Platform enclosure of human behavior and its measurement: Using behavioral trace data against platform episteme

Abstract

Digital trace data from giant platforms are gaining ground in the study of human behavior. This trend accompanies contestations regarding representativeness, privacy, access, and commercial origin. Complementing existing discussions and focusing on knowledge production, we draw attention to the different measurement regimes within passively captured behavioral logs from industries. Taking an institutional perspective on measurement as a management technology, we compare platforms with third-party audience measurement firms. Whereas the latter measure to provide “currency” for a multi-sided advertising market, the former measure internally for their own administrative purposes (i.e. prescribing behavior through design). We demonstrate the platform giants’ two-fold enclosure of first the user ecology and subsequently the previously open market for user attention. With platform trace data serving as a lifeline for scholarly research, platform episteme extends itself to enclose knowledge production. We conclude by suggesting ways in which academic quantitative social sciences may resist these platform enclosures.

Keywords

Audience measurement computational social science digital trace data Internet studies knowledge production platform episteme quantitative methodology user analytics

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References

Acker

Donovan

(2019) Data craft: a theory/methods package for critical internet studies. Information, Communication & Society 22(11): 1590–1609.

Anand

Peterson

(2000) When market information constitutes fields: sensemaking of markets in the commercial music industry. Organization Science 11(3): 270–284.

Ananny

(2019) Probably Speech, Maybe Free: Toward a Probabilistic Understanding of Online Expression and Platform Governance. Available at: https://knightcolumbia.org/content/probably-speech-maybe-free-toward-a-probabilistic-understanding-of-online-expression-and-platform-governance

Andrejevic

(2013) Infoglut: How Too Much Information Is Changing the Way We Think and Know. New York: Routledge.

Andrews

Napoli

(2006) Changing market information regimes: a case study of the transition to the BookScan audience measurement system in the US book publishing industry. Journal of Media Economics 19(1): 33–54.

Bail

Brown

Wimmer

(2019) Prestige, proximity, and prejudice: how Google search terms diffuse across the world. American Journal of Sociology 124(5): 1496–1548.

Balnaves

O’Regan

Goldsmith

(2011) Rating the Audience: The Business of Media. London: A&C Black.

Barwise

Ehrenberg

(1988) Television and Its Audience. London: SAGE.

Baym

(2013) Data not seen: the uses and shortcomings of social media metrics. First Monday 18(10). Available at: https://journals.uic.edu/ojs/index.php/fm/article/view/4873/3752

10.

Beville

(1988) Audience Ratings: Radio, Television, and Cable. London: Psychology Press.

11.

boyd

Crawford

(2012) Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society 15(5): 662–679.

12.

Bruns

(2019) After the “APIcalypse”: social media platforms and their fight against critical scholarly research. Information, Communication & Society 22(11): 1544–1566.

13.

Brunton

(2019) Hello from earth. In: Bialski

Brunton

Bunz

(eds) Communication. Minneapolis, MN: University of Minnesota Press, pp. 1–49.

14.

Busch

(2016) Looking in the wrong (la)place? The promise and perils of becoming big data. Science, Technology & Human Values 42(4): 657–678.

15.

Comaroff

(1992) Ethnography and the historical Imagination. In: Comaroff

Comaroff

(eds) Ethnography and the Historical Imagination. Boulder, CO: Westview Press, pp. 3–48.

16.

Confessore

Dance

GJX

Harris

, et al. (2018) The follower factory. The New York Times, 27 January. Available at: https://www.nytimes.com/interactive/2018/01/27/technology/social-media-bots.html

17.

Daston

Galison

(2007) Objectivity. Cambridge: Zone Books.

18.

Ehrenberg

ASC

(1968) The factor analytic search for program types. Journal of Advertising Research 8(1): 55–63.

19.

Eslami

Karahalios

Sandvig

, et al. (2016) First I “like” it, then I hide it: folk theories of social feeds. In: Proceedings of the 2016 CHI conference on human factors in computing systems. Available at: https://dl.acm.org/citation.cfm?id=2858494

20.

Espeland

Stevens

(2008) A sociology of quantification. European Journal of Sociology 49(3): 401–436.

21.

Furchtgott-Roth

Hahn

Layne-Farrar

(2007) The law and economics of regulating ratings firms. Journal of Competition Law & Economics 3(1): 49–96.

22.

Galison

(1997) Image and Logic: A Material Culture of Microphysics. Chicago, IL: The University of Chicago Press.

23.

Gehl

(2014) Reverse Engineering Social Media. Philadelphia, PA: Temple University Press.

24.

