Speculative Diagrams: experiments in mapping Youtube - David Benqué

Speculative Diagrams:
experiments in mapping
Youtube.
Betti Marenko1, David Benqué2
 Abstract: The notion that the future has a ‘shape’ is a
1Central Saint Martins Uni- deep-rooted construct, the cornerstone of how chance
versity of the Arts Lon- is mediated, for example through the ‘distributions’
don, United Kingdom
b.marenko@csm.arts.ac.uk
 of probability theory. Algorithmic prediction, via
 machine learning, builds on these shapes and ampli-
2 School of Communica- fies their complexity and authority. While the problem-
tion, Royal College of Art,
London, United Kingdom
 atic effects of this predictive regime and the preemp-
 tive politics it supports are objects of concern for
 scholars and practitioners across the humanities,
 social sciences, art, and philosophy, design is surpris-
 ingly disengaged from this conversation. Instead, it is
 either concerned with data visualization, often without
 questioning its positivist ontology, or with ‘seamless’
 non-interfaces which effectively seek to remove
 choice. Against this backdrop, our proposal brings
 together design theory and design practice to inter­
 rogate current modes of algorithmic prediction and
 the construction of subjectivity enabled by ‘choice
 design’. Our designed artifacts are diagrams to think
 through practice about the shape(s) of the possible.
 Rather than designing predictable futures, we aim to
 use diagram-making to expose and reframe choice
 design. These design artifacts - initial and ongoing
 experiments in mapping YouTube recommendations
 - are a series of computational diagrams that weave
 together the tools of computational prediction, critical
 design practice, and theory.

Keywords: algorithmic prediction;
diagrams; recommendation; criti-
cal data visualization;

 1

Frictions and Shifts in RTD

 #rtd2019 #researchthroughdesign #delft #rotterdam 2 3
 Marenko, Benqué | Architectures of Choice Vol.1 Youtube.

Introduction 1. Algorithmic governmentality:
 Our collaboration between design theory and practice (Marenko and the calculative ontology of prediction
 Benque, 2018) investigates/ complicates/ criticises the practice and Every time a user is offered a series of recommendations generated by a
 politics of algorithmic prediction: future-building via machine learn- digital platform - Google, Facebook, Amazon or YouTube - they are being
 ing. It sets out to position diagramming as a computational count- worked on by a technology (ranking algorithms) that operates by ana-
Frictions and Shifts in RTD

