Share:


Creating students’ algorithmic selves: shedding light on social media’s representational affordances

    Ignas Kalpokas   Affiliation
    ; Emilija Sabaliauskaitė   Affiliation
    ; Victoria Pegushina   Affiliation

Abstract

This article presents and analyses the results of focus group studies conducted with students at an international university in Lithuania, interpreting the results in light of the extant literature on social media’s impact on the creation and performance of the self. The authors reveal a mixed picture whereby the respondents seem to demonstrate an unexpectedly casual and cynical attitude towards social media while, upon closer inspection, still remaining part of social media’s productive exchanges, contributing their data and attention in return for satisfaction. Hence, while by no means rejecting the standard interpretation provided in mainstream literature, the authors are able to present a more complex and nuanced picture of young people’s attitudes towards and interaction with social media and the self-creation affordances thereof, ultimately a close, constitutive, and creative interrelationship between humans and code.


Santrauka


Šiame straipsnyje pristatomi ir analizuojami rezultatai, gauti iš tikslinių grupių interviu su Lietuvoje esančio tarptautinio universiteto studentais. Šie rezultatai interpretuojami literatūros, aptariančios socialinių medijų poveikį savęs kūrimui ir raiškai, kontekste. Autoriai atskleidžia prieštaringą paveikslą – respondentai demonstruoja netikėtai atsainų ir net cinišką požiūrį į socialines medijas, tačiau, pažvelgus giliau, vis vien išlieka socialinių medijų produkcijos santykių dalimi, atiduodami savo duomenis mainais į pasitenkinimą. Tad, nors ir neatmesdami literatūroje dominuojančio požiūrio, autoriai pristato sudėtingesnį ir labiau niuansuotą požiūrį į jaunų žmonių nuomonę apie socialines medijas bei jų poveikį savęs kūrimui. Tokiu būdu parodomas atviras ir kūrybiškas santykis tarp žmogiškųjų aktorių ir programinio kodo.


Reikšminiai žodžiai: savastys, aglomeracija, algoritmas, dėmesys, duomenys, savikūra, socialinės medijos

Keyword : affordances, agglomeration, algorithm, attention, data, self-creation, social media

How to Cite
Kalpokas, I., Sabaliauskaitė, E., & Pegushina, V. (2020). Creating students’ algorithmic selves: shedding light on social media’s representational affordances. Creativity Studies, 13(2), 292-307. https://doi.org/10.3846/cs.2020.10803
Published in Issue
May 4, 2020
Abstract Views
600
PDF Downloads
87
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Beer, D. (2019). The data gaze: Capitalism, power and perception. Series: Society and Space. R. Rojek (Ed.). SAGE. https://doi.org/10.4135/9781526463210

Boyd, D. (2011). Social network sites as networked publics: Affordances, dynamics, and implications. In Z. Papacharissi (Ed.), A networked self: Identity, community, and culture on social network sites (pp. 39–58). Routledge.

Bucher, T. (2018). If… Then: Algorithmic power and politics. Series: Oxford Studies in Digital Politics. A. Chadwick (Ed.). Oxford University Press. https://doi.org/10.1093/oso/9780190493028.001.0001

Carlson, M. (2018). Automating judgment? Algorithmic judgment, news knowledge, and journalistic professionalism. New Media and Society, 20(5), 1755–1772. https://doi.org/10.1177/1461444817706684

Choat, S. (2018). Science, agency and ontology: A historical-materialist response to new materialism. Political Studies, 66(4), 1027–1042. https://doi.org/10.1177/0032321717731926

Citton, Y. (2017). The ecology of attention. Polity Press.

Cotter, K. (2019). Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram. New Media and Society, 21(4), 895–913. https://doi.org/10.1177/1461444818815684

Cover, R. (2016). Digital identities: Creating and communicating the online self. Elsevier Inc.

Danaher, J., Hogan, M. J., Noone, Ch., Kennedy, R., Behan, A., Paor, De A., Felzmann, H., Haklay, M., Khoo, S.-M., Morison, J., Murphy, M. H., O’Brolchain, N., Schafer, B., & Shankar, K. (2017). Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big Data and Society, 4(2), 1–21. https://doi.org/10.1177/2053951717726554

Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data and Society, 3(2)1–11. https://doi.org/10.1177/2053951716665128

Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005

Flyverbom, M., & Murray, J. (2018). Datastructuring: Organizing and curating digital traces into action. Big Data and Society, 5(2), 1–12. https://doi.org/10.1177/2053951718799114

Goodwin, I., Griffin, Ch., Lyons, A., McCreanor, T., & Moewaka Barnes, H. (2016). Precarious popularity: Facebook drinking photos, the attention economy, and the regime of the branded self. Social Media + Society, 2(1), 1–13. https://doi.org/10.1177/2056305116628889

Just, N., & Latzer, M. (2017). Governance by algorithms: Reality construction by algorithmic selection on the internet. Media, Culture and Society, 39(2), 238–258. https://doi.org/10.1177/0163443716643157

Kelleher, J. D., & Tierney, B. (2018). Data science. Series: The MIT Press Essential Knowledge. Massachusetts Institute of Technology. https://doi.org/10.7551/mitpress/11140.001.0001

Klinger, U., & Svensson, J. (2018). The end of media logics? On algorithms and agency. New Media and Society, 20(12), 4653–4670. https://doi.org/10.1177/1461444818779750

Langlois, G., & Elmer, G. (2019). Impersonal subjectivation from platforms to infrastructures. Media, Culture and Society, 41(2), 236–251. https://doi.org/10.1177/0163443718818374

Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data and Society, 5(1), 1–16. https://doi.org/10.1177/2053951718756684

Pasquale, F. (2015). The Black Box Society: The secret algorithms that control money and information. President and Fellows of Harvard College. https://doi.org/10.4159/harvard.9780674736061

Pink, S., Sumartojo, Sh., Lupton, D., & Heyes La Bond, Ch. (2017). Mundane data: The routines, contingencies and accomplishments of digital living. Big Data and Society, 4(1), 1–12. https://doi.org/10.1177/2053951717700924

Pötzsch, H. (2018). Archives and identity in the context of social media and algorithmic analytics: Towards an understanding of iArchive and predictive retention. New Media and Society, 20(9), 3304–3322. https://doi.org/10.1177/1461444817748483

Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1–6. https://doi.org/10.1108/10748120110424816

Shi, Y., Luo, Y. L. L., Liu, Y., & Yang, Z. (2019). Affective experience on social networking sites predicts psychological well-being off-line. Psychological Reports, 122(5), 1666–1677. https://doi.org/10.1177/0033294118789039

Srnicek, N. (2017). Platform capitalism. Polity Press.

Vaidhyanathan, S. (2018). Anti-social media: How facebook disconnects us and undermines democracy. Oxford University Press.

Weber, R. H. (2018). “Rose is a rose is a rose is a rose” – What about code and law? Computer Law and Security Review, 34(4), 701–706. https://doi.org/10.1016/j.clsr.2018.05.005

Webster, J. G. (2017). Three myths of digital media. Convergence: The International Journal of Research into New Media Technologies, 23(4), 352–361. https://doi.org/10.1177/1354856517700385

Yeung, K. (2017). “Hypernudge”: Big Data as a mode of regulation by design. Information, Communication and Society, 20(1), 118–136. https://doi.org/10.1080/1369118X.2016.1186713