Speculative Diagrams: Experiments in Mapping Youtube

Title Speculative Diagrams: Experiments in Mapping YouTube Type Article URL https://ualresearchonline.arts.ac.uk/id/eprint/14082/ Dat e 2 0 1 9 Citation Marenko, Betti and Benqué, David (2019) Speculative Diagrams: Experiments in Mapping YouTube. Research Through Design Conference Proceedings. Cr e a to rs Marenko, Betti and Benqué, David

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1 2 Betti Marenko , David Benqué Abstract: The notion that the future has a ‘shape’ is a 1 Central 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 of probability theory. Algorithmic prediction, via [email protected] 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, atic effects of this predictive regime and the preemp- London, United Kingdom 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; critical data visualization;

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

Frictions and Shifts in RTD - - - them (Cheney-Lippold, 2011). 5 rounding YouTube recommendation system, and its lack of account and its lack of system, recommendation rounding YouTube it from the outside: to ‘map’ initiatives has prompted several ability, techniques 2017), ‘manual’ (Albright, engineering strategies reverse project designed the AlgoTransparency notably 2018), and most (Lewis, the 2016 US presiden- after to expose the bias in recommendations tial election (Chaslot et al, 2016, Lewis and McCormick, 2018). on in order to hypothesise data/evidence seek to gather These examples three use net All might be producing detrimental effects. the system how channels or individual vid- as nodes model which (e.g. data graphs, work suited well are networks While recommendations). (e.g. relations and eos) - system recommendation such as vast of to mapping the the complexity - - their claim to provid to model such data way the only available arguably ‘transparency’ sense of ing any all. The hand crafted does at well not scale a small number a complex actors usedgraph of of Lewis (2018) shows by When readable. co-appearances of web in videos and remains relatively ’s AlgoTransparency in its totality however, to map Youtube attempting bowl” or “spaghetti Map becomes nothing more than a “hairball” Youtube - hardly revelatory behind the processes about them (Bounegru et al., Seeing, opacity. a sort visibility-rich of 2017), in fact counter-producing 2016). and Crawford, (Ananny knowing is not necessarily in this case, 2. Mapping YouTube and the recommendations, YouTube architecture of The intricate posed by content, are manipulated challenges of its sheer volume by is promoted which type content of engineering decisions concerning et al, This is a notoriously opaque 2016). (Covington on the platform (partlyprocess non-disclosure because of the engi- for agreements) the how only limited of understanding who have neers themselves predicts 2016). The secrecy sur - let alone the public (Burrell, system 1. Algorithmic governmentality: governmentality: 1. Algorithmic prediction ontology of calculative the a by generated Every recommendations a series of offered time a user is are being - they or YouTube Amazon Facebook, - Google, digital platform ana- by operates a technology on by that algorithms) (ranking worked (Covington future behaviours to forecast on past behaviours data lysing It orients this. is more than analytics predictive et al, 2016). However, media describes Mark Hansen theorist What as potential tendencies. (Hansen, 2014, 38) – the im- ontology prediction” “the of calculative and on any experience human pact of technomedia of on the texture designing tendencies and of way subsequent – is a systemic behaviour - are not fully accessi that in ways level, a precognitive propensities at Our forthcoming awareness. and perceptual cognition ble to conscious are predictedbehaviours happen, not simply before they but before of evaluation the making them happen. think of even might Thus, we build- also their role in are designed concerns choice of systems how regimes of calls Rouvroy Antoinette philosopher ing identities - what - recommen 2016). This is how (Rouvroy, governmentality algorithmic they next, watch to what suggest or Netflix when Youtube work: dations social a on generate identities drawn but not only build user profiles, turns into prediction In other words, fragmented points. multitude of from a neutral and categories extract do not . Classifiers prescription standpoint, createobjective but actively construe is used capturedThe granularity of to recursively user data our every of (and obscure) a live-feed single subject position. simul- By continuous is a this process taneously reflecting back and building up, endlessly sets, data discrete the subject into recursive of modulation in his Gilles Deleuze points - what access distributed on a multitude of 1995, 180). (Deleuze, the ‘dividual’ calls Societies on Control Postscript 4 Figure 1. Youtube “Up Next” recommendations for the video “Saying Logan Paul 100,000 Times” by MrBeast. Screen capture by the authors on 19.10.2018, url: https://www. youtube.com/watch?v=_FX6rml2Yjs - - - - - – are Youtube Vol.1 Choice of titled Architectures recommendations, Introduction #rtd2019 #researchthroughdesign #delft #rotterdam Our collaboration between theory design and collaboration Our (Marenko practice and and the practice criticises complicates/ investigates/ 2018) Benque, prediction:politics algorithmic future-building of machine learn via - count ing. to position It sets out as a computational diagramming er-practice, to reclaim the possible from algorithmic capture. We use We capture. the possible to reclaim from algorithmic er-practice, unformed mapping the and the visualization - speculative diagrams 2016) - (O’Sullivan, situations feed into changeable as they unstable and theorise prediction design from a to think through, manipulate, of ways affords that as a practice use diagram-making standpoint. We modes through probing the possible prediction as it emerges of and In this a solid material. as fluid, rather taken on a future speculating case from this starting point practical paper into our first move we platform. the Youtube on recommendations of a mapping study: or not, an order explicitly media of is, interface to a catalogue Any to present While this is inescapable—there is no way choices. ing of While is also problematic. ordering—it without some kind of a list their interfaces appearuser-friendly choices, to be about offering to more likely content “toward underlying goal is to direct viewers that (Arnold, and subscribed” engaged them 2016, 99). Systems keep advertis for capture (eyeballs aim to maximise user “engagement” - (Coving “fresh” of foregrounding ing) to the continuous encourage ton et al, (Jiang et al, 2016) and “viral” 2014)—this has been content of traumatising from the detrimental effects, hugely to have shown ideol- 2017) to the spreading nationalist white of children (Bridle, 2015) shows, (Shapiro, design” 2018) . As “anticipatory ogy (Lewis, options on the of result in the removal design may the idea choice of making decisions on users’ behalf of for—allegedly—their pretext of reliev the notion benefit.convenience, own of Behind a rhetoric masks a political- project. fatigue” con Our ing users from “decision over the need an ordering system—with is not to challenge for cern 400 hours uploaded every minute and 1.8 billion logged-in monthly interface some is required it is hard to argue that users on YouTube the pol - examine to critically to parse want Instead this content. we predictitics embedded “what that to watch systems in the design of next” use this critique to underpin our design interventions. and - The designed artefacts proposed in mapping You – experiments here Tube design’. ‘choice of a visual understanding toward work a programme of are being choice architectures of how to show Their aim is twofold: impacting- algorith on the construction of designed everyday, in our to the attention mic identities (Cheney-Lippold, and to draw 2011); needed them through studying (and negotiations for) of complexities are framed These experiments three theoretical by practice. a critical to foreground perspectives: governmentality the idea algorithmic of 2016); ideas concerning design (Rouvroy, the politics implicit in choice methodological friction from to extract as a way maps and diagrams 1988); a critique and Guattari, (Deleuze mapping systems existent through an assess with these issues engagement lack of design’s of 2011). (Drucker, visualization the positivist ontologyment of data of

