, Volume 12, Issue 3, pp 210–227 | Cite as

Digital audiences and the deconstruction of the collective

  • Laurence BarryEmail author
  • Eran Fisher
Original Article


This paper aims at characterizing the change that occurred in audience conception with the advent of big data technologies. We argue that a good place to analyze this change is in the marketing techniques geared to capturing the characteristics of consumers of contents and goods. Some of these techniques are existing statistical tools applied to new kinds of data, others, like predictive analytics, are radically new. Our contention is that online individual actions are now studied, predicted, and managed in the way macroeconomic parameters were analyzed in the past. By changing the perspective on the individual and the group, these new technologies further transform the manner in which an audience is imagined. The conceptions of modern collectives once defined by top-down, broadly defined demographic categories, are therefore transformed or, rather, deconstructed.


Imagined audiences Digital audiences Big data Algorithms Predictive analytics 



This research was funded by Grant Number 696/16 from the Israel Science Foundation.


  1. Acxiom. 2016. The State of People-Based Marketing. Accessed 1 June 2019.
  2. Arnoux, P.H., A. Xu, N. Boyette, J. Mahmud, R. Akkiraju, and V. Sinha. 2017. 25 Tweets to Know you: A New Model to Predict Personality with Social Media. AAAI Publications, Eleventh International AAAI Conference on Web and Social Media 2017: 472–475.Google Scholar
  3. Bamman, D., J. Eisenstein, and T. Schnoebelen. 2014. Gender Identity and Lexical Variation in Social Media. Journal of Sociolinguistics 18 (2014): 135–160.Google Scholar
  4. Brown, J. 2016. Omni-Channel?
  5. Chen, J., E. Haber, R. Kang, G. Hsieh, and J. Mahmud. 2015. Making Use of Derived Personality: The Case of Social Media Ad Targeting. AAAI Publications, Ninth International AAAI Conference on Web and Social Media, 51–60.Google Scholar
  6. Chen, Y., D. Pavlov, and J.F. Canny. 2009. Large-Scale Behavioral Targeting. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 209–218.Google Scholar
  7. Cheng, H.T., L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anerson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, H. Shah. 2016. Wide and Deep Learning for Recommender Systems. arXiv:1606.07792.
  8. Connexity. 2016. Peek Behind the Curtain: The Basics Behind Connexity Audience Modelling.
  9. Constantinides, E., C.L. Romero, and M. A. Gómez Boria. 2009. Social Media: A New Frontier for Retailers? In European Retail Research, ed. Swoboda, B., D. Morschett, T. Rudolph, P. Schnedlitz, and H. Schramm-Klein, 1–28. European Retail Research. Wiesbaden: Gabler Verlag. Scholar
  10. De Choudhury, M., M. Gamon, S. Counts, and E. Horvitz. 2013. Predicting Depression via Social Media. AAAI Publications, Seventh International AAAI Conference on Weblogs and Social Media 2013: 128–137.Google Scholar
  11. Facebook. 2017. People-Based Marketing: Thinking People-First—Planning and Measurement. Accessed 1 June 2019.
  12. IBM. 2017. Watson Personal Insights. Accessed 27 Sept 2017.
  13. Kosinski, M., D. Stillwell, and T. Graepel. 2013. Private Traits and Attributes are Predictable from Digital Records of Human Behavior. PNAS 110 (15): 5802–5805.Google Scholar
  14. Kosinski, M., Y. Wang, H. Lakkaraju, and J. Leskovec. 2016. Mining Big Data to Extract Patterns and Predict Real-Life Outcomes. Psychological Methods 21 (4): 493–506.Google Scholar
  15. Lambiotte, R., and M. Kosinski. 2014. Tracking the Digital Footprints of Personality. Proceedings of the IEEE 102 (12): 1934–1938.Google Scholar
  16. LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep Learning. Nature 521: 436–444.Google Scholar
  17. Lee, K., J. Mahmud, J. Chen, M. Zhou, J.Nichols. 2014. Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information. Proceedings of the 19th International Conference on Intelligent User Interfaces, 247–256.Google Scholar
  18. Leskovec, J., A. Rajaraman, and J.D. Ullman. 2014. Mining of Massive Datasets. Stanford textbook. Accessed 1 Sept 2017.
  19. Li, W., X. Wang, R. Zhang, Y. Cui, J. Mao, and R. Jin. 2010. Exploitation and Exploration in a Performance Based Contextual Advertising System. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 27–36.Google Scholar
  20. Quantcast. 2016. Understanding Digital Audience Measurements. Accessed 1 June 2019.
  21. Schwartz, H.A., J.C. Eichstaedt, M.L. Kern, L. Dziurzynski, S.M. Ramones, et al. 2013. Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PLoS ONE 8 (9): e73791.Google Scholar
  22. Shmueli, G. 2017. Analyzing Behavioral Big Data: Methodological, Practical, Ethical, and Moral Issues. Quality Engineering 29 (1): 57–74.Google Scholar
  23. Teich, C. 2016. Targeting in the Age of Micro-Segmentation: Good, Better and Best Approaches.
  24. Weed, K. 2017. The Future of Consumer Marketing? The CMO of Unilever Says It’s ‘Consumer Segments of One’.
  25. Yarkoni, T. 2010. Personality in 100,000 Words: A Large-Scale Analysis of Personality and Word Use Among Bloggers. Journal in Research on Personality 44 (3): 363–373.Google Scholar
  26. Youyou, W., M. Kosinski, and D. Stillwell. 2015. Computer-Based Personality Judgments are More Accurate than Those Made by Humans. PNAS 112: 1036–1040.Google Scholar
  27. Zeldes, Y., S. Theodorakis, E. Solodnik, A. Rotman, G. Chamiel, and D. Friedman. 2017. Deep Density Networks and Uncertainty in Recommender Systems arXiv:1711.02487.


  1. Anderson, B. 2006. Imagined Communities. London: Verso [1983].Google Scholar
  2. Anderson, C.W. 2011. Between Creative and Quantified Audiences: Web Metrics and Changing Patterns of Newswork in Local US Newsrooms. Journalism 12 (5): 550–566.Google Scholar
  3. Andrejevic, M., and K. Gates. 2014. Editorial. Big Data Surveillance: Introduction. Surveillance and Society 12 (2): 185–196.Google Scholar
  4. Andrejevic, M. 2016. Theorizing Drones and Droning Theory. In Drones and Unmanned Aerial Systems: Legal and Social Implications for Security and Surveillance, vol. 20, ed. A. Završnik, 43. New York: Springer.Google Scholar
  5. Ang, I. 1991. Desperately Seeking the Audience. Abingdon: Routledge.Google Scholar
  6. Bauman, Z., and D. Lyon. 2013. Liquid Surveillance—A Conversation. Cambridge: Polity.Google Scholar
  7. Blackman, L. 2008. Affect, Relationality and the `Problem of Personality’. Theory, Culture & Society 25 (1): 23–47. Scholar
  8. Blackman, L. 2019. Haunted Data: Affect, Transmedia, Weird Science. London, New York: Bloomsbury Academic.Google Scholar
  9. Blackman, L., J. Cromby, D. Hook, D. Papadopoulos, and V. Walkerdine. 2008. Creating Subjectivities. Subjectivity 22 (1): 1–27. Scholar
  10. Boyd, D., and K. Crawford. 2012. Critical Questions for Big Data. Information, Communication and Society 15 (5): 662–679.Google Scholar
  11. Brown, M. 2017. Understanding the ‘Big Five’ personality traits (OCEAN) and what they mean for marketers and brands. Accessed 1 June 2019.
  12. Cardon, D. 2015. A Quoi rêvent les Algorithmes?. Paris: Seuil.Google Scholar
  13. Cheney-Lippold, J. 2011. A New Algorithmic Identity: Soft Biopolitics and the Modulation of Control. Theory, Culture and Society 28: 164–181.Google Scholar
  14. Cheney-Lippold, J. 2017. We are Data—Algorithms and the Making of Our Digital Selves. New York: New York University Press.Google Scholar
  15. Cover, R. 2006. Audience Inter/Active: Interactive Media, Narrative Control and Reconceiving Audience History. New Media & Society 8 (1): 139–58. Scholar
  16. Desrosières, A. 1988. Masses, Individus, Moyennes: la Statistique Sociale au XIXe siècle. C.N.R.S. Editions | « Hermès, La Revue » 1988/2 no. 2: 41–66.Google Scholar
  17. Desrosières, A. 1998. The Politics of Large Numbers—A History of Statistical Reasoning. Cambridge: Harvard University Press.Google Scholar
  18. Desrosières, A. 2008. Pour une Sociologie Historique de la Quantification—l’Argument Statistique I. Paris: Presses de l’Ecole des Mines.Google Scholar
  19. Desrosières, A. 2014. Prouver et Gouverner—Une Analyse Politique des Statistiques Publiques. Paris: Editions de la Découverte.Google Scholar
  20. Dumont, L. 1983. Essai sur l’Individualisme—une Perspective Anthropologique sur l’Idéologie Moderne. Paris: Seuil.Google Scholar
  21. Ewald, F. 2011. Omnes and Singulatim. After Risk. Carceral Notebooks 7: 78–107.Google Scholar
  22. Foucault, M. 1995. Discipline and Punish. New York: Vintage Books.Google Scholar
  23. Foucault, M. 2009. Security, Territory, Population. Lectures at the Collège de France. 1977-1978, eds. M. Senellart, A.I. Davidson, F. Ewald and A. Fontana, trans. G. Burchell. Basingstoke: Palgrave Mcmillan.Google Scholar
  24. Giraud, Eva. 2015. Subjectivity 2.0: Digital Technologies. Participatory Media and Communicative Capitalism. Subjectivity 8 (2): 124–146. Scholar
  25. Goldberg, L.R. 1993. An Alternative ‘Description of Personality’: The Big-Five Factor Structure. Journal of Personality and Social Psychology 59 (6): 1216–29.Google Scholar
  26. Goriunova, O. 2019a. Digital Subjects: An Introduction. Subjectivity 12 (1): 1–11. Scholar
  27. Goriunova, O. 2019b. Face Abstraction! Biometric Identities and Authentic Subjectivities in the Truth Practices of Data. Subjectivity 12 (1): 12–26. Scholar
  28. Habermas, J. 1991. The Structural Transformation of the Public Sphere—An Inquiry into a Category of Bourgeois Society. Cambridge: MIT Press.Google Scholar
  29. Hartley, J. 2002. Communication, Cultural and Media Studies: The Key Concepts. London: Routledge.Google Scholar
