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Data Ethnography

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Introduction to Data Ethnography, to facilitate the 2018 DH Downunder Digital Methods Summer Institute.

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Data Ethnography

  1. 1. How do we research what we can’t see? Dr Jonathon Hutchinson University of Sydney Jonathon.Hutchinson@Sydney.edu.au @dhutchman
  2. 2. Steve Jones, 2014
  3. 3. The University of Sydney Page 3
  4. 4. The University of Sydney Page 4
  5. 5. The University of Sydney Page 5 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.
  6. 6. The University of Sydney Page 6 Digital Intermediation Cultural Intermediation Expertise Languages Social Capital Tacit Knowledge Digital Intermediation Cultural Intermediation Data Influencers Platforms
  7. 7. The University of Sydney Page 7
  8. 8. Relational; Contextual; Temporal
  9. 9. The University of Sydney Page 9
  10. 10. The University of Sydney Page 10
  11. 11. The University of Sydney Page 11 Ethnographers Social Scientists Data Ethnography
  12. 12. The University of Sydney Page 12 Towards Data Ethnography – Rapid Ethnography Fieldwork (re)Design Programming Implementation
  13. 13. The University of Sydney Page 13 Data Ethnography
  14. 14. The University of Sydney Page 14
  15. 15. The University of Sydney Page 15 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.
  16. 16. The University of Sydney Page 17 https://drive.google.com/drive/folders/18G0eAK Le108LegaOs-sf63bPqgRmbcbf?usp=sharing
  17. 17. The University of Sydney Page 18 Persona Construction
  18. 18. The University of Sydney Page 19 Persona Construction
  19. 19. The University of Sydney Page 20 Persona Construction Persona Construction > Algorithm Training > Data Scrape
  20. 20. The University of Sydney Page 21 Persona Construction
  21. 21. The University of Sydney Page 22 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.
  22. 22. The University of Sydney Page 23
  23. 23. The University of Sydney Page 24 Persona Worksheet
  24. 24. The University of Sydney Page 25 Training Algorithms
  25. 25. The University of Sydney Page 26 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.
  26. 26. The University of Sydney Page 27 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.
  27. 27. The University of Sydney Page 28 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?
  28. 28. The University of Sydney Page 29 Repeat the process for each of your personas
  29. 29. The University of Sydney Page 30 Data Scraping
  30. 30. The University of Sydney Page 31 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
  31. 31. The University of Sydney Page 32 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)
  32. 32. The University of Sydney Page 33 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
  33. 33. The University of Sydney Page 34 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
  34. 34. How do we research what we can’t see? Dr Jonathon Hutchinson University of Sydney Jonathon.Hutchinson@Sydney.edu.au @dhutchman

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