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Lo #rtd2019 #researchthroughdesign #delft #rotterdam We approach the space of Youtube mapping by building on, building by mapping and being approachmind Youtube of the space We 3. Designed artefacts: speculative diagrams diagrams speculative artefacts: 3. Designed recommendations YouTube mapping for Collection (Selenium) to simulate with a headless are collected browser Data - with web-pagesuser interaction through recommenda and ‘click’ develop browser’s quick prototyping using the for This allows tions. (Neo4j) where are stored database in a graph data The collected edges by and recommendations videos are represented nodes, by them. and in- is logged with a time stamp Each session connecting the type probe of dicates used. then be can queried This database to return particular or sub-graphs sessions visualisation. for - use au here and discuss prototypes The show we insights. these ful of, as recommendations and visualise store techniques to collect, tomated attempt that diagrams as explorative be must taken They data. network be It must the clarified that capture. algorithmic the logics of to expose our interventionsaim of solutions to the shortcomings is not to claim is to open the aim enquiry of up paths - con that Rather, outlined above. on evidencing the challeng- and to keep apparatus front the predictive es embedded happens. in the tools through which this confrontation er tools to identify parts are interested in collecting we the page of with its homepage, or interacting with. Starting from the YouTube and re links to video pages follow we new videos and topics, of list ‘probe’ designs different Three recommendations. list cursively ways: the breadthbalance the mapping in different and depth of for a giv all recommendations follow to [fig.attempts 2.a.] ripple.py approach very was time This exhaustive en number recursions. of became (see below) consuming, and the resulting visualisations simpler approach recursion. first the A drastically after unreadable at which selects only one video [fig.2.b.] in simple_digger.py is taken a and repeats for list the process random in the recommendations as it This approach also unsatisfactory, was number times. of given presented na- the interconnected single paths which fail to capture the previous exper made evident by the recommendations ture of of the captures all compromise which a is [fig.2.c.] Digger.py iments. each before random. choosing at step one at recommendations c.

