Advertisement

Theory

  • Nigel Shadbolt
  • Kieron O’Hara
  • David De Roure
  • Wendy Hall
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

This chapter considers the theory of social machines, from three perspectives. First, it looks at social machines as social; second, as machines; third, it takes the perspective of the data that fuels the machines. Looking at the sociality of social machines, the chapter considers various approaches to developing meaningful narratives around the operation of social machines, including prosopography, wayfaring and study of information tokens across platforms in transcendental information cascades. The issues surrounding the feedback loops of reflexivity are considered, as are the need for diversity and the possibility of so-called Mandevillian intelligence, where the collective intelligence of the group is enhanced, not degraded, by the imperfect reasoning of its participants. Looking at the mechanical aspects of social machines, the chapter considers the use of a formal process language the Lightweight Social Calculus (LSC) to map out the potential interactions between participants and technology support. The specification of shadow institutions using LSC is described, as is the use of a simplified diagrammatic calculus called Sociograms to allow the design of LSC specifications. From the data perspective, the chapter looks at annotation and provenance. In particular, it maps out a provenance methodology for keeping records about where data have come from, over which data scientists can reason. The chapter concludes with two examples of the use of provenance to understand social machines, and two examples of the use of social machines to create provenance records.

Keywords

Annotation Citizen science CyberMadres Data Data citation Galaxy Zoo Green peas Lightweight Coordination Calculus (LCC) Lightweight Social Calculus (LSC) Mandevillian intelligence Narrative Open data Planet Hunters Pokémon Go! Prosopography PROV Provenance Reflexivity Retweeting Scholarly communication Shadow institutions Sociograms Transcendental information cascades Wayfaring Wikipedia Zooniverse 

References

  1. Ahmad S, Battle A, Malkani Z, Kamvar S (2011) The jabberwocky programming environment for structured social computing. In: Proceedings of the 24th annual ACM symposium on User Interface Software and Technology (UIST ’11). ACM, New York, pp 53–64.  https://doi.org/10.1145/2047196.2047203 CrossRefGoogle Scholar
  2. Ahmadian S, Azarshahi S, Paulus DL (2017) Explaining Donald Trump via communication style: grandiosity, informality and dynamism. Pers Individ Differ 107:49–53.  https://doi.org/10.1016/j.paid.2016.11.018 CrossRefGoogle Scholar
  3. von Ahn L (2006) Games with a purpose. Computer 39(6):92–94.  https://doi.org/10.1109/MC.2006.196 CrossRefGoogle Scholar
  4. Almirall E, Wareham J (2011) Living labs: arbiters of mid- and ground-level innovation. Tech Anal Strat Manag 23(1):87–102.  https://doi.org/10.1080/09537325.2011.537110 CrossRefGoogle Scholar
  5. Baldoni M, Baroglio C, Bergenti F, Marengo E, Mascardi V, Patti V, Ricci A, Santi A (2011) An interaction-oriented agent framework for open environments. In: Pirrone R, Sorbello F (eds) AI*IA 2011: artificial intelligence around man and beyond: Proceedings of the XIIth international conference of the Italian Association for Artificial Intelligence. Springer, Berlin, pp 68–79.  https://doi.org/10.1007/978-3-642-23954-0_9 CrossRefGoogle Scholar
  6. Baldoni M, Baroglio C, Marengo E, Patti V (2013) Constitutive and regulative specifications of commitment protocols: a decoupled approach. ACM T Intell Syst Technol 4(2):22.  https://doi.org/10.1145/2438653.2438657 CrossRefGoogle Scholar
  7. Barabási A-L (2005) The origin of bursts and heavy tails in human dynamics. Nature 435:201–211.  https://doi.org/10.1038/nature03459 CrossRefGoogle Scholar
  8. Barabási A-L (2010) Bursts: the hidden patterns behind everything we do, from your email to bloody crusades. Penguin, LondonGoogle Scholar
  9. Barowy DW, Curtsinger C, Berger ED, McGregor A (2012) AUTOMAN: a platform for integrating human-based and digital computation. In: Proceedings of the ACM international conference on Object Oriented Programming Systems Languages and Applications (OOPSLA ’12). ACM, New York, pp 639–654.  https://doi.org/10.1145/2384616.2384663 CrossRefGoogle Scholar
