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What an Entangled Web We Weave: An Information-centric Approach to Time-evolving Socio-technical Systems

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Abstract

A new layer of complexity, constituted of networks of information token recurrence, has been identified in socio-technical systems such as the Wikipedia online community and the Zooniverse citizen science platform. The identification of this complexity reveals that our current understanding of the actual structure of those systems, and consequently the structure of the entire World Wide Web, is incomplete, which raises novel questions for data science research but also from the perspective of social epistemology. Here we establish the principled foundations and practical advantages of analyzing information diffusion within and across Web systems with Transcendental Information Cascades, and outline resulting directions for future study in the area of socio-technical systems. We also suggest that Transcendental Information Cascades may be applicable to any kind of time-evolving system that can be observed using digital technologies, and that the structures found in such systems comprise properties common to all naturally occurring complex systems.

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Notes

  1. https://www.zooniverse.org.

  2. https://en.wikipedia.org.

References

  • Adamic, L. A., & Huberman, B. A. (2000). Power-law distribution of the world wide web. Science, 287(5461), 2115–2115.

    Article  Google Scholar 

  • Alvesson, M., & Sandberg, J. (2014). Habitat and habitus: Boxed-in versus box-breaking research. Organization Studies, 35(7), 967–987.

    Article  Google Scholar 

  • Anick, D., Mitra, D., & Sondhi, M. M. (1982). Stochastic theory of a data-handling system with multiple sources. Bell Labs Technical Journal, 61(8), 1871–1894.

    Article  MathSciNet  Google Scholar 

  • Bailey, K. D. (1990). Social entropy theory. Albany: SUNY Press.

    Google Scholar 

  • Bailey, K. D. (2006). Living systems theory and social entropy theory. Systems Research and Behavioral Science, 23(3), 291–300.

    Article  Google Scholar 

  • Barabasi, A.-L. (2005). The origin of bursts and heavy tails in human dynamics. arXiv:cond-mat/0505371.

  • Barabási, A.-L., Albert, R., & Jeong, H. (2000). Scale-free characteristics of random networks: The topology of the world-wide web. Physica A: Statistical Mechanics and its Applications, 281(1–4), 69–77.

    Article  Google Scholar 

  • Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case research strategy in studies of information systems. MIS Quarterly, 11, 369–386.

    Article  Google Scholar 

  • Berners-Lee, T., Fischetti, M., & Dertouzos, M. L. (2000). Weaving the web: The original design and ultimate destiny of the World Wide Web by its inventor. New York: HarperInformation.

    Google Scholar 

  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117.

    Article  Google Scholar 

  • Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., et al. (2000). Graph structure in the web. Computer Networks, 33(1), 309–320.

    Article  Google Scholar 

  • Cebrian, M., Rahwan, I., & Pentland, A. S. (2016). Beyond viral. Communications of the ACM, 59(4), 36–39.

    Article  Google Scholar 

  • Cheng, J., Adamic, L., Dow, P. A., Kleinberg, J. M., & Leskovec, J. (2014). Can cascades be predicted? In Proceedings of the 23rd international conference on World wide web (pp. 925–936). ACM.

  • Dinneen, J. D. & Brauner, C. (2017). Information-not-thing: further problems with and alternatives to the belief that information is physical. In CAIS-ACSI ‘17: Proceedings of the 2017 Canadian Association for Information Science.

  • Dinneen, J. D., & Brauner, C. (2015). Practical and philosophical considerations for defining information as well-formed, meaningful data in the information sciences. Library Trends, 63(3), 378–400.

    Article  Google Scholar 

  • Donner, R. V., Small, M., Donges, J. F., Marwan, N., Zou, Y., Xiang, R., et al. (2011). Recurrence-based time series analysis by means of complex network methods. International Journal of Bifurcation and Chaos, 21(04), 1019–1046.

    Article  MathSciNet  Google Scholar 

  • Eckmann, J.-P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence plots of dynamical systems. EPL (Europhysics Letters), 4(9), 973.

    Article  Google Scholar 

  • Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.

    Article  Google Scholar 

  • Feld, S. L. (1991). Why your friends have more friends than you do. American Journal of Sociology, 96(6), 1464–1477.

    Article  Google Scholar 

  • Floridi, L. (2015). Semantic conceptions of information. Stanford: The Metaphysics Research Lab, Stanford University.

    MATH  Google Scholar 

  • Grudin, J. (1994). Computer-supported cooperative work: History and focus. Computer, 27(5), 19–26.

    Article  Google Scholar 

  • Hendler, J., & Berners-Lee, T. (2010). From the semantic web to social machines: A research challenge for ai on the world wide web. Artificial Intelligence, 174(2), 156–161.

