Advertisement

Serendipity as an emerging design principle of the infosphere: challenges and opportunities

  • Urbano Reviglio
Original Paper

Abstract

Underestimated for a long time, serendipity is an increasingly recognized design principle of the infosphere. Being influenced by environmental and human factors, the experience of serendipity encompasses fundamental phases of production, distribution and consumption of information. On the one hand, design information architectures for serendipity increases the diversity of information encountered as well as users’ control over information processes. On the other hand, serendipity is a capability. It helps individuals to internalize and adopt strategies that increase the chances of experiencing it. As such, the pursuit for serendipity can help to burst filter bubbles and weaken echo chambers in social media. The article reviews the literature on emerging issues surrounding serendipity in human–computer interactions. By doing so, it firstly presents the study of serendipity and the debate about its role in digital environments. Then, it introduces the main features of a preliminary architecture for serendipity. Finally, it analyzes from an interdisciplinary perspective the values that embraces and sustains. The conclusion is that serendipity can be conceived as an emerging design and ethical principle able to strengthen media pluralism and other emerging human rights in the context of online personalization. Main limitations and potential unintended consequences are also discussed.

Keywords

Serendipity Design ethics Nudging Personalization Filter bubbles Echo chambers 

Notes

Acknowledgements

This research is funded by the ERASMUS MUNDUS program in Law, Science and Technology (LAST-JD) coordinated by University of Bologna.

Compliance with ethical standards

Conflict of interest

The author declares that he has no conflict of interest.

Informed consent

Research for this paper did not involve animal or human participants; neither was there a need to request informed consent from anyone.

