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From Algorithms to Diagrams: How to Study Platforms?
1. From Algorithms to Diagrams
Bernhard Rieder
Mediastudies Department
Universiteit van Amsterdam
Lisbon, February 28, 2019
How to Study Platforms?
2. Introduction
Over the last twenty years, online "platforms" have been discussed
intensely – under different names and from different angles.
From search engines as "metamedia" (Winkler 1997) to the "web as
platform" (O’Reilly 2005), to "walled gardens" (Zittrain 2008), "two-
sided markets" (Rochet & Tirole 2003) or "custodians of the Internet"
(Gillespie 2018), different functions of intermediation and their
implications have been highlighted.
Today, debates focus often on a limited number of large companies
(GAFA, FANG, …), specialized in connecting (different groups of
actors).
The notion of "platformization" addresses the emergence of a
"dominant economic and infrastructural model" (Helmond 2015).
3. Introduction
The algorithms implicated in making
connections are the last subject/aspect
under scrutiny, particularly in the
context of "infomediation"
(Rebillard et Smyrnaios 2010).
Words like distortion, opacity,
fragmentation or polarization mark
critiques and plurality, transparence, or
accountability are proposed as
solutions.
Some even talk about domination and
antitrust measures.
4.
5. "New operators such as Google, Microsoft, Yahoo! and Apple, as well as
the new, rising social media firms, such as Facebook or Twitter, should by
now be included in the list of the most powerful media organisations
worldwide." (Centre for Media Pluralism and Media Freedom 2013)
6. Introduction
Different disciplines approach the question of the power of platforms in
different ways and their conceptual and methodological differences mix
with normative disagreement.
In my own work, the technical dimension is central: how to think and
study technical objects as "technologies of power" (Foucault 1994)?
This includes historical and conceptual investigation of algorithmic
techniques, the creation of digital tools for researchers, empirical
research, questions concerning (software) design, et the study at the
interstice of technology, politics, and economics.
This presentation draws on these different lines of investigation.
7. The articulation focuses on a particular platform: YouTube.
Introduction
YouTube is at the center of a "hybrid media system" (Chadwick 2013) or
a "new screen ecology" (Cunningham et al. 2016).
8.
9. From algorithms to diagrams
The goal is not to do a "platform biography" (Burgess 2016), but
rather to retrace a "platform diagram".
The term can be read in a technical sense:
"At a more mechanical level, a platform is also a standardized diagram or
technology." (Bratton 2015, 44)
In Foucault's work, we find the concept of the diagram as a tool to
think the connection between heterogeneous elements – between
"discourses and architectures" (Foucault 1975, 276), between
"programs and mechanisms" (Deleuze 1984, 46).
These elements are not the emanation of a same logic, but rather an
arrangement of "parts" that function as a whole.
10. platform
(e.g. Facebook, Uber, App
Stores, etc.)
side 1
(e.g. users)
side 2
(e.g. advertisers,
sellers, etc.)
platform-enabled transaction
facilitates transaction by supporting offer, search,
security, contracting, payment, etc.
end-users
YouTube
(owned by Alphabet Inc.)
advertisers
content
creators
interfaces, ToS, etc.interfaces, ToS, etc.
11. The different aspects of "computerization" produce infrastructures that
"capture" (Agre 1994) an always larger number of practices, mediatizes
and "constitutes" (Burrows 2009, 451) them.
The "deep mediatization" (Couldry and Hepp 2016) that results is driven
by forms and function that flatten the differences between actors and
between types of content through standardized technical forms that are
exchanged in a very large market.
"In other words, by imposing a mathematically precise form upon previously unformalized
activities, capture standardizes those activities and their component elements and
thereby prepares them […] for an eventual transition to market-based relationships."
(Agre 1994, 120)
Computerization
12. Building on the theory of Coase (1937) on the "nature of the firm",
Ciborra develops an argument concerning transaction cost:
"The costs of organizing, i.e. costs of coordination and control, are decreased by
information technology which can streamline all or part of the information processing
required in carrying out an exchange: information to search for partners, to develop a
contract, to control the behavior of the parties during contract execution and so on."
(Ciborra 1985, 63)
The forms and functions that perform this "organizing" are constructed
and imply many instances of decision-making and design.
The mass of objects and context on offer in these very large markets
seems both to demand and to valorize the delegation of sorting to
algorithmic techniques that receive considerable power.
Computerization
13. Institutional forms and governance
Platforms operate as techno-institutional forms that combine
decentralization (market) and centralization (state):
"Platforms can be based on the global distribution of Interfaces and Users, and in
this, platforms resemble markets. At the same time, the programmed coordination of
that distribution reinforces their governance of the interactions that are exchanged
and capitalized through them, and for this, platforms resemble states." (Bratton
2015, 41)
How do distribution and governance operate, how to identify and
describe the factors or "causes" behind actually observable
phenomena or "outcomes"?