Frictions and Shifts in RTD 2018-07-13 14:29:51 2018-07-13 Media isp: Virgin loc: England (n=1 + 1) type: ripple Visualisation of two of two 5. Visualisation Figure Python using sessions ripple.py matplotlib. and networkx packages - recommen levels of two a. shows the Youtube dations from homepage. the first level b. shows followed of recommendations pick. by one random a. b.

2018-07-13 14:29:51 isp: Virgin Media loc: England type: ripple (n=1 + 1)

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Pehli Baar | Dhadak | Ishaan & Janhvi | Ajay Gogavale | Ajay‐Atul | Amitabh Bhattacharya Pehli Baar | Dhadak | Ishaan & Janhvi | Ajay Gogavale The rarest funniest 'Two Ronnies' Sketch ever made: 'Tickle your botty with a feather tonight'' #rtd2019 #researchthroughdesign #delft #rotterdam Mapping concerns imagining, with, and experimenting of forms Mapping concerns their only partiallyworld-building embodied in, by, and expressed 1988). As a tool and Guattari, (Deleuze visual representation given a pro 1999), mapping emphasizes potential unfolds (Corner, that temologies of data and algorithms, while at the same time relying while at and algorithms, data temologies of distinction our research? bring the theoretical on them for Here we between maps and tracing to bear visualisation. on the specificsof data is future-facing simultaneously what capacityductive visualize to tracing belongs to a logic of Conversely, is not there yet. and what reproduction. to identical from the same model, It copies it is always This distinc openness to change. lacks genuine itself and therefore visualisation using data the pitfalls of tion is helpful in articulating visualisations data as most systems, algorithmic to expose/critique reality. depictions of and objective claim to be tracings—neutral has been critiqued visualisation where decisions as a process Data unacknowledged) (often unstated “assumptions, are made reflecting judgments” (Boehnert, perspectivesideological and subjective 2016). not only what embedded concern The choices visualization in data out, objectivity and but also claims of to leave and what to include, se (ideological) the result of is effectively what neutrality made by 4. The positivist ontology of data visualization data ontology of 4. The positivist the of core the - which is also at in a paradox This project is caught to critique the epis digital humanities (Kitchin, are we 2014): how Youtube homepage Frictions and Shifts in RTD 6 Jun 2018 👁👁 6,413 TeamSquatchinUSA 7 Jun 2018 👁👁 1,035,909 Disclose Screen 'The Grimreefar' 6 Apr 2018 👁👁 4,370,852 Viral For Miles 15 Mar 2018 👁👁 14,327,927 LISTaddiction Is this 'finally' Bigfoot captured in stunning quality? June 4 (Disclose Screen) The Interactions Are Increasing (June 5, 2018) TOP 10 IDIOTS WHO 10 IDIOTS TOP CHALLENGED PROFESSIONAL - Part 2 FIGHTERS 13 Times Spoiled By Kids Got OWNED Parents 3 Aug 2017 👁👁 175,250 TeamSquatchinUSA 8 Nov 2018 👁👁 206,490 Sasquatch Central 15 Dec 2017 👁👁 5,154,076 MotleyTV 5 Oct 2015 👁👁 13,102,650 Jordan Peterson Clips ALL 6 OF MIKE ALL LOSSES TYSON The Funniest Off- The-Cuff Comebacks EVER [ROUND 2] THE 5 BEST BIGFOOT VIDEOS EVER RECORDED! (Not in any particular order) Law Enforcement Officer shares Story (August 2, 2017) 11 19 May 2014 👁👁 468,992 gpdailycourier 19 Dec 2017 👁👁 4,970,826 Top 5s Finest 23 Dec 2018 👁👁 15,986,342 Top Things 10 Dec 2018 👁👁 8,575,207 This is interesting! Mike Tyson - All Tyson Mike Knockouts by the Performed Legend HD Top 10 People getting OWNED! 5 Times People Accidentally Spotted Mythical Creatures on Tape! Bigfoot encounter at the Oregon Caves, Part 1

