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Notes Lectures Global Digital Cultures (Media and Culture)
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Notes Lectures Global Digital Cultures (Media and Culture)
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Lecture 1: Algorithms, Data, Music & VideoRecommendation Systems
Recommendation systems are algorithmically determined:
- Help users navigate the content a platform carries platform remains user friendly.
Editorial curation Algorithmic curation
Difference Rely on humans and their cultural norms/experts Rely on data and algorithms.
s that have knowledge about content that is being
shared and that can make valuable judgments.
Based on what is relevant or valuable content Based on calculations (and thus a
and what is not. technical process).
Similarities Results are deeply problematic: biased, discriminatory, and shaped by particular
business models.
Algorithms: “encoded procedures for transforming input data into a desired output, based on
specified calculations” (Gillespie).
How do platforms and digital services such as Netflix algorithmically recommend content?
- By using two types of filtering: content-based filtering and collaborative filtering.
Content-based filtering Collaborative filtering
Theory Recommending content based on the Recommending content based on what
characteristics of the content. other users like.
Users are recommended content based on Users are recommended content based on
what is stated in their profiles. Thus, the similar interests of large numbers of users.
platform needs to analyze the content.
Practice Platforms combine content-based filtering and collaborative filtering: the platform
determines through descriptions or tags what the content is about (content-based
filtering), but it also considers what aggregates of users watch, listen to, share, engage
with, etc (collaborative filtering).
Recommendation systems are algorithmically determined:
- Help users navigate the content a platform carries platform remains user friendly.
- Build on platform data infrastructures.
o “When we are made of data, we are not ourselves in terms of atoms. Rather, we are
who we are in terms of data” (Lippold cited by Zhang & Negus).
o Platforms infer on who we are in terms of the data that is collected about us data
and algorithms constantly change and thus what the platform interferes about us
constantly changes as well.
- Connect cultural producers, content advertisers, and end-users based on:
o Traditional demographic categories.
o Completely new categories that emerge from the data.
The Netflix Prize: a contest for $1 million that ran between 2006 and 2009 for which they asked
participating teams to try to improve the accuracy of Netflix’s recommendation system by 10%.
Hallinan and Striphas explored how the various teams engaged with that challenge. The participating
teams:
- Stopped looking at personal demographic information.
o It was too crude; it was not precise enough.
o Contrary to the conventional wisdom of marketing, in which the starting point is
always the demographic characteristics (gender, race, age, and so on).
- Focused more on a wide variety of other signals, which they tried to compute through new
algorithms.
,“How TikTok’s Algorithm Figures You Out” – 13-minute video by the Wallstreet Journal:
- About TikTok creating personalized categories based on an individual’s particular interests
and moods and how these are derived from tracking the viewing habits of users leads to
personalized genres and fragmentation of audiences.
- Points to the danger of users disappearing in rabbit holes of evermore extreme content.
User Practices
Users have agency: they can influence what happens and doing so with purpose.
- Data fan: “understands how their online activities are monitored and tracked to produce
metrics that quantify variables measuring the popularity of performers; semantic information
cataloguing the meanings associated with performers; and sonic data that register the most
frequently accessed musical characteristics of tracks. Data fans adopt individual and
collective strategies to deliberately intervene and to influence the statistical, sonic, and
semantic data collected by and reported on digital platforms and social media. Fans recognize
their importance as data and use this to benefit the musicians or idols they are following, and
to enhance their sense of achievement and agency” (Zhang & Negus).
o E.g., music charts in which data fans are concentrated on particular artists.
- Data teamwork: “a group of dedicated, skilled fans with extensive knowledge of digital
platforms, and who understand the technical processes driving algorithms and enabling
loopholes. The team collect data from various platforms and prepare strategies for
intervening, guiding other data fans who may not have such technical knowledge” (Zhang &
Negus).
Cultural Producers
Cultural producers:
- Depend for the livelihood increasingly on the production, distribution, and monetization of
content through platforms.
- Have been forced to adapt to the increasingly center role of data and algorithm attuned to
platform algorithms.
- Conscious of how their content is being datafied and curated based on user engagement
this consciousness and response by cultural producers has been called visibility game
similar to the Chinese data fans, they use a range of strategies to affect the visibility of
content, trying to shape the datastreams around their own content.
