Identifying the Affective Dimension of Data Mining Practice: An Exploratory Study

  • Jo BatesEmail author
  • Jess Elmore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10766)


The paper aims to illuminate how feeling, emotion and affect influence the practice of data mining. While data mining is sometimes presented as an objective and neutral technique by which to rationally understand and predict phenomena, we observe that there is an important affective dimension in how people understand, engage in and respond to data mining practices. We report the findings of a small exploratory pilot study conducted in 2016 in which we used ethnographic methods to observe the culture of a collaborative project between data scientists and a small digital marketing company. The project aimed to explore potential uses of data mining techniques in the process of telesales lead generation. Thematic analysis of collected data indicates that even in the case of a small scale project, the practice of mining data is deeply influenced by an underlying affective dimension. While these affective dynamics rarely surfaced explicitly in discussions between team members, it is clear from our interview data that feelings and emotions had a significant impact on how participants experienced and engaged with the practice of data mining. Our findings point to the necessity for a much deeper understanding of, and reflexivity in relation to, the affective dimension of data mining practice and how it emerges in the cultures and practices of data science projects. We argue that a deeper awareness of, and openness about, this affective dimension could benefit practitioners’ understanding of their own practice and motivations in decision making, and thus has the potential to improve data science practice.


Data mining Data science Data practices Emotion Affect 


  1. 1.
    Ahmed, S.: The Cultural Politics of Emotion. Edinburgh University Press, Edinburgh (2004)Google Scholar
  2. 2.
    Albright, K.: Psychodynamic perspectives in information behaviour. Inf. Res. 16(1), 9 (2011)Google Scholar
  3. 3.
    Amore, L.: Biometric borders: Governing mobilities in the war on terror. Polit. Geogr. 25(3), 336–351 (2006)CrossRefGoogle Scholar
  4. 4.
    Andrejevic, M.: Infoglut: How Too Much Information is Changing the Way We Think and Know. Routledge, New York (2013)Google Scholar
  5. 5.
    Barocas, S., Selbst, A.D.: Big data’s disparate impact. Calif. Law Rev. 104, 671 (2014)Google Scholar
  6. 6.
    Coenen, F.: Data mining: past, present and future. Knowl. Eng. Rev. 26(1), 25–29 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Edwards, P., Mayernik, M., Batcheller, A., Bowker, G., Borgman, C.: Science friction: data, metadata, and collaboration. Soc. Stud. Sci. 41(5), 667–690 (2011)CrossRefGoogle Scholar
  8. 8.
    Gillespie, T.: The relevance of algorithms. In: Media Technologies: Essays on Communication, Materiality, and Society. MIT Press, Cambridge (2014)Google Scholar
  9. 9.
    Kahneman, D.: Thinking, Fast and Slow. Penguin, London (2012)Google Scholar
  10. 10.
    Kennedy, H., Bates, J.: Data power in material contexts: introduction. Telev. New Media 18, 701–705 (2017)CrossRefGoogle Scholar
  11. 11.
    Kennedy, H., Hill, R.: The feeling of numbers: emotions in everyday engagements with data and their visualisation. Sociology (2017)Google Scholar
  12. 12.
    Kennedy, H.: Post, Mine, Repeat: Social Media Data Mining Becomes Ordinary. Palgrave, London (2016)CrossRefGoogle Scholar
  13. 13.
    Kerr, A., Garforth, L.: Affective practices, care and bioscience: a study of two laboratories. Sociol. Rev. 64(1), 3–20 (2016)CrossRefGoogle Scholar
  14. 14.
    Kirschenbaum, M.: The Remaking of Reading: Data Mining and the Digital Humanities (2009). Accessed 6 Sept 2017
  15. 15.
    Kitchin, R.: The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. SAGE, London (2014)Google Scholar
  16. 16.
    Kitchin, R., Lauriault, T.P.: Towards critical data studies: charting and unpacking data assemblages and their work. The Programmable City Working Paper 2; pre-print version of chapter to be published in Eckert, J., Shears, A., Thatcher, J. (eds.) Geoweb and Big Data. University of Nebraska Press (2014), Forthcoming. SSRN:
  17. 17.
    Lorimer, J.: Counting corncrakes: the affective science of the UK corncrake census. Soc. Stud. Sci. 38(3), 377–405 (2008)CrossRefGoogle Scholar
  18. 18.
    Mackenzie, A.: Machine Learners: Archaeology of a Data Practice. MIT Press, Cambridge (2017)Google Scholar
  19. 19.
    Mackenzie, A.: The production of prediction: what does machine learning want? Eur. J. Cult. Stud. 18(4–5), 429–445 (2015)CrossRefGoogle Scholar
  20. 20.
    Mittelstadt, B.D., Allo, P., Taddeo, M., Wachter, S., Floridi, L.: The ethics of algorithms: mapping the debate. Big Data Soc. 3(2), 1–21 (2016)CrossRefGoogle Scholar
  21. 21.
    Nahl, D., Bilal, D. (eds.): Information and Emotion: The emergent Affective Paradigm in Information Behavior Research and Theory. Information Today, Medford (2007)Google Scholar
  22. 22.
    Neyland, D.: On organising algorithms. Theory Cult. Soc. 32(1), 119–132 (2014)CrossRefGoogle Scholar
  23. 23.
    O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, New York (2016)Google Scholar
  24. 24.
    Ruckenstein, M.: Visualized and interacted life: personal analytics and engagements with data doubles. Societies 4(1), 68–84 (2014)CrossRefGoogle Scholar
  25. 25.
    Savage, M.: The ‘social life of methods’: a critical introduction. Theory Cult. Soc. 30(4), 3–21 (2013)CrossRefGoogle Scholar
  26. 26.
    Shouse, E.: Feeling, emotion and affect. Media Cult. J. 8(6) (2005).

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Information SchoolUniversity of SheffieldSheffieldUK

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