 er-practice, to reclaim the possible from algorithmic capture. We use lysing data on past behaviours to forecast future behaviours (Covington
 diagrams - speculative visualization mapping the unformed and the et al, 2016). However, predictive analytics is more than this. It orients
 unstable as they feed into changeable situations (O’Sullivan, 2016) - potential tendencies. What media theorist Mark Hansen describes as
 to think through, manipulate, and theorise prediction from a design “the calculative ontology of prediction” (Hansen, 2014, 38) – the im-
 standpoint. We use diagram-making as a practice that affords ways of pact of technomedia on the texture of human experience and on any
 probing the possible as it emerges through modes of prediction and subsequent behaviour – is a systemic way of designing tendencies and
 speculating on a future taken as fluid, rather a solid material. In this propensities at a precognitive level, in ways that are not fully accessi-
 paper we move from this starting point into our first practical case ble to conscious cognition and perceptual awareness. Our forthcoming
 study: a mapping of recommendations on the Youtube platform. behaviours are predicted not simply before they happen, but before
 we might even think of making them happen. Thus, the evaluation of
 Any interface to a catalogue of media is, explicitly or not, an order- how systems of choice are designed concerns also their role in build-
 ing of choices. While this is inescapable—there is no way to present ing identities - what philosopher Antoinette Rouvroy calls regimes of
 a list without some kind of ordering—it is also problematic. While algorithmic governmentality (Rouvroy, 2016). This is how recommen-
 user-friendly interfaces appear to be about offering choices, their dations work: when Youtube or Netflix suggest what to watch next, they
 underlying goal is to direct viewers “toward content more likely to not only build user profiles, but generate social identities drawn on a
 keep them engaged and subscribed” (Arnold, 2016, 99). Systems that multitude of fragmented points. In other words, prediction turns into
 aim to maximise user “engagement” (eyeballs capture for advertis- prescription. Classifiers do not extract categories from a neutral and
 ing) encourage to the continuous foregrounding of “fresh” (Coving- objective standpoint, but actively create them (Cheney-Lippold, 2011).
 ton et al, 2016) and “viral” content (Jiang et al, 2014)—this has been The granularity of captured user data is used to recursively construe
 shown to have hugely detrimental effects, from the traumatising of (and obscure) a live-feed of our every single subject position. By simul-
 children (Bridle, 2017) to the spreading of white nationalist ideol- taneously reflecting back and building up, this process is a continuous
 ogy (Lewis, 2018) . As “anticipatory design” (Shapiro, 2015) shows, modulation of the subject into recursive discrete data sets, endlessly
 the idea of choice design may result in the removal of options on the distributed on a multitude of access points - what Gilles Deleuze in his
 pretext of making decisions on users’ behalf for—allegedly—their Postscript on Control Societies calls the ‘dividual’ (Deleuze, 1995, 180).
 own benefit. Behind a rhetoric of convenience, the notion of reliev-
 ing users from “decision fatigue” masks a political project. Our con- 2. Mapping YouTube
 cern is not to challenge the need for an ordering system—with over The intricate architecture of YouTube recommendations, and the
 400 hours uploaded every minute and 1.8 billion logged-in monthly challenges posed by its sheer volume of content, are manipulated by
 users on YouTube it is hard to argue that some interface is required engineering decisions concerning which type of content is promoted
 to parse this content. Instead we want to critically examine the pol- on the platform (Covington et al, 2016). This is a notoriously opaque
 itics embedded in the design of systems that predict “what to watch process (partly because of non-disclosure agreements) for the engi-
 next” and use this critique to underpin our design interventions. neers themselves who have only limited understanding of how the
 system predicts - let alone the public (Burrell, 2016). The secrecy sur-
 The designed artefacts proposed here – experiments in mapping You- rounding YouTube recommendation system, and its lack of account-
 Tube ­recommendations, titled Architectures of Choice Vol.1 Youtube – are ability, has prompted several initiatives to ‘map’ it from the outside:
 a programme of work toward a visual understanding of ‘choice design’. reverse engineering strategies (Albright, 2017), ‘manual’ techniques
 Their aim is twofold: to show how architectures of choice are being (Lewis, 2018), and most notably the AlgoTransparency project designed
 designed in our everyday, impacting on the construction of algorith- to expose the bias in recommendations after the 2016 US presiden-
 mic identities (Cheney-Lippold, 2011); and to draw attention to the tial election (Chaslot et al, 2016, Lewis and McCormick, 2018).
 complexities of (and negotiations needed for) studying them through
 a critical practice. These experiments are framed by three theoretical These examples seek to gather data/evidence in order to hypothesise on
 perspectives: the idea of algorithmic governmentality to foreground how the system might be producing detrimental effects. All three use net-
 the politics implicit in choice design (Rouvroy, 2016); ideas concerning work graphs, which model data as nodes (e.g. channels or individual vid-
 maps and diagrams as a way to extract methodological friction from Figure 1. Youtube “Up Next” eos) and relations (e.g. recommendations). While networks are well suited
 existent mapping systems (Deleuze and Guattari, 1988); a critique recommendations for the vid- to mapping the the complexity of such as vast recommendation system -
 eo “Saying Logan Paul 100,000
 of design’s lack of engagement with these issues through an assess- Times” by MrBeast. Screen
 arguably the only available way to model such data - their claim to provid-
 ment of the positivist ontology of data visualization (Drucker, 2011). capture by the authors on ing any sense of ‘transparency’ does not scale well at all. The hand crafted
 19.10.2018, url: https://www. graph of a small number of actors used by Lewis (2018) shows a complex
 youtube.com/watch?v=_FX6rml2Yjs
 web of co-appearances in videos and remains relatively readable. When
 attempting to map Youtube in its totality however, AlgoTransparency’s
 Youtube Map becomes nothing more than a “hairball” or “spaghetti bowl”
 - hardly revelatory about the processes behind them (Bounegru et al.,
 2017), in fact counter-producing a sort of visibility-rich opacity. Seeing,
 in this case, is not necessarily knowing (Ananny and Crawford, 2016).