  10. Beck U, Giddens A, Lash S (1994) Reflexive modernization: politics, tradition and aesthetics in the modern social order. Polity Press, CambridgeGoogle Scholar
  11. Bennett D, Harvey A (2009) Publishing open government data, W3C. https://www.w3.org/TR/gov-data/
  12. Bourdieu P (1977) Outline of a theory of practice. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  13. Bourne PE, Clark T, Dale R, de Waard A, Herman I, Hovy E, Shotton D (2011) Force11 manifesto: improving future research communication and e-scholarship. https://www.force11.org/about/manifesto
  14. Boyd D, Golder S, Lotan G (2010) Tweet, tweet, retweet: conversational aspects of retweeting on Twitter. In: Proceedings of the 43rd Hawaii International Conference on System Sciences (HICSS ’10). ACM, New York, pp 1–10.  https://doi.org/10.1109/HICSS.2010.412 CrossRefGoogle Scholar
  15. Buneman P, Kostylev EV, Vansummeren S (2013) Annotations are relative. In: Proceedings of the 16th International Conference on Database Theory (ICDT ’13). ACM, New York, pp 177–188.  https://doi.org/10.1145/2448496.2448518 CrossRefGoogle Scholar
  16. Buneman P, Davidson S, Frew J (2016a) Why data citation is a computational problem. Commun ACM 59(9):50–57CrossRefGoogle Scholar
  17. Buneman P, Gascón A, Murray-Rust D (2016b) Composition and substitution in provenance and workflows. In: Proceedings of the 8th USENIX workshop on the Theory and Practice of Provenance (TaPP ’16). https://www.usenix.org/conference/tapp16/workshop-program/presentation/buneman
  18. Buneman P, Gascón A, Moreau L, Murray-Rust D (2017). Provenance composition in PROV. https://eprints.soton.ac.uk/408513/
  19. Cabac L, Knaak N, Moldt D, Rölke H (2006) Analysis of multi-agent interactions with process mining techniques. In: Fischer K, Timm IJ, André E, Zhong N (eds) Multiagent system technologies: proceedings of the 4th German conference MATES 2006. Springer, Berlin, pp 12–23.  https://doi.org/10.1007/11872283_2 CrossRefGoogle Scholar
  20. Candra MZC, Truong H-L, Dustdar S (2013) Provisioning quality-aware social compute units in the cloud. In: Basu S, Zhang CPL, Xiang F (eds) Service-oriented computing: proceedings of the 11th international conference, ICSOC 2013. Springer, Berlin, pp 313–327.  https://doi.org/10.1007/978-3-642-45005-1_22 CrossRefGoogle Scholar
  21. Cardamone C, Schawinski K, Sarzi M, Bamford SP, Bennert N, Urry CM, Lintott C, Keel WC, Parejko J, Nichol RC, Thomas D, Andreescu D, Murray P, Raddick MJ, Slosar A, Szalay A, VandenBerg J (2009) Galaxy Zoo green peas: discovery of a class of compact extremely star-forming galaxies. Mon Not R Astron Soc 399(3):1191–1205.  https://doi.org/10.1111/j.1365-2966.2009.15383.x CrossRefGoogle Scholar
  22. Cebrian M, Rahwan I, Pentland A (2016) Beyond viral. Commun ACM 59(4):36–39.  https://doi.org/10.1145/2818992 CrossRefGoogle Scholar
  23. Cheng J, Lada A, Alex Dow P, Kleinberg J, Leskovic J (2014) Can cascades be predicted? In: Proceedings of the 23rd international conference on World Wide Web. ACM, New York, pp 925–936.  https://doi.org/10.1145/2566486.2567997 CrossRefGoogle Scholar
  24. Cherry C (1973) Regulative rules and constitutive rules. Philos Q 23:301–315CrossRefGoogle Scholar
  25. Cisco (2017) The Zettabyte Era: trends and analysis, Cisco Solutions White Paper no. 1465272001812119. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/vni-hyperconnectivity-wp.html
  26. Corsar D, Baillie C, Markovic M, Edwards P, Nelson J, Velaga N, Beecroft M, Sripada S, Pan JZ, Papangelis K (2012) DEMO: a rural passenger information system utilising linked data and the crowd, Digital Futures 2012. http://www.dotrural.ac.uk/digitalfutures/sites/default/files/digitalfutures2012papers/Demos/Corsar_etal_IRP.pdf
  27. Corsar D, Edwards P, Nelson J, Papangelis K (2014) Mobile phones, sensors & the crowd: lessons learnt from development of a real-time travel information system. In: Proceedings of the first international conference on IoT in Urban Space (URB-IoT ’14). ACM, New York, pp 99–101.  https://doi.org/10.4108/icst.urb-iot.2014.257328 CrossRefGoogle Scholar
  28. d’Inverno M, Luck M, Noriega P, Rodriguez-Aguilar JA, Sierra C (2012) Communicating open systems. Artif Intell 186:38–94.  https://doi.org/10.1016/j.artint.2012.03.004 CrossRefGoogle Scholar
  29. De Certeau M (1984) The practice of everyday life. University of California Press, Berkeley, Los AngelesGoogle Scholar
