Block 3: Week 11 Visualisation

How do I use Twitter?

This week I decided to collect data on my use of Twitter.


For a week, I collected data about my activity on Twitter. I focussed on that activity which left a data trace which could be harvested with little or no effort automatically by another party (to echo the theme of the block).

Results and Analysis

I am visualising my data so as to represent the different uses for which I may appear to employ Twitter (and hence the kind of user I might be thought to be), and how that might be measured and judged, within the narrow parameters of the platform.

data on a week of tweets represented by hieroglyphs
How do I use Twitter?

This depiction of user identity is inspired by cartouche; used to hold a name, the oval surround is to give protection (perhaps useful in the world of social media which presents various threats.)

“Museum Luxor: Cartouches of Akhenaton” by kairoinfo4u is licensed under CC BY-NC-SA 2.0

Hieroglyphs can be used in various ways so, to be read, the way they are used needs to be known to enable translation to be achieved. Even if you have identified a hieroglyph as an ‘owl’, does it stand for a word, a syllable, a letter, or is it there to give meaning to a separate hieroglyph? In the same way, ‘likes’ in social media might have multiple functions for one user (e.g. approval, agreement, amplification, bookmarking) which the viewer has no sure way to interpret (and may not appreciate, so may make an assumption, which is certainly easier and follows the trend of simplistic data interpretation.)

Again, as with the previous Governance visualisations, the aim is for depersonalisation, accepting the lack of detail and all of the subtlety which distinguishes us from one another. The cartouche of my identity on Twitter is my data: I have become datafied. And this data will outlive me, just as these carvings did their owners, and both were/are seen as ‘just there for the taking’ [Couldry and Mejias, 2019], knowable by others interpreting as they see fit.

There are numerous online tools to interpret Twitter data, created for no other reasons it appears that the data is ‘public’, so why wouldn’t you? All of them are built on simple measures (numbers of followers, likes etc.) and crude interpretations (e.g. high follower number=good; low like number=’bad’). The whole enterprise relies on the acceptance that these interpretations are valid and sufficient (no other data is needed) and, in fact, that people’s social media lives are for others to judge.

That this data is public may encourage or discourage me from contributing; what kind of performance is this and who is it for? How is it being interpreted and how is that affecting me?

How does this relate to governance?

Arguments that using big data, such as that from social media, represent a way of fixing issues such as “…the democratic deficit…” [Bartlett et al, 2014] are just “…a new dimension to digital solutionism… Big Data solutionism… the idea that Big Data sets can control, solve and overcome economic and political crises…” [Fuchs, 2019]. Suggestions that it can fix problems within education are no different. Ironically, this ignores underlying inequalities and social injustice [Fontaine, 2016] that are also behind the unrepresentative nature of technology use and big data sets [O’Neil, 2016; Schradie, 2017].

Promotion of public sources of big data, such as that from social media, by Demos and others, as a means by which policy might be developed [Williamson, 2017], can only encourage individual organisations to see this as a legitimate approach for say, monitoring staff or student activity. Since there is no mandatory ethical step to be gone through to engage in such research, (if one considers social media data ‘open’ and free to use without further permission needed), and collection and processing is attractive since relatively ‘cheap’ [Bartlett et al, 2014], such work can could be “…sunken into objects...” and “…recede into the background…” [Anagnostopoulos et al, 2013] of institutional policy.

Using the data of individuals in this way has been called not metaphorical but actual colonialism, where data is gathered “…on terms that are partly or wholly beyond the control of the person to whom the data relates…” [Couldry and Mejias, 2019]. Though the individual is not compelled to engage with social media, should they feel unable to use it, because of the fear of surveillance?

Though it has been suggested that use of numerical data to govern [Williamson, 2017] is no substitute to a democratic process [Bartlett et al, 2014], is it not tempting for organisations to position it as the dominant means by which people might be engaged, because of the arguments of size of data sets (“…millions of voices that together form society’s constant political debate…” [Bartlett et al, 2014]) and economy (“…Acquiring tweets… is free and the technology, once in place, can be trained and purposed in a matter of minutes…” [Bartlett et al, 2014])? Could trawling the data commons become the qualitative policy instruments that replaces the need for conventional staff/student consultation?


Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.

Bartlett, J., Miller, C., Reffin, J., Weir, D. and Wibberley, S., 2014. Vox digitas. Demos.

Couldry, N., and U. A. Mejias. 2019. Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject. Television & New Media. 20 (4): pp. 336–349.

Fontaine, C. 2016. The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education, Data and Society Working Paper 08.08.2016.

Fuchs, C. 2019. Beyond Big Data Capitalism, Towards Dialectical Digital Modernity: Reflections on David Chandler’s Chapter. In: Chandler, D. and Fuchs, C. (eds.) Digital Objects, Digital Subjects: Interdisciplinary Perspectives on Capitalism, Labour and Politics in the Age of Big Data. pp. 43–51. London: University of Westminster Press. DOI: License: CC‐BY‐NC‐ND 4.0.

O’Neil, C., 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. USA: Penguin Random House.

Schradie, J., 2017. Big Data is Too Small: research implications of class inequality for online data collection. Media and class: TV, film and digital culture. Edited by June Deery and Andrea Press. Abingdon, UK: Taylor & Francis.

Williamson, B. Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.

2 Replies to “Block 3: Week 11 Visualisation”

  1. Really pleased to see you engaging so widely with relevant literature Tracey. I thought your closing line here was great: “Could trawling the data commons become the qualitative policy instruments that replaces the need for conventional staff/student consultation?” I’m not sure I shared the example before, but the QAA (the HE ‘quality assurance’ agency) actually ran a pilot project scraping student reviews of their institutions from social media. They claimed it could provide near “real-time” student satisfaction scores, and act as insights for institutional improvement: It seems to confirm your sense that “trawling” for data–just because it’s already there “for the taking”–is a powerful idea, even in the education sector.

    But my main observation from this post is your use of the cartouche as an inspiration for your visualization and discussion of hieroglyphs. I’d love to see this developed further. If I remember correctly, in ancient Egypt, only certain powerful figures could produce and decode the hieroglyphs. It was a way of exerting power through access to written language/representation. Much the same might be said now. Data might quantify our identities–as algorithmic hieroglyphs of human selves–but these hieroglyphs are only produced and/or deciphered by certain privileged actors. I’d love to think further on how the protective oval of the cartouche could be developed as a model for protecting student data profiles too. Really through provoking stuff Tracey–thank you.

  2. Thank you, Ben. No, I wasn’t awear of that work by QAA, and that final line of mine was intended as a joke, knowing social media as I do.
    Yes, those with power have always been quite keen to hang on to it, using whatever means at hand to them. We can see examples of that even in the most liberal of settings.

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