  30. Harvey, B. A Brief Personal History of Media Optimization. Accessed 1 June 2019.
  31. Karakayali, N., Burc K., and Idil G. 2018. Recommendation systems as technologies of the self: Algorithmic control and the formation of music taste. Theory, Culture & Society 35 (2): 3–24.Google Scholar
  32. Kennedy, H., and G. Moss. 2015. Known or Knowing Publics? Social Media Data Mining and the Question of Public Agency. Big Data & Society 2 (2): 1–11. Scholar
  33. Koopman, C. 2019. How We Became Our Data. Chicago: The University of Chicago press.Google Scholar
  34. Litt, E. 2012. Knock, Knock. Who’s There? The Imagined Audience. Journal of Broadcasting & Electronic Media 56 (3): 330–45. Scholar
  35. Livingstone, S. 2003. The Changing Nature of Audiences: From the Mass Audience to the Interactive Media User. In Companion to Media Studies, ed. A. Valdivia, 337–59. Oxford: Blackwell Publishing.Google Scholar
  36. Livingstone, S. 2005. On the Relation Between Audiences and Publics. In: Livingstone, S., (ed.) Audiences and Publics: When Cultural Engagement Matters for the Public Sphere. Changing Media—Changing Europe series (2). Intellect Books, Bristol:17–41.Google Scholar
  37. Marwick, A.E., and D. Boyd. 2011. I Tweet Honestly, I Tweet Passionately: Twitter Users, Context Collapse, and the Imagined Audience. New Media & Society 13 (1): 114–33.Google Scholar
  38. Matthews, Julian. 2008. A Missing Link? The Imagined Audience, News Practices and the Production of Children’s News. Journalism Practice: 264–279.Google Scholar
  39. Mayer-Schönberger, V., and K. Cukier. 2013. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Kindle Android version.Google Scholar
  40. Napoli, Philip M. 2010. Revisiting ‘Mass Communication’ and the ‘Work’ of the Audience in the New Media Environment. Media, Culture and Society 32 (3): 505–16.Google Scholar
  41. Parisi, L. 2016. Automated Thinking and the Limits of Reason. Cultural Studies <=> Critical Methodologies 16 (5): 471–81.Google Scholar
  42. Parisi, L. 2019. The Alien Subject of AI. Subjectivity 12 (1): 27–48.Google Scholar
  43. Pasquale, F. 2015. The Black Box Society: The Secret Algorithms That Control Money and Information. Kindle Android version.Google Scholar
  44. Pentland, A. 2014. Social Physics: How Social Networks Can Make Us Smarter. Penguin, Kindle Edition.Google Scholar
  45. Rouvroy, A. and T. Bern. 2013. Algorithmic Governmentality and the Prospects of Emancipation—Disparateness as a precondition for individuation through relationships?, La Découverte  « Réseaux » , 2013/1 No 177: 163–196.Google Scholar
  46. Scannell, Paddy. 2000. For-Anyone-as-Someone Structures. Media, Culture and Society 22 (1): 5–24.Google Scholar
  47. Siegel, E. 2016. Predictive Analytics—The Power to Predict Who will Click, Buy, Lie or Die. Hoboken: Wiley.Google Scholar
  48. Sternberg, Josh. 2013. “Retailers as Publishers.” Digiday (blog). Accessed 31 July 2013.
  49. Tausczik, Y.L., and J.W. Pennebaker. 2010. The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology 29 (1): 24–54.Google Scholar
  50. Turow, J. 2012. The Daily You: How the New Advertising Industry Is Defining Your Identity and Your Worth. Kindle Android version.Google Scholar
  51. Turow, J., and N. Draper. 2014. Industry Conceptions of Audience in the Digital Space. Cultural Studies 28 (4): 643–656.Google Scholar
  52. Turow, J., L. McGuigan, and E.R. Maris. 2015. Making Data Mining a Natural Part of Life: Physical Retailing, Customer Surveillance and the 21st Century Social Imaginary. European Journal of Cultural Studies 18 (4–5): 464–478.Google Scholar
  53. Zuboff, S. 2015. Big Other: Surveillance Capitalism and the Prospects of an Information Civilization. Journal of Information Technology 2015 (30): 75–89.Google Scholar

Copyright information

© Springer Nature Limited 2019

Authors and Affiliations

  1. 1.Hebrew University of JerusalemJerusalemIsrael
  2. 2.Open UniversityRa’ananaIsrael

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