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

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

  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. The notion of veillance is explained in the next paragraphs.

  8. SNS stands for social networking sites.

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

  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).

References

  • Ahmed W (2017) Using Twitter as a data source: an overview of social media research tools (updated for 2017). Impact of Social Sciences Blog

  • Andrejevic M (2014) Big data, big questions| the big data divide. Int J Commun 8:1673–1689

    Google Scholar 

  • Baack S (2015) Datafication and empowerment: how the open data movement re-articulates notions of democracy, participation, and journalism. Big Data Soc 2(2):2053951715594634

    Google Scholar 

  • Bakir V, Feilzer M, McStay A (2017) Introduction to special theme veillance and transparency: a critical examination of mutual watching in the post-Snowden, Big Data era. Big Data Soc. https://doi.org/10.1177/2053951717698996

    Article  Google Scholar 

  • Bassett K (1999) Is there progress in human geography? The problem of progress in the light of recent work in the philosophy and sociology of science. Prog Hum Geogr 23(1):27–47

    MathSciNet  Google Scholar 

  • Bellamy Foster J, Clark B (2008) The sociology of ecology: ecological organicism versus ecosystem ecology in the social construction of ecological science, 1926–1935. Organ Environ 21(3):311–352

    Google Scholar 

  • Bond CS, Ahmed OH, Hind M, Thomas B, Hewitt-Taylor J (2013) The conceptual and practical ethical dilemmas of using health discussion board posts as research data. J Med Internet Res 15(6):e112

    Google Scholar 

  • boyd d, Crawford K (2012) Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inf Commun Soc 15(5):662–679

    Google Scholar 

  • Bump A (2015) Your generational identity is a lie. The Washington Post, April 1, 2015. Accessed https://www.washingtonpost.com/news/the-fix/wp/2015/04/01/your-generational-identity-is-a-lie/?utm_term=.4ead11cda61d

  • Burrows R, Savage M (2014) After the crisis? Big Data and the methodological challenges of empirical sociology. Big Data Soc 1(1):2053951714540280

    Google Scholar 

  • Carter CJ, Koene A, Perez E, Statache R, Adolphs S, O’Malley C, McAuley D (2016) Understanding academic attitudes towards the ethical challenges posed by social media research. ACM SIGCAS Comput Soc 45(3):202–210

    Google Scholar 

  • Collins R, Restivo S (1983) Development, diversity, and conflict in the sociology of science. Sociol Q 24(2):185–200

    Google Scholar 

  • Coté M, Gerbaudo P, Pybus J (2016) Introduction. Politics of big data. Digit Cult Soc 2(2):5–18

    Google Scholar 

  • Creemers R (2018) China’s social credit system: an evolving practice of control. http://www.iberchina.org/files/2018/social_credit_china.pdf. Accessed 17 Sept 2018

  • Çulhaoğlu M (2016) Kuşak Sancısı [Generation Pains]. İleri Haber, April 2nd, 2016. Accessed https://ilerihaber.org/yazar/kusak-sancisi-52754.html

  • Dalton CM, Taylor L, Thatcher J (2016) Critical data studies: a dialog on data and space. Big Data Soc 3(1):2053951716648346

    Google Scholar 

  • Dencik L, Hintz A, Cable J (2016) Towards data justice? The ambiguity of anti-surveillance resistance in political activism. Big Data Soc 3(2):2053951716679678

    Google Scholar 

  • Erdoğan E (2015) Siyasal psikoloji siyasal katılım hakkında ne öğretebilir? Gezi Protestoları’na katılanlar üzerinden bir değerlendirme [What can we learn from political psychology about political participation: a qualitative fieldwork with “Gezi” protestors]. Marmara Üniversitesi Siyasal Bilimler Dergisi 3(1):31–58. https://doi.org/10.14782/SBD.2015112077

    Article  MathSciNet  Google Scholar 

  • Ernest P (1999) Is mathematics discovered or invented. Philos Math Educ J 12:9–13

    Google Scholar 

  • Fine K (2012) Mathematics: discovery or invention? Think 11(32):11–27

    Google Scholar 

  • Fuchs C (2017) From digital positivism and administrative big data analytics towards critical digital and social media research! Eur J Commun 32(1):37–49

    Google Scholar 

  • Fuchs C (2018) “Dear Mr. Neo-Nazi, can you please give me your informed consent so that i can quote your fascist tweet?”: questions of social media research ethics in online ideology critique. Accessed http://westminsterresearch.wmin.ac.uk/21070/

  • Gabrys J, Pritchard H, Barratt B (2016) Just good enough data: figuring data citizenships through air pollution sensing and data stories. Big Data Soc 3(2):2053951716679677

    Google Scholar 

  • Gassel HJ, Horak KH, Schang T, Timmermann W, Fuchs KH, Thiede A (1998) Peritonitis caused by multi-and omniresistant bacterial strains. Surgical and intensive care management. Chir Praxis 54(3):371–377