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Reflections The mapping experiments presented above are diagrams of how choice choice how of diagrams are presented experiments mapping The above subjects. constructs algorithmic recommendations YouTube design in subject is “neither this argues, Parisi mediaAs digital Luciana theorist deluded not its machines of component interactive enslaved solely an Instead the subject is beingstandpointof a from the reconfigured user. emerges the subject differently, 2019). Put learning (Parisi, machine” (and think choice which retrofits patterning predictive of from forms ing itself) any as determined its searching by into the unknown. Thus, these mechanisms. acknowledge ought to mapping choice at attempt - tools as “cog repurpose this reason computational consciously we For on otherwise purchase (Srnicek, prostheses” 2012) to provide nitive between mediators building the of Such acts systems. ungraspable of is “one tractability and their cognitive big data noise of impenetrable importantthe most areas where political art be today” situated could area design intervention. of (Srnicek, add - a key 2012, 4), and - we designed to capture unintelligible systems with vast, faced Moreover, cognitively to profit, the “inability for choice and orient attention is as debilitating system the world of map the gears and contours prove politicalfor mentally to map a city would as being action unable of form is any 2015). How and Kinkle, a city dweller”for (Toscano if the terrain is unknown? place reclaiming to take are possible? counter-strategies practical What here as illuminating apparatus is notion of Giorgio Agamben’s orient, deter the capacity to capture, has in some way that “anything behaviours, model, intercept, gestures, control, or secure the mine, living beings” of (Agamben, or discourses 2009, 14) with opinions, the self (Deleuze’s of a resulting dispersion and granularization has what of use to common dividual restitution Only the , again). interventions. been as counter-apparatus captured succeed can to map use diagrams They to do. our proposals attempt This is what crucially and apparatus the capture some raw of the contours to offer display by as tools use diagrams that They their inside. glimpses of aim to be is designed both patterning as predictable choice ing how visualize because they are practical . They and speculative practical data of framing it through the challenges design by choice data-driven through traces positivism. offer because they are speculative They which some partial be can knowledge and situated constructed, staying the positivist claims Against and representation. clear interpretation of and smuggled into visualisation data objectivity made by to complete reproductions with a tracings (always and unlike its tools, of so many opportunities historian offer what for to their subject), traces likeness (Ginzburg 1980) - a mode knowledge’ ‘conjectural Ginzburg calls Carlo akin to div and incomplete, contingent is qualitative, that knowledge of (chance-led inquiryination the of or the conjectures into the unknown) be cannot directly experienced). that (sensing an animal/event tracker 20 Jan 2019 👁👁 189,099 Fox News 17 Jan 2019 👁👁 112,973 Kayn 14 Aug 2015 👁👁 1,294,164 The Jeremy Kyle Show 19 Jan 2019 👁👁 525,120 BT Sport Democrat who opposed Pelosi for speaker is blocked from a seat on the judiciary committee 20 Police Officers Needed to Restrain Violent Son | The Jeremy Kyle Show BBC Question Time 17/01/2019 DERBY WOW! 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Frictions and Shifts in RTD . s d hq/ s ity d Univer .org/ - Recom YouTube s for of network s of d ance DHQ Digital Humanities , DHQ Digital ’ Grac, M. (2016) and Grac, F. d olle, . DRS2016 d igitalhumanities d ium.com/@ YouTube’https : //me . pp. 5‐36 . pp. tand ing opacity in machine learning 15 ulation Biopolitics and the Mod ulation d entity: Soft . Exploring the affor s s ’ : Und ers s , 5(6), pp. 699–730. d oi: Journalism, 5(6), pp. , Digital ’ s s s s ity Pre d Univer -on-youtube-233ad 46849f9 s d , Oxfor -wrong-on-the-internet-c39c471271d 2 s parency the tran s of d , K.: Limitation without knowing (2016) Seeing Sargin, E. (2016) Deep Neural Network Neural E. (2016) Deep and Sargin, , J. The Netflix Effect: Technology and Entertainment in the 21st in the 21st and Entertainment Technology Effect: (ed s ), The Netflix D. s ey, on, Reaktion 213-252 d on, Reaktion ed . Lon grove s pace.com/387 d rs 2016.s quare s in journalis m d evice torytelling ton, MA, ACM, 191–198 MA,ton, ACM, References The Nordic Journal of Journal of The Nordic Sens ibility. s ent of Pre (2014) The Operational Hans en, Mark B.N. 24, 47. 