Advertisement

A Taxonomic Framework for Social Machines

  • Paul Smart
  • Elena Simperl
  • Nigel Shadbolt
Chapter
Part of the Computational Social Sciences book series (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.

Keywords

Repertory Grid Technological Element Birthday Party Sociotechnical System Citizen Science Project 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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.

References

  1. 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. 2.
    Bailey, K.D.: Typologies and Taxonomies: An Introduction to Classification Techniques. Sage, Thousand Oaks (1994)Google Scholar
  3. 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. 4.
    Bernstein, A., Klein, M., Malone, T.W.: Programming the global brain. Commun. ACM 55(5), 41–43 (2012)CrossRefGoogle Scholar
  5. 5.
    Bonabeau, E.: Decisions 2.0: The power of collective intelligence. MIT Sloan Manag. Rev. 50(2), 45–52 (2009)Google Scholar
  6. 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. 7.
    Chi, E.H., Bernstein, M.S.: Leveraging online populations for crowdsourcing. IEEE Internet Comput. 16(5), 10–12 (2012)CrossRefGoogle Scholar
  8. 8.
    Clark, A.: Supersizing the Mind: Embodiment, Action, and Cognitive Extension. Oxford University Press, New York (2008)CrossRefGoogle Scholar
  9. 9.
    Clark, A., Chalmers, D.: The extended mind. Analysis 58(1), 7–19 (1998)CrossRefGoogle Scholar
  10. 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. 11.
    Doan, A., Ramakrishnan, R., Halevy, A.Y.: Crowdsourcing systems on the World Wide Web. Commun. ACM 54(4), 86–96 (2011)CrossRefGoogle Scholar
  12. 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. 13.
    Fransella, F., Bell, R., Bannister, D.: A Manual for Repertory Grid Technique, 2nd edn. Wiley, Chichester (2003)Google Scholar
  14. 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. 15.
    Gilbert, N., Troitzsch, K.G.: Simulation for the Social Scientist, 2nd edn. Open University Press, Maidenhead (2005)Google Scholar
  16. 16.
    Gilles, D., Guattari, F.: Anti-Oedipus. Continuum, London (2004)Google Scholar
  17. 17.
    Hall, W., Tiropanis, T.: Web evolution and web science. Comput. Netw. 56, 3859–3865 (2012)CrossRefGoogle Scholar
  18. 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)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Jankowicz, D.: The Easy Guide to Repertory Grids. Wiley, Chichester (2003)Google Scholar
  20. 20.
    Kelly, G.A.: The Psychology of Personal Constructs. W.W. Norton and Company, New York (1955)Google Scholar
  21. 21.
    Klein, G., Moon, B., Hoffman, R.R.: Making sense of sensemaking 2: A macrocognitive model. Intell. Syst. 21(5), 88–92 (2006)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 23.
    Landes, D.: Revolution in Time: Clocks and the Making of the Modern World. Viking Press, London (2000)Google Scholar
  24. 24.
    Law, E., von Ahn, L.: Human computation. Synth. Lect. Artif. Intell. Mach. Learn. 5(3), 1–121 (2011)CrossRefGoogle Scholar
  25. 25.
    Malone, T.W., Laubacher, R., Dellarocas, C.: The collective intelligence genome. MIT Sloan Manag. Rev. 51(3), 21–31 (2010)Google Scholar
  26. 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. 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. 28.
    Menary, R.: The Extended Mind. MIT Press, Cambridge (2010)CrossRefGoogle Scholar
  29. 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. 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. 31.
    O’Hara, K.: Social machine politics are here to stay. IEEE Internet Comput. 17(2), 87–90 (2013)Google Scholar
  32. 32.
    Parameswaran, M., Whinston, A.B.: Research issues in social computing. J. Assoc. Inf. Syst. 8(6), 336–350 (2007)Google Scholar
  33. 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. 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. 35.
    Rainie, L., Wellman, B.: Networked: The New Social Operating System. MIT Press, Cambridge (2012)Google Scholar
  36. 36.
    Raup, D.M.: Geometric analysis of shell coiling: General problems. J. Paleontol. 40, 1178–1190 (1966)Google Scholar
  37. 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. 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. 39.
    Secretan, J.: Stigmergic dimensions of online creative interaction. Cogn. Syst. Res. 21, 65–74 (2013)CrossRefGoogle Scholar
  40. 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)CrossRefGoogle Scholar
  41. 41.
    Shadbolt, N.R.: Knowledge acquisition and the rise of social machines. Int. J. Hum. Comput. Stud. 71(2), 200–205 (2013)CrossRefGoogle Scholar
  42. 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. 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. 44.
    Smart, P.R.: The web-extended mind. Metaphilosophy 43(4), 426–445 (2012)CrossRefGoogle Scholar
  45. 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. 46.
    Steiner, I.D.: Group Processes and Productivity. Academic, New York (1972)Google Scholar
  47. 47.
    Sun, R.: Cognitive social simulation incorporating cognitive architectures. Intell. Syst. 22(5), 33–39 (2007)CrossRefGoogle Scholar
  48. 48.
    Surowiecki, J.: The Wisdom of Crowds: Why the Many are Smarter than the Few. Random House, New York (2005)Google Scholar
  49. 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. 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. 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. 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. 53.
    Tyszka, J.: Morphospace of foraminiferal shells: Results from the moving reference model. Lethaia 39(1), 1–12 (2006)CrossRefGoogle Scholar
  54. 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. 55.
    von Ahn, L., Dabbish, L.: Designing games with a purpose. Comm. ACM 51(8), 58–67 (2008)Google Scholar
  56. 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. 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. 58.
    Wheeler, M., Clark, A.: Genic representation: Reconciling content and causal complexity. Br. J. Philos. Sci. 50(1), 103–135 (1999)Google Scholar
  59. 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. 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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Electronics & Computer ScienceUniversity of SouthamptonSouthamptonUK

Personalised recommendations