  30. De Nies T, Coppens S, Verborgh R, Sande MV, Mannens E, Van der Walle R, Michaelides D, Moreau L (2013) Easy access to provenance: an essential step towards trust on the Web. In: Proceedings of the 2013 IEEE 37th annual computer software and applications conference workshops. IEEE, New York, pp 218–223.  https://doi.org/10.1109/COMPSACW.2013.29 CrossRefGoogle Scholar
  31. De Roure D (2014) The future of scholarly communications. Insight 27(3):233–238.  https://doi.org/10.1629/2048-7754.171 CrossRefGoogle Scholar
  32. De Roure D, Hooper C, Meredith-Lobay M, Page K, Tarte S, Cruickshank D, De Roure C (2013) Observing social machines part 1: what to observe? In: Proceedings of the 22nd international conference on World Wide Web (WWW ’13). ACM, New York, pp 901–904.  https://doi.org/10.1145/2487788.2488077 CrossRefGoogle Scholar
  33. De Roure D, Hendler JA, Huynh TD, James D, Moreau L, Nurmikko-Fuller T, Van Kleek M, Willcox P (2018) Pokémon Go! Through the lens of social machines, unpublished paperGoogle Scholar
  34. Drăgan L, Luczak-Roesch M, Simperl E, Berendt B, Moreau L (2014) Crowdsourcing data citation graphs using provenance. In: Workshop on Provenance Analytics (ProvAnalytics2014). https://www.researchgate.net/publication/281741501_Crowdsourcing_data_citation_graphs_using_provenance
  35. Drăgan L, Luczak-Roesch M, Simperl E, Packer H, Moreau L, Berendt B (2015) A-posteriori provenance-enabled linking of publications and datasets via crowdsourcing. D-Lib 21(1/2). http://www.dlib.org/dlib/january15/dragan/01dragan.html
  36. Dustdar S, Bhattacharya K (2011) The social compute unit. IEEE Internet Comput 15(3):64–69.  https://doi.org/10.1109/MIC.2011.68 CrossRefGoogle Scholar
  37. Ebden M, Huynh TD, Moreau L, Ramchurn S, Roberts S (2012) Network analysis on provenance graphs from a crowdsourcing application. In: Groth P, Frew J (eds) Provenance and annotation of data and processes: 4th International Provenance and Annotation Workshop, IPAW 2012, revised selected papers. Springer, Berlin, pp 168–182.  https://doi.org/10.1007/978-3-642-34222-6_13 CrossRefGoogle Scholar
  38. Fornaciari T, Poesio M (2014) Identifying fake Amazon reviews as learning from crowds. In: Proceedings of the 14th conference of the European chapter of the association for computational linguistics, association for computational linguistics. pp. 279–287.  https://doi.org/10.3115/v1/E14-1030
  39. Francia PL (2017) Free media and Twitter in the 2016 Presidential election, social science computer review.  https://doi.org/10.1177/0894439317730302 MathSciNetCrossRefGoogle Scholar
  40. Franklin MJ, Kossman D, Kraska T, Ramesh S, Xin R (2011) CrowdDB: answering queries with crowdsourcing. In: Proceedings of the 2011 ACM SIGMOD international conference on management of data (SIGMOD ’11). ACM, New York, pp 61–72.  https://doi.org/10.1145/1989323.1989331 CrossRefGoogle Scholar
  41. Gatterbauer W, Balazinska M, Khoussainova N, Suciu D (2009) Believe it or not: adding belief annotations to databases. Proc VLDB Endowment 2(1):1–12. https://arxiv.org/pdf/0912.5241 CrossRefGoogle Scholar
  42. Geerts F, Kementsietsidis A, Milano D (2006) MONDRIAN: annotating and querying databases through colors and blocks. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE ’06).  https://doi.org/10.1109/ICDE.2006.102
  43. Giddens A (1984) The constitution of society: outline of the theory of structuration. Polity Press, CambridgeGoogle Scholar
  44. Gil Y, Miles S (2013) PROV model primer. http://www.w3.org/TR/prov-primer/
  45. Goel S, Watts DJ, Goldstein DG (2012) The structure of online diffusion networks. In: Proceedings of the 13th ACM conference on Electronic Commerce (EC12). ACM, New York, pp 623–638.  https://doi.org/10.1145/2229012.2229058 CrossRefGoogle Scholar
  46. Greco J, Turri J (eds) (2012) Virtue epistemology: contemporary readings. MIT Press, Cambridge, MAGoogle Scholar
  47. Green TJ, Tannen V (2017) The semiring framework for database provenance. In: Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems (PODS ’17). ACM, New York, pp 93–99.  https://doi.org/10.1145/3034786.3056125 CrossRefGoogle Scholar
  48. Green TJ, Karvounarakis G, Tannen V (2007) Provenance semirings. In: Proceedings of the Twenty-Sixth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS ’02). ACM, New York, pp 31–40.  https://doi.org/10.1145/1265530.1265535 CrossRefGoogle Scholar
  49. Groth P, Moreau L (2013) PROV-overview: an overview of the PROV family of documents, W3C. http://www.w3.org/TR/prov-overview/
  50. Halfaker A, Kittur A, Riedl J (2011) Don’t bite the newbies: how reverts affect the quantity and quality of Wikipedia work. In: Proceedings of the 7th international symposium on Wikis and Open Collaboration (WikiSym ’11). ACM, New York, pp 163–172.  https://doi.org/10.1145/2038558.2038585 CrossRefGoogle Scholar
  51. Harris CG (2012) Detecting deceptive opinion spam using human computation. In: Proceedings of AAAI workshops at the 26th AAAI conference on artificial intelligence. https://www.aaai.org/ocs/index.php/WS/AAAIW12/paper/viewPaper/5256
  52. Hayek FA (1945) The use of knowledge in society. Am Econ Rev 35(4):519–530Google Scholar
  53. Hendler J, Holm J, Musialek C, Thomas G (2012) US government linked open data: semantic.data.gov. IEEE Intell Syst 27(3):25–31.  https://doi.org/10.1109/MIS.2012.27 CrossRefGoogle Scholar
  54. Hertwig R, Todd PM (2003) More is not always better: the benefits of cognitive limits. In: Hardman D, Macchi L (eds) Thinking: psychological perspectives on reasoning, judgment and decision making. John Wiley, Chichester, pp 213–232Google Scholar
  55. Heylighan F (2016a) Stigmergy as a universal coordination mechanism I: definition and components. Cogn Syst Res 38:4–13CrossRefGoogle Scholar
  56. Heylighan F (2016b) Stigmergy as a universal coordination mechanism II: varieties and evolution. Cogn Syst Res 38:50–59CrossRefGoogle Scholar
  57. Honari A (2015) Online social research in Iran: a need to offer a bigger picture. CyberOrient 9(2). http://www.cyberorient.net/article.do?articleId=9687
  58. Honeycutt C, Herring SC (2009) Beyond microblogging: conversation and collaboration via Twitter. In: Proceedings of the 42nd Hawaii International Conference on System Sciences (HICSS ’09).  https://doi.org/10.1109/HICSS.2009.89
  59. Hua H, Tilmes C, Zednik S (2013) PROV-XML: The PROV XML Schema, W3C. https://www.w3.org/TR/prov-xml/
  60. Huynh TD, Moreau L (2014) ProvStore: a public provenance repository. In: Ludäscher B, Plale B (eds) Provenance and annotation of data and processes: 5th International Provenance and Annotation Workshop, IPAW 2014. Springer, Cham, pp 275–277.  https://doi.org/10.1007/978-3-319-16462-5_32 CrossRefGoogle Scholar
  61. Huynh TD, Groth P, Zednik S (2013) PROV implementation report, W3C. http://www.w3.org/TR/prov-implementations/
  62. Huynh TD, Ebden M, Fischer J, Roberts S, Moreau L (2018) Provenance network analytics: an approach to data analytics using data provenance. Data Min Knowl Disc 32(3):708–735.  https://doi.org/10.1007/s10618-017-0549-3 MathSciNetCrossRefGoogle Scholar
  63. Ingold T (1993) The temporality of the landscape. World Archaeol 25(2):152–174CrossRefGoogle Scholar
  64. Ingold T (2007) Lines: a brief history. Routledge, AbingdonCrossRefGoogle Scholar
  65. Kahan J, Koivunen M-R (2001) Annotea: an open RDF infrastructure for shared Web annotations. In: Proceedings of the 10th international conference on the World Wide Web. ACM, New York, pp 623–632.  https://doi.org/10.1145/371920.372166 CrossRefGoogle Scholar
  66. Khan BK, Strong DM, Wang RY (2002) Information quality benchmarks: product and service performance. Commun ACM 45(4):184–192.  https://doi.org/10.1145/505248.506007 CrossRefGoogle Scholar
  67. Kitchin R (2014a) The data revolution: big data, open data, data infrastructures and their consequences. Sage, LondonGoogle Scholar
  68. Kitchin R (2014b) The real-time city? Big data and smart urbanism. Geol J 79(1):1–14.  https://doi.org/10.1007/s10708-013-9516-8 CrossRefGoogle Scholar
  69. Kittur A, Nickerson JV, Bernstein M, Gerber E, Shaw A, Zimmerman J, Lease M, Horton J (2013) The future of crowd work. In: Proceedings of the 2013 conference on Computer Supported Cooperative Work (CSCW ’13). ACM, New York, pp 1301–1318.  https://doi.org/10.1145/2441776.2441923 CrossRefGoogle Scholar
  70. Kleinberg J (2003) Bursty and hierarchical structure in streams. Data Min Knowl Disc 7(4):373–397.  https://doi.org/10.1023/A:1024940629314 MathSciNetCrossRefGoogle Scholar
  71. Klyne G, Groth P (2013) PROV-AQ: provenance access and query, W3C. https://www.w3.org/TR/prov-aq/
  72. Latour B (2005) Reassembling the social: an introduction to actor-network-theory. Oxford University Press, OxfordGoogle Scholar
  73. Lazer D, Friedman A (2007) The network structure of exploration and exploitation. Adm Sci Q 52(4):667–694.  https://doi.org/10.2189/asqu.52.4.667 CrossRefGoogle Scholar