References

  1. Abbott, A. (2008). The traditional future: A computational theory of library research. College & Research Libraries, 69(6), 524–545.CrossRefGoogle Scholar
  2. Ahmadi, M., & Wohn, D. Y. (2018). The antecedents of incidental news exposure on social media. Social Media + Society, 4(2), 2056305118772827.CrossRefGoogle Scholar
  3. Albanie, S., Shakespeare, H., & Gunter, T. (2017). Unknowable manipulators: Social network curator algorithms. arXiv preprint arXiv:1701.04895.Google Scholar
  4. Auray, N. (2007). Folksonomy: The new way to serendipity. International Journal of Digital Economics, 65, 67–88.Google Scholar
  5. Backstrom, L. (2013). News feed FYI: A window into news feed (p. 6). Menlo Park: Facebook for Business.Google Scholar
  6. Björneborn, L. (2017). Three key affordances for serendipity: Toward a framework connecting environmental and personal factors in serendipitous encounters. Journal of Documentation, 73(5), 1053–1081.CrossRefGoogle Scholar
  7. Bode, L., & Vraga, E. K. (2015). In related news, that was wrong: The correction of misinformation through related stories functionality in social media. Journal of Communication, 65(4), 619–638.CrossRefGoogle Scholar
  8. Bodo, B., Helberger, N., Irion, K., Zuiderveen Borgesius, F., Moller, J., van de Velde, B., … de Vreese, C. (2017). Tackling the algorithmic control crisis—The technical, legal, and ethical challenges of research into algorithmic agents. Yale JL & Tech., 19, 133.Google Scholar
  9. Bogers, T., & Björneborn, L. (2013). Micro-serendipity: Meaningful coincidences in everyday life shared on Twitter. iConference, 2013, 196–208.Google Scholar
  10. Bozdag, E., & Timmermans, E. (2011). Values in the filter bubble Ethics of Personalization Algorithms in Cloud Computing. In Proceedings, 1st International Workshop on Values in Design–Building Bridges between RE, HCI and Ethics, Lisbon.Google Scholar
  11. Bozdag, E., & van den Hoven, J. (2015). Breaking the filter bubble: Democracy and design. Ethics and Information Technology, 17(4), 249–265.CrossRefGoogle Scholar
  12. Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30–44.CrossRefGoogle Scholar
  13. Campanario, J. M. (1996). Using citation classics to study the incidence of serendipity in scientific discovery. Scieontometrics, 37(1), 3–24.CrossRefGoogle Scholar
  14. Campos, J., & Figueiredo, A. D. (2002). Programming for serendipity. In Proceedings of the AAAI fall symposium on chance discoveryThe discovery and management of chance events.Google Scholar
  15. Carr, N. (2016). Utopia is creepy: And other provocations. New York: W W Norton & Co Inc.Google Scholar
  16. Carr, P. L. (2015). Serendipity in the stacks: Libraries, information architecture, and the problems of accidental discovery. College & Research Libraries, 76, 831–842.CrossRefGoogle Scholar
  17. Carson, A. B. (2015). Public discourse in the age of personalization: Psychological explanations and political implications of search engine bias and the filter bubble. Journal of Science Policy & Governance, 7(1).Google Scholar
  18. Cobo, C., & Moravec, J. (2011). Invisible Learning. Toward a New Ecology of Education. Col·lecció Transmedia XXI. Laboratori de Mitjans Interactius/Publicacions i Edicions de la Universitat de Barcelona. Barcelona.Google Scholar
  19. Colton, S., & Wiggins, G. A. (2012). Computational creativity: The final frontier?. In Ecai (Vol. 2012, pp. 16–21).Google Scholar
  20. Cunningham, D. J. (2001). Fear and loathing in the information age. Cybernetics & Human Knowing, 8(4), 64–74.Google Scholar
  21. Danezis, G., Domingo-Ferrer, J., Hansen, M., Hoepman, J. H., Metayer, D. L., Tirtea, R., & Schiffner, S. (2015). Privacy and data protection by design-from policy to engineering. arXiv preprint arXiv:1501.03726.Google Scholar
  22. Darbellay, F., Moody, Z., Sedooka, A., & Steffen, G. (2014). Interdisciplinary research boosted by serendipity. Creativity Research Journal, 26(1), 1–10.CrossRefGoogle Scholar
  23. de Melo, R. M. C. (2018). On serendipity in the digital medium: Towards a framework for valuable unpredictability in interaction Design.Google Scholar