14. A platform diagram should allow for discussing together and
juxtaposing elements that are often treated separately:
The elements of this tentative diagram:
1) "Algorithms"
2) Constructed infrastructures
3) Participants, practices, and contents
4) Business models
5) Policies and "values"
6) The company and its environment
Each element points toward many possibilities to study platforms: it
highlights particular questions, specific approaches and methods, as
well as specific historical trajectories.
The YouTube diagram
16. The question of the power of algorithms is not new, but is now appears
with urgency, in particular since machine learning has started to spread.
The power often attributed to algorithms fuels demands for
transparency and accountability: but the problem is complicated.
Scholars in the social and human sciences are proposing empirical
(Sandvig et al. 2014; Diakopoulos 2014) as well as conceptual
approaches (Burrell 2016; Mackenzie 2015, 2017) to capture how
algorithms operate or "think".
1) "Algorithms"
17. Experiments (here: Kosinski et al. 2013)
show how e.g. Facebook "likes" can
predict intimate or sensitive variables
rather well.
By specifying a target variable such as
"time on site" or "click probability",
these techniques represent an
"interested" reading of reality (Rieder
2017).
In this context, "the goal is not truth,
but performativity, that is, the best
input/output relation" (Lyotard 1997).
1) Thinking machine learning
18. Instead of specifying a model as a set formula, machine learning allows for deriving a model from
specific "orchestrations" of feedback. Every signal receives a significance in relation to a target
variable that defines a desired outcome. The model is, at the same time, complex (many variables
and relationships between them) and dynamic (it changes in response to captured feedback).
19. Different initiatives for empirical observation have been
appearing, here AlgoTransparency by Guillaume Chaslot.
25. We identified three types of "morphology":
⦿ stable over long periods of time (low avRBD);
⦿ stable with "newsy" interruptions (average avRBD);
⦿ "newsy" queries that change constantly (high avRDB);
Stable periods are often organized around "explainer"-videos published by
channels that present themselves as neutral or around well-known US
actors (e.g. Stephen Colbert); during more agitated periods, "native"
actors intervene more often.
YouTube's "platform vernacular" (Gibbs et al. 2015) is clearly important,
but intersects with the subjects sitting behind the queries, making each
case different from the others.
1) YouTube, studied empirically
26. We can detect correlation between video production, search volume (via
Google Trends), and the level of change; but a clear picture of causality
remains elusive.
1) YouTube, studied empirically
27. Our approach is interested in the technical operation of search on
YouTube, but situates this operation in a large system that is heavily
affected by use practices.
We move from ranking algorithms to ranking "cultures" and combine
different qualitative and quantitative methods in a gesture of "descriptive
assemblage" (Savage 2009). Technical factors, platform vernaculars and
the particularities of individual subject are taken into account.
Technical and non-technical factors, the "platform" and the "practices"
become impossible to distinguish.
1) Beyond "algorithms"
28. "Concepts like ‘algorithm’ have become sloppy shorthands, slang terms for the act of
mistaking multipart complex systems for simple, singular ones." (Bogost 2015, n.p.)
"Programmed coordination" (Bratton 2015) is not limited to ordering
algorithms, but includes a large set of forms and functions that operate
on the level of interfaces or in the bowels of backends.
Empirical approaches like the "walkthrough method" (Light et al. 2017)
can take stock of affordances that link up to form "grammars of action"
(Agre 1994), in order to understand, e.g. on Facebook, "the specific
ways in which sociality is programmed (i.e., encoded, assembled, and
organized)" (Bucher 2013, 480).
The study of data flows, their "compression" into metrics and their
operational uses constitute a second level of analysis.
2) Constructed infrastructures
34. Platforms are inhabited by participants, practices, contents and various
relations between these elements; algorithms and infrastructures enter
into dynamics with appropriations.
Ethnographic studies provide interesting, but partial perspectives; the
first attempts to describe YouTube on the whole (p.ex. Bärtl 2018)
remain sketches – that confirm, however, familiar elements such as the
strong presence of a "Matthew effect".
Previously often described using the term "participatory culture"
(Jenkins et al. 2015), we can now observe a broad variety of actors and
vernaculars as well as important scale variation.
The proliferation of "multi-channel networks" (MCN) introduces
another layer of complexity. (cf. Lobato 2016)
3) Participants, practices, and contents
35. The techno-institutional format of the
channel (and its subscription logic) is
potentially the central "natively digital
object" (Rogers 2013) that facilitates
stabilization, professionalization, branding
and audience accumulation.
Channels render what is on offer readable
and navigable.
Who is on YouTube and why? Which themes
or issues dominate? What "works"? What is
the role of algorithms and of audience
selection dynamics?
Who will reign the new screen ecology?