Gentzkow

Shapiro

(2011) Ideological segregation online and offline. The Quarterly Journal of Economics 126(4): 1799–1839.

25.

Gerrard

(2018) Beyond the hashtag: circumventing content moderation on social media. New Media & Society 20(12): 4492–4511.

26.

Gillespie

(2010) The politics of “platforms.” New Media & Society 12(3): 347–364.

27.

Gillespie

(2016) #Trendingistrending: when algorithms become culture. In: Seyfert

Roberge

(eds) Algorithmic Cultures: Essays on Meaning, Performance and New Technologies. New York: Routledge, pp. 52–75.

28.

Grind

Schechner

McMillan

, et al. (2019) How Google interferes with its search algorithms and changes your results. The Wall Street Journal, 15 November. Available at: https://www.wsj.com/articles/how-google-interferes-with-its-search-algorithms-and-changes-your-results-11573823753

29.

Grosser

(2014) What do metrics want? How quantification prescribes social interaction on Facebook. Computational Culture 4. Available at: http://computationalculture.net/what-do-metrics-want/

30.

Harding

(1993) Rethinking standpoint epistemology: what is “strong objectivity”? In: Alcoff

Potter

(eds) Feminist Epistemologies. New York: Routledge, pp. 49–82.

31.

Harding

(2005) Negotiating with the positivist legacy: new social justice movements and a standpoint politics of method. In: Steinmetz

(ed.) The Politics of Method in the Human Sciences: Positivism and Its Epistemological Others. Durham, NC: Duke University Press, pp. 346–365.

32.

Hargittai

(2020) Potential biases in big data: omitted voices on social media. Social Science Computer Review 38(1): 10–24.

33.

Heyck

(2015) Age of System: Understanding the Development of Modern Social Science. Baltimore, MD: Johns Hopkins University Press.

34.

Hindman

(2018) The Internet Trap: How the Digital Economy Builds Monopolies and Undermines Democracy. Princeton, NJ: Princeton University Press.

35.

Joseph

(2018) ‘Organic reach on Facebook is dead’: advertisers expect price hikes after Facebook’s feed purge. Digiday, 15 January. Available at: https://digiday.com/marketing/organic-reach-facebook-dead-advertisers-will-spend-reach-facebooks-feed-purge/

36.

Joseph

(2020) Ebiquity tries to pivot via the acquisition of digital advisory firm digital decisions. Digiday, 8 January. Available at: https://digiday.com/marketing/ebiquity-acquires-digital-advisory-firm-digital-decisions/

37.

Karpf

(2012) Social science research methods in Internet time. Information, Communication & Society 15(5): 639–661.

38.

Karppi

(2018) Disconnect: Facebook’s Affective Bonds. Minneapolis, MN: University of Minnesota Press.

39.

King

(2011) Ensuring the data-rich future of the social sciences. Science 331(6018): 719–721.

40.

King

Pan

Roberts

(2017) How the Chinese government fabricates social media posts for strategic distraction, not engaged argument. American Political Science Review 111(3): 484–501.

41.

Kramer

ADI

Guillory

Hancock

(2014) Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences of the United States of America 111(24): 8788–8790.

42.

Lazer

Pentland

Adamic

, et al. (2009) Computational social science. Science 323(5915): 721–723.

43.

Lingel

(2019) Notes from the web that was: the platform politics of craigslist. Surveillance & Society 17(1–2): 21–26.

44.

McGuigan

(2019) Automating the audience commodity: the unacknowledged ancestry of programmatic advertising. New Media & Society 21: 2366–2385.

45.

Mayer-Schönberger

Cukier

(2013) Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: Houghton Mifflin Harcourt.

46.

Milavsky

(1992) How good is the A. C. Nielsen people-meter system? A review of the report by the committee on nationwide television audience measurement. Public Opinion Quarterly 56(1): 102–115.

47.

Napoli

(2005) Audience measurement and media policy: audience economics, the diversity principle, and the local people meter. Communication Law and Policy 10(4): 349–382.

48.

Napoli

(2019) What social media platforms can learn from audience measurement: lessons in the self-regulation of “black boxes.” First Monday 24(12). Available at: https://firstmonday.org/ojs/index.php/fm/article/view/10124/8288

49.

YMM

Taneja

(2019) Mapping user-centric internet geographies: how similar are countries in their web use patterns? Journal of Communication 69(5): 467–489.

50.

Nielsen

(1945) Two years of commercial operation of the audimeter and the Nielsen Radio Index. Journal of Marketing 9(3): 239–255.

51.