  24. De Rond, M. (2014). The structure of serendipity. Culture and Organization, 20(5), 342–358.CrossRefGoogle Scholar
  25. Delacroix, S. (2018). Taking turing by surprise? Designing digital computers for morally-loaded contexts. arXiv preprint arXiv:1803.04548.Google Scholar
  26. Derakhshan, H. (2016). Social Media Is Killing Discourse Because It’s Too Much Like TV in MIT Technology Review. Accessed Jan 15, 2018, from https://www.technologyreview.com/s/602981/social-media-is-killing-discourse-because-its-too-much-like-tv/.
  27. DeVito, M. A. (2017). From editors to algorithms: A values-based approach to understanding story selection in the Facebook news feed. Digital Journalism, 5(6), 753–773.CrossRefGoogle Scholar
  28. Domingos, P. (2015). The master algorithm: How the quest for the ultimate learning machine will remake our world. New York: Basic Books.Google Scholar
  29. Dunbar, K., & Fugelsang, J. (2005). Scientific thinking and reasoning. The Cambridge Handbook of Thinking and Reasoning, 705–725.Google Scholar
  30. Dylko, I., Dolgov, I., Hoffman, W., Eckhart, N., Molina, M., & Aaziz, O. (2018). Impact of customizability technology on political polarization. Journal of Information Technology & Politics, 15(1), 19–33.CrossRefGoogle Scholar
  31. Edward Foster, A., & Ellis, D. (2014). Serendipity and its study. Journal of Documentation, 70(6), 1015–1038.CrossRefGoogle Scholar
  32. Erdelez, S. (1997). Information encountering: a conceptual framework for accidental information discovery. In Proceedings of an international conference on Information seeking in context (pp. 412–421). Taylor Graham Publishing, London.Google Scholar
  33. Erdelez, S. (2004). Investigation of information encountering in the controlled research environment. Information Processing & Management, 40(6), 1013–1025.zbMATHCrossRefGoogle Scholar
  34. Erdelez, S., & Jahnke, I. (2018). Personalized systems and illusion of serendipity: A sociotechnical lens. In Workshop of WEPIR 2018.Google Scholar
  35. Eskens, S., Helberger, N., & Moeller, J. (2017). Challenged by news personalisation: Five perspectives on the right to receive information. Journal of Media Law, 9(2), 259–284.CrossRefGoogle Scholar
  36. Floridi, L. (2011). The informational nature of personal identity. Minds and Machines, 21(4), 549.CrossRefGoogle Scholar
  37. Floridi, L. (2015a). The politics of uncertainty. Philosophy & Technology, 28(1), 1–4.CrossRefGoogle Scholar
  38. Floridi, L. (2015b). The onlife manifesto. Cham: Springer.CrossRefGoogle Scholar
  39. Floridi, L. (2016a). Mature information societies—A matter of expectations. Philosophy & Technology, 29(1), 1–4.MathSciNetCrossRefGoogle Scholar
  40. Floridi, L. (2016b). Tolerant paternalism: Pro-ethical design as a resolution of the dilemma of toleration. Science and Engineering Ethics, 22(6), 1669–1688.CrossRefGoogle Scholar
  41. Fogg, B. J., Lee, E., & Marshall, J. (2002). Interactive technology and persuasion. The Handbook of Persuasion: Theory and Practice. Thousand Oaks: Sage.Google Scholar
  42. Forsyth, M. (2014). The unknown unknown: Bookshops and the delight of not getting what you wanted. London: Icon Books Ltd.Google Scholar
  43. Friedman, B., Kahn, P., & Borning, A. (2002). Value sensitive design: Theory and methods. University of Washington technical report, pp. 02–12.Google Scholar
  44. Gal, M. S. (2017). Algorithmic challenges to autonomous choice. Michigan Telecommunications and Technology Law Review, 2017.Google Scholar
  45. Ge, M., Delgado-Battenfeld, C., & Jannach, D. (2010). Beyond accuracy: Evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems (pp. 257–260). ACM, New York.Google Scholar
  46. Gibson, J. J. (2014). The ecological approach to visual perception: Classic edition. Hove: Psychology Press.CrossRefGoogle Scholar
  47. Gillespie, T. (2014). The relevance of algorithms. Media technologies: Essays on communication, materiality, and society, p. 167.Google Scholar
  48. Granovetter, M. S. (1977). The strength of weak ties. In Social networks (pp. 347–367). Chicago: University of Chicago PressCrossRefGoogle Scholar