    Article  MathSciNet  Google Scholar 

  • Hohenberg, P. C., & Halperin, B. I. (1977). Theory of dynamic critical phenomena. Reviews of Modern Physics, 49(3), 435.

    Article  Google Scholar 

  • Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

    Article  Google Scholar 

  • Honari, A. (2015). Online social research in Iran: A need to offer a bigger picture. CyberOrient: The Online Journal of Virtual Middle East, 9(2).

  • Jung, C. G. (2010). Synchronicity: An acausal connecting principle. (From Vol. 8. of the collected works of CG Jung) (New in Paper). Princeton: Princeton University Press.

    Google Scholar 

  • Kant, I. (1934). Critique of pure reason. (Trans: Smith, N. K.). London: Macmillan.

  • Keegan, B. C., Lev, S., & Arazy, O. (2016). Analyzing organizational routines in online knowledge collaborations: A case for sequence analysis in CSCW. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing (pp. 1065–1079). ACM.

  • Kleinberg, J. (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373–397.

    Article  MathSciNet  Google Scholar 

  • Kraut, R. E., Resnick, P., Kiesler, S., Burke, M., Chen, Y., Kittur, N., et al. (2012). Building successful online communities: Evidence-based social design. Cambridge: MIT Press.

    Google Scholar 

  • Kullback, S. (1997). Information theory and statistics. Chelmsford: Courier Corporation.

    MATH  Google Scholar 

  • Lee, C. P. & 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 (pp. 179–194). ACM.

  • Lee, E., Karimi, F., Jo, H.-H., Strohmaier, M., & Wagner, C. (2017). Homophily explains perception biases in social networks. arXiv:1710.08601.

  • Lee, A. S. (2010). Retrospect and prospect: Information systems research in the last and next 25 years. Journal of Information Technology, 25(4), 336–348.

    Article  Google Scholar 

  • Lerman, K. (2016). Information is not a virus, and other consequences of human cognitive limits. Future Internet, 8(2), 21.

    Article  Google Scholar 

  • Lerman, K., Yan, X., & Wu, X.-Z. (2016). The “majority illusion” in social networks. PloS ONE, 11(2), e0147617.

    Article  Google Scholar 

  • 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 (pp. 1137–1142). ACM.

  • Luczak-Roesch, M., Tinati, R., O’Hara, K., & Shadbolt, N. (2015a). Socio-technical computation. In Proceedings of the 18th ACM conference companion on computer supported cooperative work & social computing (pp. 139–142). ACM.

  • Luczak-Roesch, M., Tinati, R., Simperl, E., Van Kleek, M., Shadbolt, N., & Simpson, R. J. (2014). Why won’t aliens talk to us? content and community dynamics in online citizen science. In ICWSM.

  • 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 2015 (pp. 633–638). ACM.

  • Malone, T. W., Laubacher, R., & Dellarocas, C. (2009). Harnessing crowds: Mapping the genome of collective intelligence. Technical Report No. 4732-09, MIT Sloan Research Paper.

  • McKinney, E. H, Jr., & Yoos, C. J. (2010). Information about information: A taxonomy of views. MIS Quarterly, 34, 329–344.

    Article  Google Scholar 

  • McLuhan, M., & Fiore, Q. (1967). The medium is the message. New York, 123, 126–128.

    Google Scholar 

  • Ovchinnikov, I. V. (2016). Introduction to supersymmetric theory of stochastics. Entropy, 18(4), 108.

    Article  Google Scholar 

  • Parameswaran, M., & Whinston, A. B. (2007). Research issues in social computing. Journal of the Association for Information Systems, 8(6), 336.

    Article  Google Scholar 

  • Parisi, G., & Sourlas, N. (1982). Supersymmetric field theories and stochastic differential equations. Nuclear Physics B, 206(2), 321–332.

    Article  MathSciNet  Google Scholar 

  • Pettit, P. (2006). When to defer to majority testimony-and when not. Analysis, 66(291), 179–187.

    Article  Google Scholar 

  • Pettit, P. (2010). Groups with minds of their own. In A. I. Goldman & D. Whitcomb (Eds.), Social epistemology: Essential readings (p. 242). Oxford: Oxford University Press.

    Google Scholar 

  • Popper, K. (1972). Objective knowledge: An evolutionary approach. Oxford University Press.

  • Popper, K. (2013). Knowledge and the body-mind problem: In defence of interaction. London: Routledge.

    Google Scholar 

  • Potthast, M., Stein, B., & Gerling, R. (2008). Automatic vandalism detection in wikipedia. In European conference on information retrieval (pp. 663–668). Springer.

  • Quinn, A. J. & Bederson, B. B. (2011). Human computation: A survey and taxonomy of a growing field. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1403–1412). ACM.

  • Rabiner, L. R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286.

