gorman, S.: Volunteered geographic information and crowdsourcing disaster relief: A case study of the haitian earthquake. World Med. Health Policy 2(2), 7–33 (2010)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported under SOCIAM: The Theory and Practice of Social Machines. The SOCIAM Project is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EP/J017728/1 and comprises the Universities of Southampton, Oxford and Edinburgh.

Author information

Authors and Affiliations

  1. Electronics & Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Paul Smart, Elena Simperl & Nigel Shadbolt

Authors
  1. Paul Smart
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elena Simperl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nigel Shadbolt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paul Smart .

Editor information

Editors and Affiliations

  1. CREATE-NET, Trento, Italy

    Daniele Miorandi

  2. University of Trento, Trento, Italy

    Vincenzo Maltese

  3. School of Informatics Informatics, The University of Edinburgh, Edinburgh, United Kingdom

    Michael Rovatsos

  4. Faculty EEMCS, University of Twente, Enschede, The Netherlands

    Anton Nijholt

  5. The University of Edinburgh, Edinburgh, United Kingdom

    James Stewart

Appendix

Appendix

Tables 1, 2, 3, 4, 5, and 6 present the dimensions of the taxonomic framework for social machines.

Table 1 Mapping of social machine dimensions to the dimensions associated with four other systems (i.e., social computing, collective intelligence, human computation and crowdsourcing systems)
Full size table
Table 2 Dimensions and characteristics for the category ‘motivational factors and incentive mechanisms’
Full size table
Table 3 Dimensions and characteristics for the category ‘technology and engineering’
Full size table
Table 4 Dimensions and characteristics for the category ‘goal, task and process’
Full size table
Table 5 Dimensions and characteristics for the category ‘quality assessment’
Full size table
Table 6 Dimensions and characteristics for the category ‘participation and interaction’
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Smart, P., Simperl, E., Shadbolt, N. (2014). A Taxonomic Framework for Social Machines. In: Miorandi, D., Maltese, V., Rovatsos, M., Nijholt, A., Stewart, J. (eds) Social Collective Intelligence. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-08681-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08681-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08680-4

  • Online ISBN: 978-3-319-08681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation