# The Datafied Self ## Retrieving and defining In order to represent myself through my social media data, I downloaded my Facebook- and Twitter-data, acquired via my GDPR-granted “right of access” (European Parliament and Council of European Union, 2016, art. 15), and then uploaded it into the “Apply Magic Sauce”-project, which is "a non-profit academic research project coordinated by the University of Cambridge Psychometrics Centre" (University of Cambridge: The Psychometrics Centre, n.d.). The aim of the tool is to predict, or give an educated guess as to how I might be interpreted by algorithms, from digital footprints left behind from my datafied behavior, so that it helps me understand the data which is said to comprise me online. Or as they describe it themselves: "a modest attempt to reverse the trend in Big Data and empower citizens to not only retain control of their data but also derive meaningful insight from it” (University of Cambridge: The Psychometrics Centre, n.d.). It operates solely from data derived from my prior performative actions (for example “Likes”, “Posts”, “Comments”, etc. from Facebook), and not from data which may or may not have been entered manually by me, as for example age. This data can be said to constitute the backbone of my datafied self (Cheney-Lippold, 2017) – the very same basis on which I’m, as an example, being served targeted ads online. I here approach the datafied self as a digital object, being “objects that take shape on a screen or hide in the back end of a computer program, composed of data and metadata regulated by structures or schemas” (Hui, 2016, p. 1). The reason behind opting for this specific course of action, is an interest in finding out how machines and algorithms perceive my datafied self, as part of better understanding datafication as a phenomenon. My aim for using this tool has been to become confronted with and explore the data-foundation, from which Facebook and Twitter can be said to be creating a datafied version of me, and to question the implied results and impacts of this. ## Experiencing and understanding the data behind the datafied self Having been confronted with the amount of data Facebook is storing about me, I was left in a staggered state of powerlessness. I strongly believed that much of the data I was presented with had already been deleted by me, but it might have ended up in a state of reminiscent of a virtual limbo instead - hidden from plain sight, but still very much in effect. Simply skimming through the thousands and utter thousands of JSON-formatted lines, every single one being a part of my datafied self, created an urge wanting to delete my Facebook-profile entirely. But alas, I have an inherently hard time trusting a “deletion” of my profile, when all of that data is so deeply engrained into the Facebook-ecosystem, distributed onto the platformed web (Helmond, 2015). Especially after experiencing Anne Helmond lecturing on Facebooks platform as a business model, and their ways of routinely and systematically circumventing disclosure of unofficial partnerships and open “data faucets” (Helmond, 2019). The data which Facebook has amounted about me is not just scary in regard to pure size but turn exponentially more frightening when that same data is inputted into “Apply Magic Sauce”. It outputs predictions, ranging from parameters like age, psychological gender, top 5 personality traits, to political orientation, religious orientation and relationship status. In this case I also inputted my acquired Twitter data, but it didn’t seem to influence the results too much as it mainly takes original tweets into account - of which I have none. It proved to be both creepily correct and alarmingly wrong, at the same time. And as part of my reflections upon this, I’m suddenly not sure which part is the worst of it. It seems that the more accurate the data is, the more effective algorithms have derived it. At the same time, when the algorithms are wrong it has actual effects on me in my social life, for example when I’m served ads or suggested content online – being an issue I bring up in the conclusive part of this assignment. ## The Data Selfie My ‘Data Selfie’ (see appendix “[Data Selfie.png](https://curatingdata.broholttrans.dk/resources/Data%20Selfie.png)”) has been produced directly from the “Apply Magic Sauce” results, being representations of the graphs and aggregated results. I’ve chosen sub-results which I determine to being either completely nonsensical, scarily correct, or just plain wrong. A part of the process of choosing has also been to avoid presenting data which I for some reason don’t feel comfortable sharing. I also commented the assumptions where applicable. Visualizing the results made me think more deeply of them, almost as if I were looking into a mirror of how I’m being perceived online, as I inscribed my reactions directly on the visualization, countering and opposing the results. ![figure 1](https://i.imgur.com/amuipUU.jpg =750x) I deem these results to be the outcome of juxtaposition, partly on because they’re derived from my datafied self as a digital object, which from the offset I consider as non-representative as vast parts of the actual data comprising it can be said to originate from earlier editions of me as a social individual. The profiling portrayed in the selfie reflects different phases of social life I’ve gone through, since creating a Facebook-profile in 2010. It also encapsulates wildly different personas, as I at this point at least attempt at sharply separating my “personal”-, “professional”- and “board of directors”-personas, as I, at this stage in my life, mainly consider Facebook as a "necessary-evil" for managing groups of people and carry out communication, being quite contextual at that. That also includes the fact that that I consciously haven’t been ‘liking’ arbitrary or non-arbitrary things on Facebook for at least the last few years. Yet the ‘old’ likes, of which I was certain that I’d already removed, is now partaking in defining me as a person, much in the same way as Cheney-Lippold describes, that “our algorithmic identities also regulate us in many different … ways” (Cheney-Lippold, 2017, p. 100) ## Conclusive research question I’ve been tasked to base my conclusion upon a research question, on the basis of this assignment insofar, which I’ve here chosen to infer from the statement, that “[w]ho we algorithmically are is not an intelligible list of adjectives but a mishmash of patterns that, at the moment, is good enough for Google [, Facebook and Twitter]. They’re useful for them but ultimately meaningless for us” (Cheney-Lippold, 2017, p. 148). My research question is then: Can these datafied selves be said to hold value? I would pose this question on the back of the unresolved questions in the back of my mind, whilst reflecting on the assignment as is; if I consider opting out of the datafication on social media as a form of resistance tactic (Duffy & Chan, 2019), am I then furthering or hindering myself in life, leaving my datafied self representing a much younger me? Am I instead subjecting myself to self-surveillance, at the cost of expressing myself? In order to explore that research question, I would take on the framework of Data Colonialism, as proposed by Couldry & Mejias (2019a, 2019b, 2019c; 2019) building upon decolonialism and the concepts of capitalism and Marxism, as an instantiation of the datafication of our modern society, as discursively articulated by, among others, Cukier & Mayer-Schönberger (2013) and van Dijck (2014). ## Bibliography * Cheney-Lippold, J. (2017). We are data: Algorithms and the making of our digital selves. New York University Press. * Couldry, N., & Mejias, U. A. (2019a). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press. * Couldry, N., & Mejias, U. A. (2019b). Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject. Television & New Media, 20(4), 336–349. https://doi.org/10.1177/1527476418796632 * Couldry, N., & Mejias, U. A. (2019c). Making data colonialism liveable: How might data’s social order be regulated? Internet Policy Review, 8(2). https://doi.org/10.14763/2019.2.1411 * Duffy, B. E., & Chan, N. K. (2019). “You never really know who’s looking”: Imagined surveillance across social media platforms. New Media & Society, 21(1), 119–138. https://doi.org/10.1177/1461444818791318 * European Parliament and Council of European Union. (2016, April 27). Regulation (EU) 2016/679 [Official Journal of the European Union]. Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679&rid=3 * Helmond, A. (2015). The Platformization of the Web: Making Web Data Platform Ready. Social Media + Society, 1(2), 2056305115603080. https://doi.org/10.1177/2056305115603080 * Helmond, A. (2019, October 31). Websites, platforms, and apps: Examining the techno-commercial evolution of digital objects and digital ecosystems [Guest Lecture]. Centre for Internet Studies, Aarhus University | Incuba, Large Auditorium, Åbogade 15, 8200 Aarhus N. https://cfi.au.dk/news/article/artikel/lectures-and-workshop-on-historiographies-of-websites-platforms-and-apps/ * Hui, Y. (2016). On the existence of digital objects. University of Minnesota Press. * Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work and think. Murray. * Mejias, U. A., & Couldry, N. (2019). Datafication. Internet Policy Review, 8(4). https://doi.org/10.14763/2019.4.1428 * University of Cambridge: The Psychometrics Centre. (n.d.). Apply Magic Sauce—Prediction API. Apply Magic Sauce. Retrieved October 1, 2020, from https://applymagicsauce.com/about-us * van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208. https://doi.org/10.24908/ss.v12i2.4776 ## Appendix * Appendix “[Data Selfie.png](https://curatingdata.broholttrans.dk/resources/Data%20Selfie.png)”