    Article  Google Scholar 

  • Shahaf, D., Yang, J., Suen, C., Jacobs, J., Wang, H., & Leskovec, J. (2013). Information cartography: Creating zoomable, large-scale maps of information. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1097–1105). ACM.

  • Shannon, C. E. (1949). Communication theory of secrecy systems. Bell Labs Technical Journal, 28(4), 656–715.

    Article  MathSciNet  Google Scholar 

  • Smart, P. R. (2018). Mandevillian intelligence. Synthese, 195(9), 4169–4200. https://doi.org/10.1007/s11229-017-1414-z.

    Article  Google Scholar 

  • Smart, P., Simperl, E., & Shadbolt, N. (2014). A taxonomic framework for social machines. In D. Miorandi, V. Maltese, M. Rovatsos, A. Nijholt, & J. Stewart (Eds.), Social collective intelligence (pp. 51–85). Cham: Springer.

    Google Scholar 

  • Strogatz, S. H. (2014). Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineering. London: Hachette UK.

    MATH  Google Scholar 

  • Tarski, A. (1944). The semantic conception of truth: And the foundations of semantics. Philosophy and Phenomenological Research, 4(3), 341–376.

    Article  MathSciNet  Google Scholar 

  • Tinati, R., & Luczak-Roesch, M. (2017). Wikipedia: A complex social machine. SIGWEB Newsletter, Winter, 6. https://doi.org/10.1145/3027141.3027147.

  • Tinati, R., Luczak-Roesch, M., & Hall, W. (2016). Finding structure in wikipedia edit activity: An information cascade approach. In Proceedings of the 25th international conference companion on world wide web (pp. 1007–1012). International World Wide Web Conferences Steering Committee.

  • Tinati, R., Luczak-Roesch, M., Shadbolt, N., & Hall, W. (2015a). Using wikiprojects to measure the health of wikipedia. In Proceedings of the 24th international conference on world wide web (pp. 369–370). ACM.

  • Tinati, R., Van Kleek, M., Simperl, E., Luczak-Rösch, M., Simpson, R., & Shadbolt, N. (2015b). Designing for citizen data analysis: A cross-sectional case study of a multi-domain citizen science platform. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 4069–4078). ACM.

  • Van de Sompel, H., Nelson, M., & Sanderson, R. (2013). HTTP framework for time-based access to resource states–Memento. No. RFC 7089. https://www.rfc-editor.org/info/rfc7089.

  • Van de Sompel, H., Nelson, M. L., Sanderson, R., Balakireva, L. L., Ainsworth, S., & Shankar, H. (2009). Memento: Time travel for the web. arXiv:0911.1112.

  • Webber Jr, C. L. & Zbilut, J. P. (2005). Recurrence quantification analysis of nonlinear dynamical systems. In Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 26–94). National Science Foundation.

  • Weber, M. (1978). Economy and society: An outline of interpretive sociology (Vol. 1). Berkeley: University of California Press.

    Google Scholar 

  • Whitehead, A . N., & Russell, B. (1912). Principia mathematica (Vol. 2). Cambridge: University Press.

    MATH  Google Scholar 

  • Williams, M. J., & Musolesi, M. (2016). Spatio-temporal networks: Reachability, centrality and robustness. Royal Society Open Science, 3(6), 160196.

    Article  MathSciNet  Google Scholar 

  • Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), 686–688.

    Article  Google Scholar 

  • Zollman, K. J. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587.

    Article  Google Scholar 

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Acknowledgements

This article underwent an extensive open peer-review process, because the initial version was published as a pre-print on PeerJ (https://doi.org/10.7287/peerj.preprints.2789). The authors want thank all people who took the time to provide critical comments and feedback about this version or who engaged in discussions when the argument was presented at scientific events. Our work was partially supported under SOCIAM: The Theory and Practice of Social Machines, funded by the UK EPSRC under Grant EP/J017728/2 and the Victoria University of Wellington Research Establishment Grant 8-1620-213486-3744.

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Authors and Affiliations

  1. School of Information Management, Victoria University of Wellington, Rutherford House, Pipitea Campus, Wellington, New Zealand

    Markus Luczak-Roesch & Jesse David Dinneen

  2. Web and Internet Science, University of Southampton, Building 32, Highfield Campus, Southampton, UK

    Kieron O’Hara

  3. Microsoft Corp, Singapore, Singapore

    Ramine Tinati

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  1. Markus Luczak-Roesch
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  2. Kieron O’Hara
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  3. Jesse David Dinneen
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Correspondence to Markus Luczak-Roesch.

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Luczak-Roesch, M., O’Hara, K., Dinneen, J.D. et al. What an Entangled Web We Weave: An Information-centric Approach to Time-evolving Socio-technical Systems. Minds & Machines 28, 709–733 (2018). https://doi.org/10.1007/s11023-018-9478-1

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