  49. Gup, T. (1997). Technology and the end of serendipity. The Chronicle of Higher Education, 44(21), A52.Google Scholar
  50. Harambam, J., Helberger, N., & van Hoboken, J. (2018). Democratizing algorithmic news recommenders: How to materialize voice in a technologically saturated media ecosystem. Philosophical Transactions A, 376(2133), 20180088.CrossRefGoogle Scholar
  51. Helberger, N. (2011). Diversity by design. Journal of Information Policy, 1, 441–469.CrossRefGoogle Scholar
  52. Helberger, N., Karppinen, K., & D’Acunto, L. (2016). Exposure diversity as a design principle for recommender systems. Information, Communication & Society, 21, 1–17.Google Scholar
  53. Hendler, J., & Hugill, A. (2013). The syzygy surfer:(Ab) using the semantic web to inspire creativity. International Journal of Creative Computing, 1(1), 20–34.CrossRefGoogle Scholar
  54. Hildebrandt, M. (2009). Profiling and AmI. In The future of identity in the information society (pp. 273–310). Berlin: Springer.CrossRefGoogle Scholar
  55. Hildebrandt, M. (2017). Privacy as protection of the incomputable self: Agonistic machine learning.Google Scholar
  56. Hildebrandt, M., & Koops, B. J. (2010). The challenges of ambient law and legal protection in the profiling era. The Modern Law Review, 73(3), 428–460.CrossRefGoogle Scholar
  57. Hoffmann, C. P., Lutz, C., Meckel, M., & Ranzini, G. (2015). Diversity by choice: Applying a social cognitive perspective to the role of public service media in the digital age. International Journal of Communication, 9(1), 1360–1381.Google Scholar
  58. Hoven, J. van den, Miller, S., & Pogge, T. (Eds.). (2017). Designing in ethics. Cambridge: Cambridge University Press.Google Scholar
  59. Karppinen, K. (2008). Media and the paradoxes of pluralism. The Media and Social Theory, 27–42.Google Scholar
  60. Keymolen, E. (2016). Trust on the line: A philosophycal exploration of trust in the networked era.Google Scholar
  61. Kop, R. (2012). The unexpected connection: Serendipity and human mediation in networked learning. Journal of Educational Technology & Society, 15(2), 2–11.MathSciNetGoogle Scholar
  62. Kotkov, D., Wang, S., & Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowledge-Based Systems, 111, 180–192.CrossRefGoogle Scholar
  63. Kroes, P., & van de Poel, I. (2015). Design for values and the definition, specification, and operationalization of values. Handbook of Ethics, Values, and Technological Design: Sources, Theory, Values and Application Domains, 151–178.Google Scholar
  64. Krotoski, A. (2011). Digital serendipity: Be careful what you don’t wish for in The Guardian International Edition. Accessed Jan 15, 2018, from https://www.theguardian.com/technology/2011/aug/21/google-serendipity-profiling-aleks-krotoski.
  65. Loepp, B., Hussein, T., & Ziegler, J. (2014). Choice-based preference elicitation for collaborative filtering recommender systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (New York, NY, 2014), pp. 3085–3094.Google Scholar
  66. Lupo, L. (2012). Filosofia della serendipity (Vol. 73). Napoli: Guida Editori.Google Scholar
  67. Lutz, C., Hoffmann, P. C., & Meckel, M. (2017). Online serendipity: A contextual differentiation of antecedents and outcomes. Journal of the Association for Information Science and Technology, 68(7), 1698–1710.CrossRefGoogle Scholar
  68. Lynch, M. P. (2016). The Internet of us: Knowing more and understanding less in the age of big data. New York: WW Norton & Company.Google Scholar
  69. Makri, S. (2014). Serendipity is not Bullshit. Paper presented at the EuroHCIR 2014, The 4th European Symposium on Human-Computer Interaction and Information Retrieval, 13 Sep 2014, London, UK.Google Scholar
  70. Makri, S., & Blandford, A. (2012). Coming across information serendipitously—Part 1, p. A process model. Journal of Documentation, 68(5), 684–705.CrossRefGoogle Scholar
  71. Makri, S., Blandford, A., Woods, M., Sharples, S., & Maxwell, D. (2014). “Making my own luck”: Serendipity strategies and how to support them in digital information environments. Journal of the Association for Information Science and Technology, 65(11), 2179–2194.CrossRefGoogle Scholar