Online discussion between Instagram influencers playing the visibility game: “Influencers might
be reframed as ‘playing the visibility game,’ which shifts focus from a narrative of a lone manipulator
to one of an assemblage of actors. Within the visibility game, there is a limit to the extent that
algorithms control behavior. Influencers’ interpretations of Instagram’s algorithmic architecture—and
the visibility game more broadly—influence their interactions with the platform beyond the rules
instantiated by its algorithms. Re-directing inquiries toward the visibility game, rather than narratives
of individuals ‘gaming the system,’ makes present the interdependency between users, algorithms,
and platform owners and demonstrates how algorithms structure, but do not unilaterally determine
user behavior” (Cotter). Cultural producers have agency.
Visibility game:
- Entails influencers developing insight into how platforms organize content.
- Creators develop quite sophisticated knowledge of how platforms organize content mostly
revolves around engagement creators try to influence how their content becomes visible
and try to maximize engagement and follow accounts using pods: groups of influencers
agreeing to engage with each other’s posts a form of manipulation / a form of gaming the
system.
- Highlights the interdependency between users, algorithms, and platforms and show the
agency in this configuration (Cotter).
, - Blockbuster dynamic: some win at the visibility game while others remain invisible.
The logic of production, distribution, and monetization appears to be changing:
“With the increasing introduction of digital platforms, social media and smartphones, fans came to
play a decisive part in the development of a new type of content trafficking and data practice. The
recorded music industry was being reconfigured: shifting from a business that created and supplied
products for purchase by consumers, according to a linear or ‘supply chain,’ towards a circuit within
which cultural and economic value accumulates as active fans (along with musicians, industries, and
governments) seek to shape, edit, update and manipulate a continual flow of changing content that is,
in turn, filtered by bots, curators, censors and algorithms while being constantly mined and analyzed
for data that loops back into further interventions” (Zhang and Negus) Rather than a linear process
of production, distribution, consumption, and monetization, all these elements become entangled in a
constant feedback loop in which all these elements are responding to each other Consequently, the
way in which we can think about the cultural commodity appears to be changing as well Cultural
commodity becomes “contingent cultural commodity” in which content is constantly optimized.
Contingent cultural commodity: “Products and services offered and circulated via digital platforms
are contingent in the sense that they are malleable, modular in design, and informed by datafied user
feedback, open to constant revision and recirculation. As such, we will speak of contingent
commodities, which appear not only in the news sphere but also across all domains of cultural
production, including video, fashion blogging, and music” (Nieberg & Poell).
- E.g., BuzzFeed: by identifying trending social media topics and popular search trends,
production costs are calculated. After the content is produced and users are sorted in groups,
new calculations are done. Data is collected on relevant user engagement in the form of social
sharing, comments, search ranks, etc. These lead to a new round of evaluations, which is
whether it is necessary to edit or optimize the content, or invest further in paid promotion,
which can lead to an article given a new title for example. Here, content is contingent,
changing in relation to the data that is circulated, collected, and analyzed.
Music as data stimulates/pressures producers to act as coders, treating their productions not as
songs but also coding it with metadata, and analyzing how it is being received “As the production
and circulation of music takes on the characteristics and features of software through digitization,
musicians, and labels become more like software developers, building songs to match particular
content needs and trigger particular algorithmic variables. Artists must now think of song titles and
lyrics not just as signatures of their creative processes, but as keywords that might direct traffic to
their content. Many fans likely now understand their activities not solely as acts of fandom for artists
they enjoy, but as part of a larger process of engineering popularity through play counts. Marketers
typically now consider bots and other artificial ways of boosting play counts as simply part of the
price of taking part in crowded platforms.” (Morris). The process of optimization plays a big role.
Sarandos, Netflix’s chief content officer, about the production of House of Cards: “With House
of Cards, it was identifying not just somebody who saw The Social Network or liked David Fincher
but trying to figure out what everybody who liked Benjamin Button, Seven, Fight Club and Social
Network have in common. It’s that they love David Fincher’s style of storytelling. You look at Kevin
Spacey fans, and then you say, ‘How about people who love political thrillers?’ We went back and
pulled all the political thrillers people have watched and rated highly. So, you’ve got all these
populations, and right where they overlap in the middle is the low-hanging fruit. If we can get the
show in front of these people, they will watch it and love it” (quoted in Hallinan & Striphas). Netflix
attempts to target specific audiences rather than undifferentiated mass audiences by using data
and algorithm to break down film and television content in very particular elements (content-based
filtering). To determine what the audience might like, these elements are then recombined.
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