    Google Scholar 

  • Gezgin UB (2017) Eleştirel Psikolojide Bir Yolculuk: Marksist Psikolojiden Politik Psikolojiye ve Ötesine [A Journey through Critical Psychology: From Marxist Psychology to Political Psychology and Beyond]. http://ulasbasargezginkulliyati.blogspot.com/p/elestirel-psikolojide-bir-yolculuk.html. Accessed 17 Sept 2018

  • Gezgin UB (2018) Marxist psychology—a short introduction. Lambert, Germany. https://www.amazon.fr/Marxist-Psychology-Ulas-Basar-Gezgin/dp/6139832675

  • Gifkins K, Suttor N (2013) Social media, research ethics and your research. http://ro.ecu.edu.au/creswk/62/. Accessed 17 Sept 2018

  • Gill KS (2013) The Internet of things! then what? AI Soc 28(4):367–371

    Google Scholar 

  • Golder S, Ahmed S, Norman G, Booth A (2017) Attitudes toward the ethics of research using social media: a systematic review. J Med Internet Res 19(6):e195

    Google Scholar 

  • Gray J, Gerlitz C, Bounegru L (2018) Data infrastructure literacy. Big Data Soc 5(2):2053951718786316

    Google Scholar 

  • Houghton S, Lawrence D, Hunter SC, Rosenberg M, Zadow C, Wood L, Shilton T (2018) Reciprocal relationships between trajectories of depressive symptoms and screen media use during adolescence. J Youth Adolesc. https://doi.org/10.1007/s10964-018-0901-y

    Article  Google Scholar 

  • Housley W, Procter R, Edwards A, Burnap P, Williams M, Sloan L, Rana O, Morgan J, Voss A, Greenhill A (2014) Big and broad social data and the sociological imagination: a collaborative response. Big Data Soc 1(2):2053951714545135

    Google Scholar 

  • Ju-young H, Yoon-ji K (2013) Factors influencing self-confidence in the maternal role among early postpartum mothers. Korean J Women Health Nurs 19(1):48–56

    Google Scholar 

  • Kant I (1781) Critique of pure reason. Cambridge University Press, Oxford

    Google Scholar 

  • Kinder-Kurlanda K, Weller K (2014) I always feel it must be great to be a hacker!: the role of interdisciplinary work in social media research. In: Proceedings of the 2014 ACM conference on web science. ACM, pp 91–98

  • Kitchin R (2014) Big data, new epistemologies and paradigm shifts. Big Data Soc 1(1):2053951714528481

    Google Scholar 

  • Kolikant YBD (2010) Digital natives, better learners? Students’ beliefs about how the Internet influenced their ability to learn. Comput Hum Behav 26(6):1384–1391

    Google Scholar 

  • Kotska G (2018) China’s social credit systems and public opinion: explaining high levels of approval. Accessed https://www.researchgate.net/profile/Genia_Kostka/publication/326625329_China%27s_Social_Credit_Systems_and_Public_Opinion_Explaining_High_Levels_of_Approval/links/5b5ae52a0f7e9bc79a6717b7/Chinas-Social-Credit-Systems-and-Public-Opinion-Explaining-High-Levels-of-Approval.pdf. Accessed 17 Sept 2018

  • Kuhn TS (2012) The structure of scientific revolutions. University of Chicago Press, Chicago

    Google Scholar 

  • Kuyucu M (2016) The social media generation: social media use in Turkey in the sample of Istanbul. IOSR J Humanit Soc Sci 21(2):84–98

    Google Scholar 

  • Kvale S (1976) Meanings as data and human technology. Scand J Psychol 17(1):171–180

    Google Scholar 

  • Lauricella AR, Cingel DP, Blackwell C, Wartella E, Conway A (2014) The mobile generation: youth and adolescent ownership and use of new media. Commun Res Rep 31(4):357–364

    Google Scholar 

  • Lee M, Wright E (2016) Ethical issues in (online) social network research in education. J Cyber Educ 10(1):9–14

    Google Scholar 

  • Lukoianova T, Rubin VL (2014) Veracity roadmap: is big data objective, truthful and credible? Adv Classif Res Online. https://doi.org/10.7152/acro.v24i1.14671

    Article  Google Scholar 

  • Lyon D (2014) Surveillance, snowden, and big data: capacities, consequences, critique. Big Data Soc 1(2):2053951714541861

    Google Scholar 

  • Marengo D, Longobardi C, Fabris MA, Settanni M (2018) Highly-visual social media and internalizing symptoms in adolescence: the mediating role of body image concerns. Comput Hum Behav 82:63–69

    Google Scholar 

  • Milan S, Gutiérrez M (2015) Citizens’ media meets big data: the emergence of data activism. Mediaciones 11(14):120–133