  72. Maloney, A., & Conrad, L. Y. (2016). Expecting the unexpected: Serendipity, discovery, and the scholarly research process (white paper), Thousand Oaks: SAGE.CrossRefGoogle Scholar
  73. Marcus, G. E. (2010). Sentimental citizen: Emotion in democratic politics. University Park: Penn State Press.Google Scholar
  74. Matt, C., Benlian, A., Hess, T., & Weiß, C. (2014). Escaping from the Filter Bubble? The Effects of Novelty and Serendipity on Users’ Evaluations of Online Recommendations. In Proceedings of the 35th International Conference on Information Systems (ICIS2014), Auckland, New Zealand.Google Scholar
  75. McCay-Peet, L., & Toms, E. G. (2013). Proposed facets of a serendipitous digital environment. In Teoksessa iConference 2013 Proceedings, ss. 688–691.Google Scholar
  76. McCay-Peet, L., & Toms, E. G. (2017). Researching serendipity in digital information environments. Synthesis Lectures on Information Concepts, Retrieval, and Services, 9(6), i–i91.CrossRefGoogle Scholar
  77. Meckel, M. (2011). “Sos—save our serendipity”, Personal Blog. https://www.miriammeckel.de/2011/10/11/sos-save-our-serendipity/.
  78. Merton, R. K., & Barber, E. (2006). The travels and adventures of serendipity: A study in sociological semantics and the sociology of science. Princeton: Princeton University Press.Google Scholar
  79. Nagulendra, S., & Vassileva, J. (2016). Providing awareness, explanation and control of personalized filtering in a social networking site. Information Systems Frontiers, 18(1), 145–158.CrossRefGoogle Scholar
  80. Negroponte, N. (1996). Being digital. New York: Vintage.Google Scholar
  81. Olma, S. (2016). In Defence of Serendipity. Watkins Media Limited, 2016.Google Scholar
  82. Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. New York: Penguin.Google Scholar
  83. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard: Harvard University Press.CrossRefGoogle Scholar
  84. Peirce, C. S. (1992). The essential Peirce: Selected philosophical writings (Vol. 2). Indiana: Indiana University Press.zbMATHGoogle Scholar
  85. Pentland, A. (2015). Social physics: How social networks can make us smarter. New York: Penguin.Google Scholar
  86. Pinch, T. J., & Bijker, W. E. (1984). The social construction of facts and artefacts: Or how the sociology of science and the sociology of technology might benefit each other. Social Studies of Science, 14(3), 399–441.CrossRefGoogle Scholar
  87. Polly, J. A. (1992). Surfing the internet. An Introduction. Wilson Library Bulletin, 66(10), 38.Google Scholar
  88. Powers, E. (2017). My news feed is filtered? Awareness of news personalization among college students. Digital Journalism, 5(10), 1315–1335.CrossRefGoogle Scholar
  89. Quattrociocchi, W., Scala, A., & Sunstein, C. R. (2016). Echo chambers on Facebook. Available at SSRN: https://ssrn.com/abstract=2795110.
  90. Race, T., & Makri, S. (2016). Accidental information discovery: Cultivating serendipity in the digital age. Cambridge: Chandos Publishing.Google Scholar
  91. Reviglio, U. (2017). Serendipity by design? How to turn from diversity exposure to diversity experience to face filter bubbles in social media. In International Conference on Internet Science (pp. 281–300). Springer, Cham.Google Scholar
  92. Rubin, V. L., Burkell, J., & Quan-Haase, A. (2011). Facets of serendipity in everyday chance encounters: A grounded theory approach to blog analysis. Information Research, 16(3), 27Google Scholar
  93. Schmidt, E. (2006). How we’re doing and where we’re going. Google Inc. Press Day 2006.Google Scholar
  94. Schönbach, K. (2007). ‘The own in the foreign’: Reliable surprise-an important function of the media? Media, Culture & Society, 29(2), 344–353.CrossRefGoogle Scholar
  95. Sen, A. (1990). Justice: Means versus freedoms. Philosophy & Public Affairs, 19(2), 111–121.MathSciNetGoogle Scholar
  96. Sen, A. (2005). Human rights and capabilities. Journal of Human Development, 6(2), 151–166.MathSciNetCrossRefGoogle Scholar
  97. Shannon, C. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423.MathSciNetzbMATHCrossRefGoogle Scholar