3) Participants, practices, and contents
36.
37.
38. Network of "related channels" (YouTube Data Tools +
Gephi) starting from Rubin Report and The Young Turks.
39.
40. The already mentioned elements
connect directly and deeply with the
business models in use.
YouTube's model still reposes in large
part on advertisement, distributed
through "keyword bidding" and shared
(55%/45%) with more than a million
"partners" (2016).
The now classic mechanisms of "free"
apply.
4) Business models
41. Certain actors (e.g. channels with more than 100k
subscribers) have a right to "human contact".
44. Paid services such as YouTube Premium (previously RED) and YouTube
Music remove ads and allow for additional uses.
The join function integrates the Patreon model into the YouTube
platform and sales function further broaden the set of monetization
possibilities.
4) Business models
45. Can we automatically detect channels' business
elements and intersect them with other metrics?
47. Justification discourses are (were?) often based on the idea of "revealed
preference", which holds that "the individual guinea-pig, by his market
behaviour, reveals his preference pattern" (Samuelson 1948). This fuels
and justifies the use of feedback signals as "votes" and popularity
measures as means of ordering.
Due to commercial (nervous advertisers) and political pressure (public
criticism, regulation menace), the situation has become much more
complicated.
The platform now has to engage in control and censorship beyond
copyright (Content ID); we see a series of experiments that include new
rules, controlled and policed by algorithms, editors and gamified users.
5) Policies and "values"
49. Jack Dorsey and Alex Jones at the exit of the "Twitter-
Facebook Senate hearings" in September 2018.
50. How can the effects of these
interventions be studied empirically?
51.
52. After the phase of "flaunted neutrality", begins
that of "humanist values".
« [W]e're making a major change to how we build Facebook.
I'm changing the goal I give our product teams from focusing
on helping you find relevant content to helping you have
more meaningful social interactions. […] Now, I want to be
clear: by making these changes, I expect the time people
spend on Facebook and some measures of engagement will
go down. But I also expect the time you do spend on
Facebook will be more valuable. And if we do the right thing, I
believe that will be good for our community and our business
over the long term too. » (Zuckerberg 2018)
These normative questions are certainly not
simple.
5) Policies and "values"
55. The large platforms follow a strategy of "concentric diversification",
"moving into activities which mesh to some degree with the present
product line, technological expertise, customer base, or distribution
channels." (Thompson & Strickland 1978)
6) The company and its environment
Do the synergies thus liberated favor the appearance and stabilization of
cross-market oligopolies and monopolies?
Core: established business, mastery of
process and product, steady revenues;
Extension layer: mastery of process and
product, but not yet established and
generating significant revenue;
Expansion layer: experimental process and
product, "competence testing";
56. YouTube is part of the Alphabet / Google product family and entertains
various relationships with other products.
How to study these relationships?
What can resist the aspiration power of platforms? Large brands? Public
service? Other platforms? Who are YouTube's competitors? Facebook +
Instagram? Amazon + Twitch? Netflix? Disney? Traditional TV networks?
Google Search
1998 2000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
AdWords
Google News Beta
AdSense
GmailIPO
Picasa
Google Maps
YouTube
Android
Google Chrome
AdMob
Freebase
Google Wallet
Hangouts
Play Store Android Wear
Android Auto
Google Print
DoubleClick
Google X
Chrome OS
Google+
Google Drive
Google Cloud Platform
Google Now
6) The company and its environment
57. Conclusions
Platforms represent a reconfiguration of transaction modalities,
following a "digital" mode, including these elements:
⦿ Normalization and standardization that facilitate the organization of transactions
in market forms; a "flattening" of certain cultural differences through the notion
of "content" and the extension of this principle to many different domains;
⦿ Forms of algorithmic coordination that automate continuous, adaptive and
interested optimization and integrate it into infrastructures;
⦿ (Modulable) interfaces that capture participants, practices, and data, serving as
coordinators and translators between participants and the techno-institutional
modes of platforms;
⦿ Very large number of participants and contents that are constantly producing
value for the platform by appropriating and feeding the forms and functions;
⦿ Business models that stimulate the production of content while broadly keeping
the logic of "free" for users;
⦿ Policies, values, and justification discourses that seek to protect the model;
⦿ Modes of diversification and competition that favors the emergence of
monopolies and cross-market domination;
58. Conclusions
The emergent diagram describes a model that is deeply expansionist
and feeds on the dialectics between decentralization and centralization.
Platforms are "transversal network effect machines" that advance on a
"North California" (Cunningham et al. 2016) model: experimentation,
iteration, risk taking, etc.
We under-estimate the complexity of the "platform problem" and the
enormous energies liberated by the model if we treat it as a product of
capitalism "as usual".
Political responses to the challenges of platforms depends on the clarity
of analyses and our capacity to thing different elements together.