  74. Lebo T, Sahoo S, McGuinness D (2013) PROV-O: The PROV ontology, W3C. https://www.w3.org/TR/prov-o/
  75. Lee CP, Paine D (2015) From The Matrix to a Model of Coordinated Action (MoCA): a conceptual framework of and for CSCW. In: Proceedings of the 18th ACM conference on Computer Supported Cooperative Work & Social Computing (CSCW15). ACM, New York, pp 179–194.  https://doi.org/10.1145/2675133.2675161 CrossRefGoogle Scholar
  76. Lefebvre H (1991) The production of space. Blackwell, OxfordGoogle Scholar
  77. Lerman K, Yan X, Wu X-Z (2016) The “majority illusion” in social networks. PLoS One 11(2):e0147617.  https://doi.org/10.1371/journal.pone.0147617 CrossRefGoogle Scholar
  78. Leskovec J, Chakrabarti D, Kleinberg J, Faloutsos C, Ghahramani Z (2010) Kronecker graphs: an approach to modeling networks. J Mach Learn Res 11(2):985–1042MathSciNetzbMATHGoogle Scholar
  79. Lewis TG, Marsh L (eds) (2016) Special issue—human-human stigmergy. Cogn Syst Res 38:1–60. https://www.sciencedirect.com/science/journal/13890417/38/supp/C
  80. Lintott CJ, Schawinski K, Slosar A, Land K, Bamford S, Thomas D, Raddick MJ, Nichol RC, Szalay A, Andreescu D, Murray P, Vandenberg J (2008) Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey. Mon Not R Astron Soc 389(3):1179–1189.  https://doi.org/10.1111/j.1365-2966.2008.13689.x CrossRefGoogle Scholar
  81. Little G, Chilton LB, Goldman M, Miller RC (2010) TurKit: human computation algorithms on Mechanical Turk. In: Proceedings of the 23rd annual ACM symposium on User Interface Software and Technology (UIST ’10). ACM, New York, pp 57–66.  https://doi.org/10.1145/1866029.1866040 CrossRefGoogle Scholar
  82. Luczak-Roesch M, Tinati R (2016) The social in the platform trap: why a microscopic system focus limits the prospect of social machines. Discover Soc 34:1–6Google Scholar
  83. Luczak-Roesch M, Tinati R, Simperl E, Van Kleek M, Shadbolt N, Simpson R (2014) Why won’t aliens talk to us? Content and community dynamics in online citizen science. In: 8th International AAAI Conference on Weblogs and Social Media. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/download/8092/8136
  84. Luczak-Roesch M, O’Hara K, Tinati R, Shadbolt N (2015a) Socio-technical computation. In: Proceedings of the 18th ACM conference companion on computer supported cooperative work & social computing. ACM, New York, pp 139–142Google Scholar
  85. Luczak-Roesch M, Tinati R, Shadbolt N (2015b) When resources collide: towards a theory of coincidence in information spaces. In: Proceedings of the 24th international conference on World Wide Web. ACM, New York, pp 1137–1142.  https://doi.org/10.1145/2740908.2743973 CrossRefGoogle Scholar
  86. Luczak-Roesch M, Tinati R, Van Kleek M, Shadbolt N (2015c) From coincidence to purposeful flow? Properties of transcendental information cascades. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining. ACM, New York, pp 633–638.  https://doi.org/10.1145/2808797.2809393 CrossRefGoogle Scholar
  87. Luczak-Roesch M, Tinati R, Aljaloud S, Hall W, Shadbolt N (2016) A universal socio-technical computing machine. In: Bozzon A, Cudre-Maroux P, Pautasso C (eds) Web Engineering: Proceedings of the 16th International Conference, ICWE 2016. Springer, Cham, pp 559–562Google Scholar
  88. Luczak-Roesch M, O’Hara K, Dinneen JD, Tinati R (2018) What an entangled Web we weave: an information-centric approach to time-evolving socio-technical systems. Mind Machine.  https://doi.org/10.1007/s11023-018-9478-1 CrossRefGoogle Scholar
  89. Lyman P, Varian HR (2003) How much information? 2003. http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/
  90. Malone TW, Laubacher R, Dellarocas C (2009) Harnessing crowds: mapping the genome of collective intelligence, MIT Sloan Research Paper No. 4732-09.  https://doi.org/10.2139/ssrn.1381502
  91. Marcinkowski M (2016) Data, ideology, and the developing critical program of social informatics. J Assoc Inf Sci Technol 67(5):1266–1275.  https://doi.org/10.1002/asi.23483/abstract CrossRefGoogle Scholar
  92. Marsden J (2013) Stigmergic self-organization and the improvisation of Ushahidi. Cogn Syst Res 21:52–64.  https://doi.org/10.1016/j.cogsys.2012.06.005 CrossRefGoogle Scholar
  93. Mason W, Jones A, Goldstone RL (2008) Propagation of innovations in networked groups. J Exp Psychol Gen 137(3):422–433.  https://doi.org/10.1037/a0012798 CrossRefGoogle Scholar
