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Global Digital Cultures Full Lecture, Viewings & Readings Notes
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Global Digital Cultures Full Lecture, Viewings & Readings Notes (edit 18th November: I got an 8.9 with these notes)
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Week 1: Algorithms, Data, Music, VideoConcepts
-Digital: Social media plattforms, the media, open markets
-Global: All around the world (mainstream content)
-Culture: The content of the media and who is it targeted to
Platforms
Platforms are data infrastructures that facilitate, aggregate, monetise and govern
interactions between users, content, and service providers.
Example
-Youtube is a platform because it connects content creators, content, and advertisers. Also,
it is a data infrastructure because of the number of users and content it has; it is also an
open market because users can become content creators and monetise their videos and a
governmental framework because it makes certain content more visible than others.
-Netflix is not a platform because it is not an open market; it is a media company because it
sells its own content to users.
Platforms have become inevitable; cultural producers now need them.
For example: a singer needs Spotify or Youtube.
Recommendation Systems
Selection in previous mediums was chosen before the content was launched
(newspapers, tv etc.)
Nowadays, in platforms, selection and moderation are made after distribution due to the
large amounts of content.
Two Types of Curation:
-Editorial Curation: Done by humans, mainly experts on the subject choose what is to be
shown
-Algorithmic Curation: Most popular nowadays, done by algorithms that showcase what is
most viral and desired content for the user
Algorithms: “Encoded procedures for transforming input data into desired output data based
on specific calculations.”
Move away from what cultural experts considered to be great works to what algorithms think
is best for users
, No recommendation system is unbiased since humans directly do one, and men also create
algorithms to choose what is more appealing technologically. Therefore, none is more
objective than the other.
Most platforms use both editorial and algorithmic curation
App Store uses both; however, it uses mainly editorial
Spotify uses both pretty much equally
Youtube almost wholly relies on algorithms
Collaborative Filtering: Shows content that mainstream audiences enjoyed
Content-Based Filtering: Shows content by genre, showing it to users with similar search
results
We are made out of data: We have become whom we are based on our search history,
tasks etc. Therefore, we are based on data.
Rabbit Hole: On the internet, a rabbit hole frequently refers to an extraordinarily engrossing
and time-consuming topic
User Practices
User practices entail users behaving in a specific way to trigger the algorithm.
Data Fan: A data fan understands how their online activities are monitored and tracked to
produce metrics that quantify variables measuring the popularity of performers. Data fans
use individual and collective tactics to consciously intervene in and influence the statistical,
sonic, and semantic data gathered and reported on digital platforms and social media.
Making content viral
Data Teamwork: A data team is a group of dedicated, skilled fans with extensive knowledge
of digital platforms 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.
Visibility Game: The effort to make things visible to monetise them. Cultural producers play
the visibility game, and by doing so, they expose what works to become viral. Many times
they do this collaboratively among members of the platform.
From Supply Chain To Circuit
There has been a shift in the business model of the recorded music industry. From a
business that created and supplied products for purchase, a ‘supply chain’. Towards a circuit
within which cultural and economic value accumulates as active fans seek to shape, edit,
update and manipulate a continual flow of changing content that is, in turn, filtered by bots,
curators, censors and algorithms.
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