Penner

Dodge

(2019) Using administrative data for social science and policy. The Russell Sage Foundation Journal of the Social Sciences 5(2): 1–18.

52.

Petre

Duffy

Hund

(2019) “Gaming the system”: platform paternalism and the politics of algorithmic visibility. Social Media + Society 5(4): 1–12. DOI: 10.1177/2056305119879995.

53.

Plantin

J-C

Lagoze

Edwards

, et al. (2018) Infrastructure studies meet platform studies in the age of Google and Facebook. New Media & Society 20(1): 293–310.

54.

Porter

(1995a) Information cultures: a review essay. Accounting, Organizations and Society 20(1): 83–92.

55.

Porter

(1995b) Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton, NJ: Princeton University Press.

56.

Prior

(2009) The immensely inflated news audience: assessing bias in self-reported news exposure. Public Opinion Quarterly 73(1): 130–143.

57.

Public Opinion Quarterly (2019) New Data in Social and Behavioral Research. Available at: https://academic.oup.com/poq/pages/cfp_new_data_si

58.

Puschmann

(2019) An end to the wild west of social media research: a response to Axel Bruns. Information, Communication & Society 22(11): 1582–1589.

59.

Rosenblat

Stark

(2016) Algorithmic labor and information asymmetries: a case study of Uber’s drivers. International Journal of Communication 10: 3758–3784.

60.

RTI International (2016) CRE Guide for Validating New and Modeled Audience Data v.1.0. Available at: http://www.researchexcellence.com/files/pdf/2016-11/id402_guide_for_validating_audience_data_2016_11_11.pdf

61.

Salganik

(2019) Bit by Bit: Social Research in the Digital Age. Princeton, NJ: Princeton University Press.

62.

Salganik

Dodds

Watts

(2006) Experimental study of inequality and unpredictability in an artificial cultural market. Science 311(5762): 854–856.

63.

Sandvig

Hamilton

Karahalios

, et al. (2014) Auditing algorithms: research methods for detecting discrimination on internet platforms. In: Proceedings of the data and discrimination: converting critical concerns into productive inquiry, Seattle, WA, 22 May. Available at: https://pdfs.semanticscholar.org/b722/7cbd34766655dea10d0437ab10df3a127396.pdf

64.

Scharkow

Mangold

Stier

, et al. (2020) How social network sites and other online intermediaries increase exposure to news. Proceedings of the National Academy of Sciences of the United States of America 117(6): 2761–2763.

65.

Seaver

(2019) Captivating algorithms: recommender systems as traps. Journal of Material Culture 24(4): 421–436.

66.

Social Science One (2019) Public Statement from the Co-Chairs and European Advisory Committee of Social Science One. Available at: https://socialscience.one/blog/public-statement-european-advisory-committee-social-science-one

67.

Srnicek

(2016) Platform Capitalism. Cambridge: Polity Press.

68.

Steinmetz

(2005) The Politics of Method in the Human Sciences: Positivism and Its Epistemological Others. Durham, NC: Duke University Press.

69.

Taneja

(2013) Audience measurement and media fragmentation: revisiting the monopoly question. Journal of Media Economics 26(4): 203–219.

70.

Taneja

(2016) Using commercial audience measurement data in academic research. Communication Methods and Measures 10(2–3): 176–178.

71.

Taneja

(2014) Does the great firewall really isolate the Chinese? Integrating access blockage with cultural factors to explain web user behavior. The Information Society 30(5): 297–309.

72.

Tang

Lee

FLF

(2018) Social media campaigns, electoral momentum, and vote shares: evidence from the 2016 Hong Kong Legislative Council election. Asian Journal of Communication 28(6): 579–597.

73.

Van Dijck

(2014) Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society 12(2): 197–208.

74.

Webster

(2014) The Marketplace of Attention: How Audiences Take Shape in a Digital Age. Cambridge, MA: MIT Press.

75.

Webster

Ksiazek

(2012) The dynamics of audience fragmentation: public attention in an age of digital media. Journal of Communication 62(1): 39–56.

76.

Webster

Phalen

(1997) The Mass Audience: Rediscovering the Dominant Model. Mahwah, NJ: Lawrence Erlbaum.

77.

Taneja

(2019) How did the data extraction business model come to dominate? Changes in the web use ecosystem before mobiles surpassed personal computers. The Information Society 35(5): 272–285.

78.

Zhu

JJH

Zhou

Guan

, et al. (2019) Applying user analytics to uses and effects of social media in China. Asian Journal of Communication 29(3): 291–306.

79.

Zuboff

(2019) The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: Profile Books.