 #rtd2019 #researchthroughdesign #delft #rotterdam 4 5

3. Designed artefacts: speculative diagrams
 for mapping YouTube recommendations a. Start on Youtube homepage Figure 2a, b, c. Three
 probe designs:
 a. ripple.py
 Visualisation
 We then query the graph database to retrieve the data from probe
 We approach the space of Youtube mapping by building on, and being mind- b. simple_digger.py sessions and visualise them. Python was used to experiment with
 ful of, these insights. The prototypes we show and discuss here use au- c. digger.py popular network visualisation packages networkx and matplotlib [fig.
 tomated techniques to collect, store and visualise recommendations as List recommended videos 5]. We also relied on the built in Neo4j browser during testing, and it
Frictions and Shifts in RTD

 network data. They must be taken as explorative diagrams that attempt provided one of the more flexible ways to explore the data, a mixture
 to expose the logics of algorithmic capture. It must be clarified that the of automated layouts and manual manipulation [fig. 3]. We tested
 aim of our interventions is not to claim solutions to the shortcomings for each generating flowcharts for Mermaid.js [fig.4] but these quickly reached
 outlined above. Rather, the aim is to open up paths of enquiry that con- the limits of the library resulting in crashes and unreadable outputs.
 front the predictive apparatus and to keep on evidencing the challeng- Finally, we tested Gephi, an open source graph analysis and visualisa-
 es embedded in the tools through which this confrontation happens. tion software popular amongst Digital Humanities practitioners. [fig. 6].
 Load video page
 Collection None of these tools were satisfactory. Beyond the structural as-
 Data are collected with a headless browser (Selenium) to simulate pect of simply having too many data points to show, simple things
 user interaction with web-pages and ‘click’ through recommenda- add video data
 like the lack of text-wrapping functionality to display video titles
 tions. This allows for quick prototyping using the browser’s develop- to graph on multiple lines of text rendered our outputs unreadable very
 er tools to identify parts of the page we are interested in collecting quickly. We addressed this by zooming in on the granularity of in-
 or interacting with. Starting from the YouTube homepage, with its dividual paths/traces and to design our own visualisation using
 list of new videos and topics, we follow links to video pages and re- D3.js. [fig 7 & 8]. Importantly, practical concerns for readability
 cursively list recommendations. Three different ‘probe’ designs List recommended videos highlighted the performative nature of data as material and the
 balance the breadth and depth of the mapping in different ways: challenges of data visualization - discussed in the next section.
 ripple.py [fig. 2.a.] attempts to follow all recommendations for a giv-
 en number of recursions. This exhaustive approach was very time Repeat for
 consuming, and the resulting visualisations (see below) became n levels for each
 unreadable after the first recursion. A drastically simpler approach
 is taken in simple_digger.py [fig.2.b.] which selects only one video at
 random in the recommendations list and repeats the process for a
 given number of times. This approach was also unsatisfactory, as it Load video page
 2018-08-01 13:09:50
 presented single paths which fail to capture the interconnected na-
 ture of the recommendations made evident by the previous exper- Figure 3. Ltd
 isp: M247 Visualisation in the
 Neo4j Browser interface.
 loc: England
 iments. Digger.py [fig.2.c.] is a compromise which captures all of the
 add video data type: digger (n=10)
 recommendations at each step before choosing one at random.
 to graph