38-53 Aesthetics as s 10.1080/21670811.2016.1186497. d ium.com/@ s : //me Med ium http on the internet. (2017) Something is wrong J. Brid le, james brid le/s omething-is the machine ‘think (2016) How J. Burrell, and Society Big Data . 3,1 1-12 algorithms. N., Bar d iol, A., G., Montcou Chas lot, d és ert,s ky, S., Wielon Le s ed 3 October s (Acce s parency.org/ : //algotran http at [Online]. Available AlgoTransparency 2018). I Algorithmic (2011) A New d , J. Cheney-Lippol & Society Culture , 28, 6. 164–18 Control. Theory, of In Mappings, critique and invention mapping: s peculation, of (1999) The agency J. Corner, Denis Cos Ad ams P., Covington, , RecSys Systems on Recommender Conference the 10th ACM of Proceedings mendations. Bos s s s ity Pre Columbia Univer York, . New G. (1995) Negotiations Deleuze, and Schizophrenia. Capitalism (1988). A Thousand Plateaus. F. G. and Guattari, Deleuze, Continuum London, Production. Harvar Knowledge of Visual Forms (2014) Graphesis. J. Drucker, Press Dis play Graphical s to (2011) ‘Humanities Approache J. Drucker, : //www. http at Available 1 [Online]. , vol. 5, no. Quarterly vol/5/1/000091/000091.html. d ’ d and Sherlock Holmes : Clues and s cientific metho Freu C. (1980). ‘Morelli, Ginzburg, , 9. Oxfor Workshop History Stanford, Stanford University Press University Stanford Stanford, is an apparatus? What G. (2009) Agamben, (2017) ‘FakeTube:Albright, J. AI-Generated News on d -new d 1gi/faketube-ai-generate M. and Crawfor Ananny, Media & Society New . 1–17 accountability. algorithmic to id eal and its application in McDonald , K. Choice/Participation/Autonomy, d the Myth of an Arnold , S. (2016) Netflix and Smith-Row Century, Bloomsbury Bloomsbury . London, the Ecocene Towards Politics: Boehnert, J (2018) Design, Ecology, Things Does Political ualis ation Vis (2016) Data Boehnert, J. s : // http Bounegru, L. Network et al. (2017) ‘Narrating - - 14 Acknowledgements Choice of The project Architectures - intially com was Youtube Vol.1 Everythingmissioned for Happens the at So Much, an exhibition - Communi of London College London Design for (LCC) cation a collabo 2018. It was Festival between Supra Systems ration and BBC R&D. LCC Studio at doctoral research Benqué’s David Art of is sup College the Royal at ported a scholarship from Mi- by (UK). Cambrdge Research crosoft - - - #rtd2019 #researchthroughdesign #delft #rotterdam Still, these initial and ongoing experiments are attempts at building re building at Still, are attempts experiments these initial and ongoing We think that our programme of work is important is work of programme our research design for that think We a technology. of the instalment precede Diagrams reasons. key for its materiality/ a technology words, in other to be possible, For extracting a diagram. by By to be ‘selected’ machinery/toolkit have design choice of current technologies by expressed the diagram and dislodge its situate that diagrams counter might develop we an un- do so, To and recommendations. preferences mechanisms of especially designers essential is for diagramming of derstanding use. they the technologies that who do not design Conclusion On this basis, some questions remain: how do we visually acknowledge visually acknowledge do we remain: how some questions On this basis, its partiality and incomplete conveying our system, of the limitations lations, not revelations. In demonstrating the project’s feasibility, they they feasibility, the project’s In demonstrating not revelations. lations, the tools of widely used understanding an in the prior artprovide on many mapping. toolsnetwork how highlight such as Gephi have They that assumptions starting with built-in purposes, our for limitations tracing, than mapping, mode rather operation.make their default of data critique to data using of challenge the emphasize they Importantly, reflection to an issue a constructive hope, thus offering, we systems, research to doing be can is central through design, productive that that regime the predictive ly addressed framing of through the theoretical politics it enforces. and the preemptive governmentality algorithmic of the indeterminacies and uncertainties communicate do we How ness? production? and knowledge through meaningful other Which practice might be diagrammed with this approach? choice’ of ‘architectures