  94. McKinney EH, Yoos CJ (2010) Information about information: a taxonomy of views. MIS Q 34(2):329–344CrossRefGoogle Scholar
  95. Minder P, Bernstein A (2012) CrowdLang: a programming language for the systematic exploration of human computation systems. In: Aberer K, Flache A, Jager W, Liu L, Tang J, Guéret C (eds) Social informatics: Proceedings of the 4th international conference, SocInfo 2012. Springer, Berlin, pp 124–137.  https://doi.org/10.1007/978-3-642-35386-4_10 CrossRefGoogle Scholar
  96. Mironov AM (2010) Theory of processes. https://arxiv.org/abs/1009.2259v1
  97. Monroy-Hernández A (2013) Can crowds fill the void left by defunct newspapers? Reflections on our experiments with locative crowdsourcing, Social Media Collective Research Blog. https://socialmediacollective.org/2013/11/12/can-crowds-fill-the-void-left-by-defunct-newspapers-reflections-on-our-experiments-with-locative-crowdsourcing/
  98. Moreau L (2015) Aggregation by provenance types: a technique for summarising provenance graphs. https://arxiv.org/abs/1504.02616
  99. Moreau L (2017) A canonical form for PROV documents and its application to equality, signature, and validation. ACM T Internet Technol 17(4):35.  https://doi.org/10.1145/3032990 CrossRefGoogle Scholar
  100. Moreau L, Lebo T (2013) Linking across provenance bundles, W3C. https://www.w3.org/TR/prov-links/
  101. Moreau L, Missier P (2013a) PROV-N: The Provenance Notation, W3C. http://www.w3.org/TR/prov-n/
  102. Moreau L, Missier P2013bPROV-DM: The PROV Data Model, W3CGoogle Scholar
  103. Moreau L, Batlajery BV, Huynh TD, Michaelides D, Packer H (2018) A templating system to generate provenance. IEEE T Software Eng 44(2):103–121.  https://doi.org/10.1109/TSE.2017.2659745 CrossRefGoogle Scholar
  104. Morozov E (2011) The net delusion: how not to liberate the world. Penguin, LondonGoogle Scholar
  105. Murray-Rust D, Robertson D (2014) LSCitter: building social machines by augmenting existing social networks with interaction models. In: Proceedings of the 23rd International Conference on the World Wide Web (WWW ’14 Companion). ACM, New York, pp 875–880.  https://doi.org/10.1145/2567948.2578832 CrossRefGoogle Scholar
  106. Murray-Rust D, Papapanagiotou P, Robertson D (2015b) Softening electronic institutions to support natural interaction. Human Comput 2(2):155–188CrossRefGoogle Scholar
  107. Murray-Rust D, Scekic O, Papapanagiotou P, Truong H-L, Robertson D, Dustdar S (2015c) A collaboration model for community-based software development with social machines. EAI Endorsed Trans Collab Comput 1(5).  https://doi.org/10.4108/eai.17-12-2015.150812 CrossRefGoogle Scholar
  108. Murray-Rust D, Tarte S, Hartswood M, Green O (2015d) On wayfaring in social machines. In: Proceedings of the 24th international conference on the World Wide Web (WWW ’15). ACM, New York, pp 1143–1148.  https://doi.org/10.1145/2740908.2743971 CrossRefGoogle Scholar
  109. Murray-Rust D, Davoust A, Papapanagiotou P, Manataki A, Van Kleek M, Shadbolt N, Robertson D (2018) Towards executable representations of social machines. In: Chapman P, Stapleton G, Moktefi A, Perez-Kriz S, Bellucci F (eds) Diagrammatic representation and inference: 10th international conference, diagrams 2018. Springer, Cham, pp 765–769.  https://doi.org/10.1007/978-3-319-91376-6_77 CrossRefGoogle Scholar
  110. Myhill C (2004) Commercial success by looking for desire lines. In: Masoodian M, Jones S, Rogers B (eds) 6th Asia Pacific Conference on Computer Human Interaction (APCHI 2004). Springer, Berlin, pp 293–304Google Scholar
  111. Naroditskiy V, Rahwan I, Cebrian M, Jennings NR (2012) Verification in referral-based crowdsourcing. PLoS One 7(10):e45924.  https://doi.org/10.1371/journal.pone.0045924 CrossRefGoogle Scholar
  112. O’Hara K (2012a) Huxley: a beginner’s guide. Oneworld, OxfordGoogle Scholar
  113. O’Hara K (2012d) Transparency, open data and trust in government: shaping the infosphere. In: Proceedings of the 4th Annual ACM Web Science Conference (WebSci ’12). ACM, New York, pp 223–232.  https://doi.org/10.1145/2380718.2380747 CrossRefGoogle Scholar
  114. O’Hara K (2012e) Data quality, government data and the open data infosphere. Presented at the AISB/IACAP World Congress 2012: Information Quality Symposium. https://eprints.soton.ac.uk/340045/
