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What Does a Good Digital Methods Project Look Like?

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Talk given at the Digital Methods Winter School 2016, University of Amsterdam, Netherlands, 11 January 2016.

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What Does a Good Digital Methods Project Look Like?

  1. 1. What Does a Good Digital Methods Project Look Like? 11 January 2016, University of Amsterdam Liliana Bounegru | lilianabounegru.org | @bb_liliana Some Highlights from the DMI Winter and Summer Schools 2015
  2. 2. DMI Winter School 2015! ! Show Me Your Dashboard: New Media Monitoring and Data Analytics as Critical Practice
  3. 3. How may we identify and develop critical data analytics?
  4. 4. The People’s Dashboard
  5. 5. Where is the social in social media platforms?
  6. 6. The People’s Dashboard (2015). https://wiki.digitalmethods.net/Dmi/PeoplesDashboard Facebook Instagram LinkedIn Twitter YouTube LastFm
  7. 7. The People's Dashboard plug-in for Facebook: http://bit.ly/peoplesdashboard
  8. 8. The Hong Kong Protests Through the Eyes of Weibo
  9. 9. How may we study public sentiment on social media platforms?
  10. 10. The Hong Kong protests through the eyes of Weibo (2015). https://wiki.digitalmethods.net/Dmi/ TheHongKongProtestsThroughTheEyesOfWeibo
  11. 11. The Hong Kong protests through the eyes of Weibo (2015). https://wiki.digitalmethods.net/Dmi/ TheHongKongProtestsThroughTheEyesOfWeibo
  12. 12. DMI Summer School 2015! ! Post-Snowden Media Empiricism and Secondary Social Media: Data Studies Beyond Facebook and Twitter
  13. 13. How may we rethink the study of online media post-Snowden? ! What value do secondary social media platforms have for research?
  14. 14. Political Memes in a Globalised World:! Does Love Win?
  15. 15. Does Love Win? The Mechanics of Memetics (2015). https://wiki.digitalmethods.net/Dmi/ SummerSchool2015DoesLoveWin
  16. 16. Does Love Win? The Mechanics of Memetics (2015). https://wiki.digitalmethods.net/Dmi/ SummerSchool2015DoesLoveWin
  17. 17. Can we map the geographies of expressions of support and their counter-reactions?
  18. 18. Does Love Win? The Mechanics of Memetics (2015). https://wiki.digitalmethods.net/Dmi/ SummerSchool2015DoesLoveWin
  19. 19. Does Love Win? The Mechanics of Memetics (2015). https://wiki.digitalmethods.net/Dmi/ SummerSchool2015DoesLoveWin
  20. 20. #Whomademyclothes - Seeking the Voices of the Workers
  21. 21. Can we find the voices of the workers online? How can we profile them?
  22. 22. #Whomademyclothes - Seeking the voices of the workers (2015). https://wiki.digitalmethods.net/Dmi/ HTwhomademyclothes
  23. 23. #Whomademyclothes - Seeking the voices of the workers (2015). https://wiki.digitalmethods.net/Dmi/ HTwhomademyclothes
  24. 24. #Whomademyclothes - Seeking the voices of the workers (2015). https://wiki.digitalmethods.net/Dmi/ HTwhomademyclothes
  25. 25. #Whomademyclothes - Seeking the voices of the workers (2015). https://wiki.digitalmethods.net/Dmi/ HTwhomademyclothes
  26. 26. #Whomademyclothes - Seeking the voices of the workers (2015). https://wiki.digitalmethods.net/Dmi/ HTwhomademyclothes
  27. 27. Tracking Ecologies
  28. 28. Tracking Ecologies (2015). https://wiki.digitalmethods.net/Dmi/SummerSchool2015TrackingEcologies
  29. 29. Can we identify tracker styles per website type?
  30. 30. Tracking Ecologies (2015). https://wiki.digitalmethods.net/Dmi/SummerSchool2015TrackingEcologies
  31. 31. Tracking Ecologies (2015). https://wiki.digitalmethods.net/Dmi/SummerSchool2015TrackingEcologies
  32. 32. Tracking Ecologies (2015). https://wiki.digitalmethods.net/Dmi/SummerSchool2015TrackingEcologies
  33. 33. Tracking Ecologies (2015). https://wiki.digitalmethods.net/Dmi/SummerSchool2015TrackingEcologies
  34. 34. Tracking Ecologies (2015). https://wiki.digitalmethods.net/Dmi/SummerSchool2015TrackingEcologies
  35. 35. Micro-Temporalities of the Web
  36. 36. What is the ‘page weight’ of bugs (trackers, beacons, widgets and other devices) and how can we experience their micro-temporalities differently?
  37. 37. Micro-Temporalities of the Web (2015). http://gauthiier.github.io/www-micro-temporalities/
  38. 38. Some data sprint tips from last year’s project coordinators • Spend time with your data. • Test hypotheses and tools on small datasets first. • Don't start with theory but start with the question, the claims (in the literature or press) and the data; let the data inform theory. • Let your expectations be challenged and shaped by the data. • Split big groups into subgroups. • Be persistent - there are always ups and downs. • Take your coffee breaks :-)
  39. 39. Thank you! Liliana Bounegru | lilianabounegru.org | @bb_liliana

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