  98. Shearer, E., & Gottfried, J. (2017). News use across social media platforms 2017. Pew Research Center, Journalism and Media.Google Scholar
  99. Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.CrossRefGoogle Scholar
  100. Smith, A. (2018). Many Facebook users don’t understand how the site’s news feed works. Pew Research Center, Journalism and Media.Google Scholar
  101. Stiegler, B. (2017). The new conflict of the faculties and functions: Quasi-causality and serendipity in the anthropocene. Qui Parle, 26(1), 79–99.CrossRefGoogle Scholar
  102. Sunstein, C. R. (2009). Going to extremes: How like minds unite and divide. Oxford: Oxford University Press.Google Scholar
  103. Sunstein, C. R. (2017a). # Republic: Divided Democracy in the Age of Social Media. Princeton: Princeton University Press.CrossRefGoogle Scholar
  104. Sunstein, C. R. (2017b). Default rules are better than active choosing (Often). Trends in Cognitive Sciences, 21(8), 600–606.CrossRefGoogle Scholar
  105. Sunstein, C. R. (2017c). In praise of serendipity in The Economist, Accessed Feb 4, 2018, from https://www.economist.com/news/books-and-arts/21718464-social-media-should-encourage-chance-encounters-not-customised-experiences-praise.
  106. Taleb, N. N. (2012). Antifragile: Things that gain from disorder (Vol. 3). New York: Random House.Google Scholar
  107. Thurman, N. (2011). Making ‘The Daily Me’: Technology, economics and habit in the mainstream assimilation of personalized news. Journalism, 12(4), 395–415.CrossRefGoogle Scholar
  108. Thurman, N., & Schifferes, S. (2012). The future of personalization at news websites: Lessons from a longitudinal study. Journalism Studies, 13(5–6), 775–790.CrossRefGoogle Scholar
  109. Turing, A. M. (1950). ‘Computing machinery and intelligence’. Mind, 59, 433–460.MathSciNetCrossRefGoogle Scholar
  110. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.CrossRefGoogle Scholar
  111. van Andel, P. (1994). Anatomy of the unsought finding. serendipity: Origin, history, domains, traditions, appearances, patterns and programmability. The British Journal for the Philosophy of Science, 45(2), 631–648.CrossRefGoogle Scholar
  112. van den Hoven, J., & Rooksby, E. (2008). Distributive justice and the value of information: A (broadly) Rawlsian approach. Information Technology and Moral Philosophy, p. 376. Cambridge: Cambridge University PressGoogle Scholar
  113. Verbeek, P. P. (2011). Moralizing technology: Understanding and designing the morality of things. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  114. Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113(3), 554–559.CrossRefGoogle Scholar
  115. Winner, L. (1980). Do artifacts have politics? Daedalus, 109(1), 121–136.Google Scholar
  116. Yadamsuren, B., & Erdelez, S. (2016). Incidental exposure to online news. Synthesis Lectures on Information Concepts, Retrieval, and Services, 8(5), i–i73.CrossRefGoogle Scholar
  117. Yamamoto, M., Hmielowski, J., Beam, M., & Hutchens, M. (2018). Skepticism as a political orientation factor: A moderated mediation model of online opinion expression. Journal of Information Technology & Politics, 15(2), 178–192.CrossRefGoogle Scholar
  118. Yaqub, O. (2018). Serendipity: Towards a taxonomy and a theory. Research Policy, 47(1), 169–179.CrossRefGoogle Scholar
  119. Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society, 20(1), 118–136.CrossRefGoogle Scholar
  120. Zarsky, T. Z. (2002). Mine your own business: Making the case for the implications of the data mining of personal information in the forum of public opinion. Yale JL & Tech, 5, 1.Google Scholar
  121. Zuckerman, E. (2013). Rewire: Digital cosmopolitans in the age of connection. New York: W. W. Norton & Company.Google Scholar
  122. Zuiderveen Borgesius, F. J., Trilling, D., Moeller, J., Bodó, B., De Vreese, C. H., & Helberger, N. (2016). Should we worry about filter bubbles? Internet Policy Review. Journal on Internet Regulation, 5(1), 16.Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Law, Science and Technology (ERASMUS+)University of BolognaBolognaItaly

Personalised recommendations