  115. O’Hara K (2014a) Enhancing the quality of open data. In: Floridi L, Illari P (eds) The philosophy of information quality. Springer, Cham, pp 201–215.  https://doi.org/10.1007/978-3-319-07121-3_11 CrossRefGoogle Scholar
  116. O’Keefe RA (1990) The craft of prolog. MIT Press, Cambridge, MAGoogle Scholar
  117. Ott BL (2017) The age of Twitter: Donald J. Trump and the politics of debasement. Crit Stud Media Commun 34(1):59–68.  https://doi.org/10.1080/15295036.2016.1266686 CrossRefGoogle Scholar
  118. Packer HS, Drǎgan L, Moreau L (2014b) An auditable reputation service for collective adaptive systems. In: Miorandi D, Maltese V, Rovatsos M, Nijholt A, Stewart J (eds) Social collective intelligence: combining the powers of humans and machines to build a smarter society. Springer, Cham, pp 159–184.  https://doi.org/10.1007/978-3-319-08681-1_8 CrossRefGoogle Scholar
  119. Papapanagiotou P, Davoust A, Murray-Rust D, Manataki A, Van Kleek M, Shadbolt N, David Robertson (2018) Social machines for all, AAMAS 2018Google Scholar
  120. Parecki A (2017) Webmention, W3C recommendation. https://www.w3.org/TR/webmention/
  121. Pentland A (2008) Honest signals: how they shape our world. MIT Press, Cambridge, MACrossRefGoogle Scholar
  122. Pentland A (2014) Social physics: how good ideas spread—the lessons from a new science. Penguin, New YorkGoogle Scholar
  123. Phethean C, Simperl E, Tiropanis T, Tinati R, Hall W (2016) The role of data science in Web science. IEEE Intell Syst 31(3):102–107.  https://doi.org/10.1109/MIS.2016.54 CrossRefGoogle Scholar
  124. Pires EB, Braga J (eds) (2015) Bernard de Mandeville’s tropology of paradoxes: morals, politics, economics, and therapy. Springer, ChamGoogle Scholar
  125. Ramchurn SD, Huynh TD, Venanzi M, Shi B (2013) Collabmap: crowdsourcing maps for emergency planning. In: Proceedings of the 5th Annual ACM Web Science Conference (WebSci ’13). ACM, New York, pp 326–335.  https://doi.org/10.1145/2464464.2464508 CrossRefGoogle Scholar
  126. Ricci A, Tummolini L, Castelfranchi C (2017) Augmented societies with mirror worlds, AI and Society.  https://doi.org/10.1007/s00146-017-0788-2
  127. Riveni M, Truong H-L, Dustdar S (2014) On the elasticity of social compute units. In: Jarke M, Quix JMC, Rolland C, Manolopoulos Y, Mouratidis H, Horkoff J (eds) Advanced information systems engineering: proceedings of the 26th international conference, CAiSE 2014. Springer, Cham, pp 364–378.  https://doi.org/10.1007/978-3-319-07881-6_25 CrossRefGoogle Scholar
  128. Robertson D (2004) A lightweight coordination calculus for agent systems. In: Leite J, Omicini A, Torroni P, Yolum P (eds) Declarative Agent Languages and Technologies II. DALT 2004. Springer-Verlag, Berlin, Heidelberg, pp 183–197.  https://doi.org/10.1007/11493402_11 CrossRefGoogle Scholar
  129. Robertson D (2012) Lightweight coordination calculus for agent systems: retrospective and prospective. In: Sakama C, Sardina S, Vasconcelos W, Winikoff M (eds) Declarative Agent Languages and Technologies IX: revised selected and invited papers of 9th international workshop, DALT 2011. Springer, Berlin, pp 84–89.  https://doi.org/10.1007/978-3-642-29113-5_7 CrossRefGoogle Scholar
  130. Robertson D, Moreau L, Murray-Rust D, O’Hara K (2014) An open system for social computation. In: O’Hara K, Carolyn Nguyen M-H, Haynes PD (eds) Digital enlightenment yearbook 2014: social networks and social machines, surveillance and empowerment. IOS Press, Amsterdam, pp 235–252Google Scholar
  131. Rogers Y, Marshall P (2017) Research in the wild: synthesis lectures on human-centred infomatics. Morgan & Claypool, San Rafael, CA.  https://doi.org/10.2200/S00764ED1V01Y201703HCI037 CrossRefGoogle Scholar
  132. Rozinat A, Zickler S, Veloso M, van der Aalst WMP, McMillen C (2009) Analyzing multi-agent activity logs using process mining techniques. In: Asama H, Kurokawa H, Ota J, Sekiyama K (eds) Distributed autonomous robotic systems, vol 8. Springer, Berlin, pp 251–260.  https://doi.org/10.1007/978-3-642-00644-9_22 CrossRefGoogle Scholar
  133. Searle J (1969) Speech acts: an essay in the philosophy of language. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  134. Sengupta B, Jain A, Bhattacharya K, Truong H-L, Dustdar S (2012) Who do you call? Problem resolution through social compute units. In: Liu C, Ludwig H, Toumani F, Qi Y (eds) Service oriented computing: proceedings of the 10th international conference, ICSOC 2012. Springer, Berlin, pp 48–62.  https://doi.org/10.1007/978-3-642-34321-6_4 CrossRefGoogle Scholar