    Google Scholar 

  • Milan S, Van Der Velden L (2016) The alternative epistemologies of data activism. Digit Cult Soc 2(2):57–74

    Google Scholar 

  • Mützel S (2015) Facing big data: making sociology relevant. Big Data Soc 2(2):2053951715599179

    Google Scholar 

  • Nagl M, Pfausler B, Schmutzhard E, Fille M, Gottardi W (1998) Tolerance and bactericidal action of N-chlorotaurine in a urinary tract infection by an omniresistant Pseudomonas aeruginosa. Zentralblatt für Bakteriologie 288(2):217–223

    Google Scholar 

  • Nesi J, Prinstein MJ (2015) Using social media for social comparison and feedback-seeking: gender and popularity moderate associations with depressive symptoms. J Abnorm Child Psychol 43(8):1427–1438

    Google Scholar 

  • Rajão R, Jarke J (2018) The materiality of data transparency and the (re) configuration of environmental activism in the Brazilian Amazon. Soc Mov Stud 17(3):318–332

    Google Scholar 

  • Reich JA (2015) Old methods and new technologies: social media and shifts in power in qualitative research. Ethnography 16(4):394–415

    Google Scholar 

  • Rowlands S, Davies A (2006) Mathematics masterclass: is mathematics discovered or invented? Math Sch 35(2):2–6

    Google Scholar 

  • Roy J (2014) Open data and open governance in Canada: a critical examination of new opportunities and old tensions. Future Internet 6(3):414–432

    Google Scholar 

  • Schrock AR (2016) Civic hacking as data activism and advocacy: a history from publicity to open government data. New Media Soc 18(4):581–599

    Google Scholar 

  • Schrock A, Shaffer G (2017) Data ideologies of an interested public: a study of grassroots open government data intermediaries. Big Data Soc 4(1):2053951717690750

    Google Scholar 

  • Schroeder R (2014) Big data and the brave new world of social media research. Big Data Soc 1(2):2053951714563194

    Google Scholar 

  • Sha XW, Carotti-Sha G (2016) Big data. AI Soc. https://doi.org/10.1007/s00146-016-0662-7

    Article  Google Scholar 

  • Shapiro LAS, Margolin G (2014) Growing up wired: social networking sites and adolescent psychosocial development. Clin Child Fam Psychol Rev 17(1):1–18

    Google Scholar 

  • Sharon T, Zandbergen D (2017) From data fetishism to quantifying selves: self-tracking practices and the other values of data. New Media Soc 19(11):1695–1709

    Google Scholar 

  • Shilton K, Sayles S (2016) “We Aren’t All Going to Be on the Same Page about Ethics”: ethical practices and challenges in research on digital and social media. In: System sciences (HICSS), 2016 49th Hawaii international conference. IEEE, pp 1909–1918

  • Thatcher J, O’Sullivan D, Mahmoudi D (2016) Data colonialism through accumulation by dispossession: new metaphors for daily data. Environ Plan D Soc Sp 34(6):990–1006

    Google Scholar 

  • Thomas SL, Nafus D, Sherman J (2018) Algorithms as fetish: faith and possibility in algorithmic work. Big Data Soc 5(1):2053951717751552

    Google Scholar 

  • Tiggemann M, Slater A (2014) NetTweens: the internet and body image concerns in preteenage girls. J Early Adolesc 34(5):606–620

    Google Scholar 

  • Tinati R, Halford S, Carr L, Pope C (2014) Big data: methodological challenges and approaches for sociological analysis. Sociology 48(4):663–681

    Google Scholar 

  • Townsend L, Wallace C (2016) Social media research: a guide to ethics. University of Aberdeen, 1–16. Accessed Social media research: a guide to ethics. University of Aberdeen. https://www.gla.ac.uk/media/media_487729_en.pdf

  • Van Dijck J (2014) Datafication, dataism and dataveillance: big data between scientific paradigm and ideology. Surveill Soc 12(2):197–208

    Google Scholar 

  • Vossen HG, Valkenburg PM (2016) Do social media foster or curtail adolescents’ empathy? A longitudinal study. Comput Hum Behav 63:118–124

    Google Scholar 

  • Weller K, Kinder-Kurlanda K (2014) “I love thinking about ethics!”: Perspectives on ethics in social media research. AoIR Selected Papers of Internet Research, vol 4. https://spir.aoir.org/index.php/spir/article/viewFile/997/661. Accessed 17 Sept 2018

  • Xu J, Wang J, Xuan S, Fang G, Tian J, Teng Y (2018) The effects of childbirth age on maternal and infant outcomes in pregnant women. Iran J Public Health 47(6):788–793

    Google Scholar 

  • Zimiles H (1993) The adoration of “hard data”: a case study of data fetishism in the evaluation of infant day care. Early Child Res Q 8(3):369–385

    Google Scholar 

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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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