Frictions and Shifts in RTD 16 - - , The d ia . hington, s hington, & Wa ter ecret d its s ecret tigate . Big Data & Society. Big Data . s : //www. . http ’ Fastcodesign d ian.com/technology/2018/ s ’ Cris is of thetics in an Age - s ociety.net/output/alternative-in d ata , 193–200 s peculative- d benque.com/ d avi s d es ign-fewer-choice ting the Reactionary Right on YouTube, Right on YouTube, ting the Reactionary s is _in_an_Age_of_Cri thetics s s s Pre ACM gow d igm s hifts s and para temologie d oi/10.1177/2053951714528481 , Glas ’ .s agepub.com/ Dominique Lévy, 13-25 : Dominique Lévy, York New s ar, Ya d Begum Schneid er an . Karin s : //theairpump. : http at Available titute. Royal Anthro d on: Royal s h Mus eum, Lon . Briti , in Art, and Representation Materiality ’ s s : //www.theguar http , 2nd February. , The Guardian ’ s 41286-018-00064-3 d oi.org/10.1057/ d iction/. -plotting-to-reclaim-algorithmic-pre d cas : Broa Influence Alternative , R. (2018) s ins id er inve an ex-YouTube ‘How and McCormick, E. (2018) , P. d es ign.com/3045039/the-next-big-thing-in- tco s ), in Situational d iagrammatic of (and a practice S. (2016) On the d iagram Sullivan, ’ #rtd2019 #researchthroughdesign #delft #rotterdam Shapiro, A. s Choice s (2015) ‘The Big Thing In Des ign? Le Shapiro, Next s gram O , ed s Diagram s i, L.Pari . 10.1057 AI. Subjectivity (2019) The alien s ubject of s : // http of and Immune Strategy d icalization : Ra A. Governmentality (2016) Algorithmic Rouvroy, 30-36 d Number”, an Deleuziana 3: “Life La s m? Capitalis m and Neoliberali d f d eleuziana.org/wp-content/uploa d s /2016/12/Rouvroy2eng.p : //www.la http fas Kitchin, R. (2014) Big Data,s epi Kitchin, new s : //journal 1–12 http s Lewi , Winches the absolute of (2015) Cartographies J. A.d Kinkle, an s cano, To Jiang, L., Miao, Y., Yang, Y., Lan, Z. A. and Hauptmann, A Clos er : Vid eo Style Viral G. (2014) Y., Yang, Jiang, L., Y., Miao, d eos on YouTube Vi Viral at Look s Lewi algorithmic reclaim to s : plotting d iagram ‘Speculative (2018) D. and Benque, B. Marenko, d iction. pre s : // http & Society. Data Initiative, Med ia Manipulation fluence/. algorithm feb/02/youtube-algorithm-election-clinton-trump-guillaume-chas lot pological Ins Aes s m: Political Neoliberali N. (2012) ‘Navigating Srnicek, d u/1925994/ d emia.e s : //www.aca the Object http Ungrounding Contradiction: of Matter Navigating_Neoliberalis m_Political_Aes Book Zero

Frictions and Shifts in RTD