  135. Sengupta B, Jain A, Bhattacharya K, Truong H-L, Dustdar S (2013) Collective problem solving using Social Compute Units. IJCIS 22(4):1341002.  https://doi.org/10.1142/S0218843013410025 CrossRefGoogle Scholar
  136. Shadbolt N, O’Hara K (2013) Linked data in government. IEEE Internet Comput 17(4):72–77.  https://doi.org/10.1109/MIC.2013.72 CrossRefGoogle Scholar
  137. Shadbolt N, O’Hara K, Berners-Lee T, Gibbins N, Glaser H, Hall W, Schraefel MC (2012) Linked open government data: lessons from data.gov.uk. IEEE Intell Syst 27(3):16–24.  https://doi.org/10.1109/MIS.2012.23 CrossRefGoogle Scholar
  138. Smart P (2017) Mandevillian intelligence, Synthese.  https://doi.org/10.1007/s11229-017-1414-z CrossRefGoogle Scholar
  139. Smart P (2018) Knowledge machines. Knowl Eng Rev 33:e11.  https://doi.org/10.1017/S0269888918000139 CrossRefGoogle Scholar
  140. Sosa E (2007) A virtue epistemology vol.1: apt belief and reflective knowledge. Oxford University Press, OxfordCrossRefGoogle Scholar
  141. Southan C, Sharman JL, Benson HE, Faccenda E, Pawson AJ, Alexander SPH, Buneman P, Davenport AP, McGrath JC, Peters JA, Spedding M, Catterall WA, Fabbro D, Davies JA (2016) The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res 44(D1):D1054–D1068.  https://doi.org/10.1093/nar/gkv1037 CrossRefGoogle Scholar
  142. Stephens-Davidowitz S (2017) Everybody lies: big data, new data, and what the internet can tell us about who we really are. HarperCollins, New YorkGoogle Scholar
  143. Strohmaier M, Wagner C (2014) Computational social science for the World Wide Web. IEEE Intell Syst 29(5):84–88CrossRefGoogle Scholar
  144. Suárez-Ruiz F, Zhou X, Pham Q-C (2018) Can robots assemble an IKEA chair? Sci Robot 3(17):eaat6385.  https://doi.org/10.1126/scirobotics.aat6385 CrossRefGoogle Scholar
  145. Sunstein CR (2006) Deliberating groups versus prediction markets (or Hayek’s challenge to Habermas). Episteme 3(3):192–213.  https://doi.org/10.3366/epi.2006.3.3.192 CrossRefGoogle Scholar
  146. Tarte S, Willcox P, Glaser H, De Roure D (2015) Archetypal narratives in social machines: approaching sociality through prosopography. In: Proceedings of the ACM web science conference 2015. ACM, New York, p 24.  https://doi.org/10.1145/2786451.2786471 CrossRefGoogle Scholar
  147. Thaler RH, Sunstein CR (2009) Nudge:improving decisions about health, wealth and happiness, revised and expanded edition. Penguin, New YorkGoogle Scholar
  148. Tian Y, Hankins RA, Patel JM (2008) Efficient aggregation for graph summarization. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data. ACM, New York, pp 567–580.  https://doi.org/10.1145/1376616.1376675 CrossRefGoogle Scholar
  149. Tinati R, Luczak-Roesch M, Hall W (2016a) Finding structure in Wikipedia edit activity: an information cascade approach. In: Proceedings of the 25th International Conference on the World Wide Web (WWW16). ACM, New York, pp 1007–1012.  https://doi.org/10.1145/2872518.2891110 CrossRefGoogle Scholar
  150. Tinati R, Luczak-Roesch M, Hall W, Shadbolt N (2016b) More than an edit: using transcendental information cascades to capture hidden structure in Wikipedia. In: Proceedings of the 25th International Conference on the World Wide Web (WWW16). ACM, New York, pp 115–116.  https://doi.org/10.1145/2872518.2889401 CrossRefGoogle Scholar
  151. Vass J (2013) Webscience, “social machines” and principles for redesigning theories of agency: a prolegomenon. Presented at ACM Web Science Conference. https://eprints.soton.ac.uk/359190/
  152. Vass J, Munson J (2015) Revisiting the three Rs of social machines: reflexivity, recognition and responsivity. In: Proceedings of the 24th International Conference on the World Wide Web, pp. 1161–1166.  https://doi.org/10.1145/2740908.2743974
  153. Verboven K, Carlier M, Dumolyn J (2007) A short manual to the art of prosopography. In: Keats-Rohan KSB (ed) Prosopography approaches and applications: a handbook. University of Oxford Linacre College Unit for Prosopographical Research, OxfordGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nigel Shadbolt
    • 1
  • Kieron O’Hara
    • 2
  • David De Roure
    • 3
  • Wendy Hall
    • 2
  1. 1.Department of Computer ScienceUniversity of OxfordOxfordUK
  2. 2.Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  3. 3.Oxford eResearch CentreUniversity of OxfordOxfordUK

Personalised recommendations