 The collected data are stored in a graph database (Neo4j) where
 videos are represented by nodes, and recommendations by edges
 connecting them. Each session is logged with a time stamp and in-
 dicates the type of probe used. This database can then be queried
 to return particular sessions or sub-graphs for visualisation.
 b.
 Start on Youtube homepage

 c. Start on Youtube homepage List recommended videos

 List recommended videos Pick one video
 at random

 Pick one video
 for each
 at random Load video page

 Load video page Load video page add video data
 to graph

 add video data
 repeat n times
 to graph repeat n times

 #rtd2019 #researchthroughdesign #delft #rotterdam 6 7

Dua Lipa ‐ IDGAF (Official Music Video)
 Maroon 5 ‐ Girls Like You ft. Cardi B

 Charlie Puth ‐ 'How Long' [Official Video]

 Figure 5. Visualisation of two
 2018-07-12 22:20:20 2018-07-13 14:29:51
 ripple.py sessions using Python
 Jonas Blue ‐ Rise ft. Jack & Jack

 packages networkx and matplotlib.
 Shawn Mendes ‐ Treat You Better isp: Virgin Media a. shows twoisp: Virgin
 levels of Media
 recommen-
 dations from the Youtube
 loc: England loc: England
 Charlie Puth ‐ The Way I Am [Official Video]

 Charlie Puth Special Olympics Performance in Seattle | LIVE 7‐1‐18 homepage.
 type: ripple (n=2) b. shows thetype:
 first ripple
 level (n=1 + 1)
Frictions and Shifts in RTD

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 of recommendations followed
 by one random pick.
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 Figure 4. Visualisa-
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 tion using Mermaid.js
 isp: Virgin Media
 Marshmello & Anne‐Marie ‐ FRIENDS (Music Video) *OFFICIAL FRIENDZONE ANTHEM* isp: Virgin Media
 loc: England loc: England
 4. The positivist ontology of data visualization type: ripple (n=2) type: ripple (n=1 + 1)
 This project is caught in a paradox - which is also at the core of the
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 temologies of data and algorithms, while at the same time relying
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 to expose/critique algorithmic systems, as most data visualisations
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 to include, and what to leave out, but also claims of objectivity and
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ISP: UK-2 Limited - Location: England

 lections. “Most information visualizations are acts of interpretation
 masquerading as presentation (…) they are images that act as if they
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 are just showing us what is, but in actuality, they are arguments made Performed by the PROFESSIONAL
 in graphical form” (Drucker, 2014). Boehnert uses the terms “digital Legend HD FIGHTERS - Part 2
 positivism” (over-simplistic take on how data mediates knowledges),
 Frictions and Shifts in RTD

 “datawash” (data visualisation that obfuscates knowledge on contro-
 versial issues) and “darkdata”( what is not measured or datified may 10 Dec 2018 5 Oct 2015 15 Mar 2018
 8,575,207 13,102,650 14,327,927
 be more relevant than what is) - to zoom in on the political nature of
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 data visualization practices and tools. Data visualization carries val-
 ue judgments that are patently obfuscated by the blanket of digital
 positivism (Boehnert, 2018). Modes of “knowledge visualisation” can
 be used instead, which declare their ongoing situatedness and par- Holy Shit! Moments Top 10 People The Funniest Off- 13 Times Spoiled
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 insights. Drucker’s distinction between data “which are assumed to
 be given” and capta which are “actively taken” is a key demarcation
 here (Drucker, 2011). While algorithmic transparency - “designed to
 give audiences an appreciation of programming decisions and con- 16 Nov 2018 23 Dec 2018 15 Dec 2017 6 Apr 2018
 4,797,494 15,986,342 5,154,076 4,370,852
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 are “meanings achieved through relations, not revelations” (ibid.).

