The document discusses the challenges and methodologies in researching unseen digital intermediation issues in the context of user data and privacy. It advocates for the development of innovative ethnographic methods to explore the complexities of interoperability in data and its implications for public interest research. Additionally, it includes a step-by-step guide on persona construction and analyzing YouTube algorithms and user comments to enhance understanding of digital cultural practices.

1. How do we research what we can’t see?
Dr Jonathon Hutchinson
University of Sydney
Jonathon.Hutchinson@Sydney.edu.au
@dhutchman

2. Steve Jones, 2014

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It is a 21st century digital
intermediation problem: the
potential benefits of platform
comparable user-data is useful
for the concerned stakeholders,
while simultaneously intruding on
their personal information
potentially increasing
surveillance, personal security
breaches, and the capitalization
of our digital selves.

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Digital Intermediation
Cultural
Intermediation
Expertise
Languages
Social
Capital
Tacit
Knowledge
Digital
Intermediation
Cultural
Intermediation
Data
Influencers
Platforms

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8. Relational; Contextual; Temporal

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Ethnographers
Social
Scientists
Data
Ethnography

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Towards Data Ethnography – Rapid
Ethnography
Fieldwork (re)Design Programming Implementation

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Data Ethnography

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Discussion
– Interoperability is increasing across all sectors of society
– Some aspects are positive; unfortunately there are a
number of negative life issues for some misrepresented
members
– The enmeshed state/government stewardship of
interoperability complicates matters for public interest
researchers
– We need to be actively designing new methodologies in
these areas to continue our work.

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https://drive.google.com/drive/folders/18G0eAK
Le108LegaOs-sf63bPqgRmbcbf?usp=sharing

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Persona Construction

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Persona Construction

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Persona Construction
Persona Construction > Algorithm Training > Data Scrape

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Persona Construction

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Persona Construction
1. Name
2. Age
3. Gender
4. Occupation
5. Hobbies
6. Location
7. The sorts of devices they use (tech familiarity)
Create three personas now.

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Persona Worksheet

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Training Algorithms

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Training the YouTube Algorithm
1. If you are signed into Firefox, you will need to sign out (this
is a good practice to undertake, regardless).
2. Open Firefox as your browser for this exercise and click
Create Profile. Name the Profile the same name as the
Persona you have created. It is fine to store the profile
information in whichever directory Firefox suggests, so press
‘Done’ when finished.
3. Open a new tab and go to Gmail. You will need to create a
new Google account. Enter the name of the account as you
have constructed, for example First Name, Surname, and
DOB. Assign an email address to the persona and record
this in your persona table.
4. Log in to Google.

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Training the YouTube Algorithm
5. Open a new tab and go to www.youtube.com.
6. You should be already signed in, but if not sign in to YouTube using
the details you have just created.
7. Record the suggested channels for you on the front page. This is
crucial. These videos represent the ‘out of the box’ videos in which
YouTube thinks your persona will be interested. These will also
provide interesting insights when you compare the results after you
have trained the algorithm.
8. Enter your first hobby as an interest term, for example ‘horse
racing’. Click on the top result from the search. Record the URLs of
the top ten videos that are listed in the Recommended list on the
right hand side.
9. Return to the search bar and enter the next search term and repeat
step 7.
10.Repeat process for each search term.

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Observations
What are the videos?
What are the common genres?
Who are they aiming the videos toward?
Can you discern any economics or politics at play here?

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Repeat the process
for each of your
personas

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Data Scraping

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Understanding the Network(s) – Comment
Threads
– We can now undertake a number of analyses with the trained
YouTube algorithms
– Look at the Digital Methods Initiative YouTube
[https://tools.digitalmethods.net/netvizz/youtube/]
– Launch the ‘Video Info and Comments’ tool
[https://tools.digitalmethods.net/netvizz/youtube/mod_video_i
nfo.php]
– We can now capture the comments and analyse them in
various ways
– If you are versed in Topic Modelling, this may work for you
– If you want to put them into a Word Cloud, that’s OK too

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Understanding User Comments (Discourse
Analysis)
1. Log into your first persona that you have constructed and
used to train the YouTube algorithm.
2. Select the top recommended video for you (Suggestions for
You).
3. Click on the video.
4. Record the Video ID (video ids can be found in URLs, e.g.
https://www.youtube.com/watch?v=aXnaHh40xnM)
5. Press Submit
6. Download the …_comments.tab file
7. Open in Excel
8. Begin processing in your chosen platform (Let’s chose what
we want to do today)

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Gephi – Shall we try this now?
– Many of the DMI tools provide us with a .gdf file
– These can be opened with and used in Gephi
[https://gephi.org/]
– I can provide additional info on how to do this if needed
– There is another SNA session later this week

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If we do have time, here’s some Gephi settings
– Open the .gdf file with Gephi
– See if we need to filter any data
– Apply these settings
– Threads: set this to the number of
processors in your computer, to
maximise the use of computing power
and speed up the network
visualisation
– Tick LinLog mode, which improves
the cohesion of clusters in the
network
– Set Tolerance to 1000 or higher
(much higher values are useful for
large networks of 100,000 or more
nodes

35. How do we research what we can’t see?
Dr Jonathon Hutchinson
University of Sydney
Jonathon.Hutchinson@Sydney.edu.au
@dhutchman