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An invitation to critical social science of big data: from critical theory and critical research to omniresistance

Abstract

How a social science of big data would look like? In this article, we exemplify such a social science through a number of cases. We start our discussion with the epistemic qualities of big data. We point out to the fact that contrary to the big data champions, big data is neither new nor a miracle without any error nor reliable and rigorous as assumed by its cheer leaders. Secondly, we identify three types of big data: natural big data, artificial big data and human big data. We present and discuss in what ways they are similar and in what other ways they differ. The assumption of a homogenous big data in fact misleads the relevant discussions. Thirdly, we extended 3 Vs of the big data and add veracity with reference to other researchers and violability which is the current author’s proposal. We explain why the trinity of Vs is insufficient to characterize big data. Instead, a quintinity is proposed. Fourthly, we develop an economic analogy to discuss the notions of data production, data consumption, data colonialism, data activism, data revolution, etc. In this context, undertaking a Marxist approach, we explain what we mean by data fetishism. Fifthly, we reflect on the implications of growing up with big data, offering a new research area which is called as developmental psychology of big data. Finally, we sketch data resistance and the newly proposed notion of omniresistance, i.e. resisting anywhere at any occasion against the big brother watching us anywhere and everywhere.

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Notes

  1. 1.

    However, we can note a number of articles on big data and sociology, although they did not and were not expected to discuss the other five disciplines mentioned here: Burrows and Savage (2014), Housley et al. (2014), Mützel (2015), Tinati et al. (2014).

  2. 2.

    For ethical issues associated with data collection on social media see Ahmed (2017), Bond et al. (2013), Carter et al. (2016), Fuchs (2017, 2018), Gifkins and Suttor (2013), Golder et al. (2017), Kinder-Kurlanda and Weller (2014); Lee and Wright (2016), Reich (2015), Shilton and Sayles (2016), Townsend and Wallace (2016) and Weller and Kinder-Kurlanda (2014).

  3. 3.

    We can also add more Vs to this definition such as validation, value and voice. By voice, we mean, to what extent the data producer is represented as a subject (datafier) or an object (datafied). We will see more about this in the concluding discussion.

  4. 4.

    The current author is not the first to propose the notion of data colonialism; for an earlier discussion see Thatcher et al. (2016). However, with due respect to the authors, in the current article that notion is connected with the relevant literature and situated within its proper context.

  5. 5.

    For earlier discussions of data fetishism, see Kvale (1976), Sharon and Zandbergen (2017), Thomas Nafus and Sherman (2018) and Zimiles (1993).

  6. 6.

    Furthermore, since the conceptualization of X, Y, Z generations is based on American socio-political events (see Bump 2015), their applicability in other countries is limited. Every country has its own socio-political turning points, thus a generation conceptualization based on American history cannot be universal. For example, a turning point in Turkish history was September 12 military coup in 1980 which brought an apolitical generation contrary to the 68 and 78 generations that were highly politicized to the extent of self-sacrifice. This generation is called as the September 12 generation (Çulhaoğlu 2016). We can also talk about the Gezi generation which had witnessed and actively participated into Gezi Park protests of 2013 in Turkey (see Erdoğan 2015). Likewise, in Vietnamese history, doi moi (đổi mới) of 1986 which means renovation referring to the move towards mixed economy, open market and invitation of foreign capital to the country is a turning point. Thus, we can talk about a doi moi generation or post-war generation who has not witnessed the war. Nevertheless, because of lack of scientific critical skills of practitioners, American model of generation is assumed to be correct in other countries which even leads to self-categorization, organizationally imposed categorizations and self-fulfilling prophesies based on generational stereotypes (see, Gezgin 2017).

  7. 7.

    The notion of veillance is explained in the next paragraphs.

  8. 8.

    SNS stands for social networking sites.

  9. 9.

    For a relevant discussion on whether mathematics is a discovery or invention, see Ernest (1999), Fine (2012), Rowlands and Davies (2006).

  10. 10.

    For some other works as case examples in sociology of science, see Bassett (1999), Bellamy Foster and Clark (2008) and Collins and Restivo (1983).

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Correspondence to Ulaş Başar Gezgin.

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Gezgin, U.B. An invitation to critical social science of big data: from critical theory and critical research to omniresistance. AI & Soc 35, 187–195 (2020). https://doi.org/10.1007/s00146-018-0868-y

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Keywords

  • Philosophy of big data
  • Cognitive science of big data
  • Economics of big data
  • Sociology of big data
  • Psychology of big data
  • Politics of big data