 Our last experiment [new fig.] shows how foregrounding the re- Footage of 14 5 Times People THE 5 BEST BIGFOOT Is this 'finally'
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 this experiment does not just suggest that knowledge about the
as
 system can only be grasped at a micro-scale. Importantly, it re- 29 Mar 2018 19 Dec 2017 8 Nov 2018 7 Jun 2018
ple (n=1)
 lies on traces to build new understanding and a mode of knowl- Figure 6 ripple.py session
 10,911 4,970,826 206,490 1,035,909
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 edge-making that is situated, incomplete and speculative. The
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Trace: 2019-01-21 12:11:06
 Type: simple_digger
 ISP: UK-2 Limited - Location: England
 Figure 8 simple_digger.py session visualised with D3.js.
 5. Reflections via mapping and diagramming
 The mapping experiments presented above are diagrams of how choice
 Who wants to be a Thief Tries to Youtube homepage WOW! Maurizio design in YouTube recommendations constructs algorithmic subjects.
 Millionaire - Attack Jeremy Sarri slams his As digital media theorist Luciana Parisi argues, this subject is “neither
 Major Fraud After Being Chelsea players | solely an enslaved component of machines not its deluded interactive
Frictions and Shifts in RTD

 Exposed | The "This is a huge,
 Jeremy Kyle Show huge gamble!" user. Instead the subject is being reconfigured from the standpoint of a
 learning machine” (Parisi, 2019). Put differently, the subject emerges
 10 Apr 2008 13 Sep 2016 19 Jan 2019
 from forms of predictive patterning which retrofits choice (and think-
 9,168,308 2,801,724 525,120 ing itself) as determined by its searching into the unknown. Thus, any
 RomeijnLand The Jeremy Kyle Show BT Sport attempt at mapping choice ought to acknowledge these mechanisms.
 For this reason we consciously repurpose computational tools as “cog-
 nitive prostheses” (Srnicek, 2012) to provide purchase on otherwise
 ungraspable systems. Such acts of building mediators between the
 Michael Gove rips Jacob Rees-Mogg Ant & Dec's 20 Police Officers
 into Jeremy Corbyn Calmly DISMANTLES Undercover Prank Needed to Restrain impenetrable noise of big data and their cognitive tractability is “one of
 big style Student who called On The Jeremy Kyle Violent Son | The the most important areas where political art could be situated today”
 him 'STUPID' Show - Saturday Jeremy Kyle Show
 Night Takeaway
 (Srnicek, 2012, 4), and - we add - a key area of design intervention.

 Moreover, faced with vast, unintelligible systems designed to capture
 16 Jan 2019 10 Dec 2018 20 Feb 2014 14 Aug 2015
 230,381 1,273,737 8,015,446 1,294,164 attention and orient choice for profit, the “inability to cognitively
 Elliot Etherington 50 Patriots
 Ant & Dec's
 Saturday Night Takeaway The Jeremy Kyle Show
 map the gears and contours of the world system is as debilitating
 for political action as being unable mentally to map a city would prove
 for a city dweller” (Toscano and Kinkle, 2015). How is any form of
 reclaiming to take place if the terrain is unknown?
 Ben: one of Jeremy "YOU'RE A "How Many More BBC Question Time What practical counter-strategies are possible?
 Kyle's most LIAR"German Referendums Do You 17/01/2019 DERBY
 disliked guest. Reporter INSULTS Want?" Rees-Mogg
 Nigel Farage! DISMANTLES Giorgio Agamben’s notion of apparatus is illuminating here as
 Watch His Response Remainer
 “anything that has in some way the capacity to capture, orient, deter-
 mine, intercept, model, control, or secure the gestures, behaviours,
 22 Jul 2010 28 Dec 2018 27 Dec 2018 17 Jan 2019 opinions, or discourses of living beings” (Agamben, 2009, 14) with
 4,476,928 157,979 153,184 112,973
 a resulting dispersion and granularization of the self (Deleuze’s
 Phil Townsend ELITE PLUS 50 Patriots Kayn
 dividual, again). Only the restitution to common use of what has
 been captured can succeed as counter-apparatus interventions.

 Fox News Sunday Why did Mueller’s Hannity: Pelosi Democrat who This is what our proposals attempt to do. They use diagrams to map
 with Chris Wallace office choose to feigns moral opposed Pelosi for
 1/20/19 - Chris speak out about outrage speaker is blocked
 the contours of the capture apparatus and crucially to offer some raw
 Wallace Fox News BuzzFeed report? from a seat on the glimpses of their inside. They use diagrams as tools that by display-
 January 20, 2019 judiciary ing how choice is designed as predictable patterning aim to be both
 committee
 practical and speculative. They are practical because they visualize
 20 Jan 2019 20 Jan 2019 17 Jan 2019 20 Jan 2019 data-driven choice design by framing it through the challenges of data
 36,831 46,732 286,428 189,099
 positivism. They are speculative because they offer traces through
 Fox News Live Now Fox News Fox News Fox News which some partial and situated knowledge can be constructed, staying
 clear of interpretation and representation. Against the positivist claims
 to complete objectivity made by data visualisation and smuggled into
 Conan Furloughs so many of its tools, and unlike tracings (always reproductions with a
 Non-Essential likeness to their subject), traces offer opportunities for what historian
 Staffers
 Carlo Ginzburg calls ‘conjectural knowledge’ (Ginzburg 1980) - a mode
 of knowledge that is qualitative, contingent and incomplete, akin to div-
 ination (chance-led inquiry into the unknown) or the conjectures of the
 2 Oct 2013 tracker (sensing an animal/event that cannot be directly experienced).
 13,604,430

 Team Coco

 #rtd2019 #researchthroughdesign #delft #rotterdam 12 13

Conclusion References
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 for key reasons. Diagrams precede the instalment of a technology.
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 machinery/toolkit have to be ‘selected’ by a diagram. By extracting d1gi/faketube-ai-generated-news-on-youtube-233ad46849f9
Frictions and Shifts in RTD

 the diagram expressed by current technologies of choice design
 we might develop counter diagrams that situate and dislodge its Ananny, M. and Crawford, K. (2016) Seeing without knowing: Limitations of the transparency
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 systems, thus offering, we hope, a constructive reflection to an issue Bounegru, L. et al. (2017) ‘Narrating Networks. Exploring the affordances of networks
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 Bridle, J. (2017) Something is wrong on the internet. Medium https://medium.com/@
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 the limitations of our system, conveying its partiality and incomplete-
 ness? How do we communicate the indeterminacies and uncertainties Burrell, J. (2016) How the machine ‘thinks’: Understanding opacity in machine learning
 through meaningful practice and knowledge production? Which other algorithms. Big Data and Society. 3,1 1-12
 ‘architectures of choice’ might be diagrammed with this approach?
 Chaslot, G., Montcoudiol, A., Ledésert, S., Wielonsky, N., Bardolle, F. and Grac, M. (2016)
 AlgoTransparency [Online]. Available at http://algotransparency.org/ (Accessed 3 October
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 of Control. Theory, Culture & Society, 28, 6. 164–18

 Acknowledgements Corner, J. (1999) The agency of mapping: speculation, critique and invention In Mappings,
 The project Architectures of Choice Denis Cosgrove ed. London, Reaktion 213-252
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 missioned for Everything Happens Covington, P., Adams, J. and Sargin, E. (2016) Deep Neural Networks for YouTube Recom-
 So Much, an exhibition at the mendations. Proceedings of the 10th ACM Conference on Recommender Systems, RecSys
 London College of Communi- Boston, MA, ACM, 191–198
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 Festival 2018. It was a collabo- Deleuze, G. (1995) Negotiations. New York, Columbia University Press
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