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Exploring the Global Demographics of Twitter

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Paper by Axel Bruns, Darryl Woodford, and Troy Sadkowsky presented at the Association of Internet Researchers conference, Daegu, Korea, 22-25 Oct. 2014.

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Exploring the Global Demographics of Twitter

  1. 1. Exploring the Global Demographics of Twitter Axel Bruns, Darryl Woodford, and Troy Sadkowsky ARC Centre of Excellence for Creative Industries and Innovation Queensland University of Technology Brisbane, Australia a.bruns / dp.woodford / t.sadkowsky @ qut.edu.au @snurb_dot_info / @dpwoodford / @tsadkowsky http://socialmedia.qut.edu.au/ / http://mappingonlinepublics.net/
  2. 2. THE GLOBAL TWITTER USERBASE • Limited information: – Some details from Twitter in IPO documents and updates to shareholders – Some commercial research (e.g. Socialbakers), using unknown methods – Some extrapolations based on surveys, usually country-specific (e.g. Pew Centre, Sensis) • Significant gap: – Difficult to assess global and national Twitter patterns without background data – Impossible to assess relative size of local Twitterspheres without global data
  3. 3. OUR APPROACH • Data gathering and analysis: – Slow crawl through Twitter account ID numberspace, from ID 0 to ID ~2,000,000,000 (as of 31 Aug. 2013) – Retrieval of all publicly available profile information for each ID from Twitter API – Storage and processing in Google BigQuery database – Data analysis via Tableau Desktop • Limitations: – Accounts, not users (!) – All dates in AEST – Single snapshot, blurry edges: accounts created/deleted during data gathering – Twitter API not always a completely reliable data source – Data for most recent accounts will have changed substantially since gathering – No information available about deleted historical accounts – Privacy concerns (rightly) prevent any detailed profiling
  4. 4. CUMULATIVE GROWTH
  5. 5. DAILY GROWTH
  6. 6. TWITTER AND BREAKING NEWS
  7. 7. ACCELERATING GROWTH? • Oldest surviving accounts: – @jack (ID #12), @biz (ID #13): 22 March 2006 • Daily growth: – From ~200k (March 2009) to ~900k (August 2013) • Monthly growth: – From ~5m (March 2009) to ~26m (August 2013) • But: – Dataset only includes accounts which still exist: growth = accession – attrition – Attrition is likely to increase with time: most recent accounts least likely to be deleted soon
  8. 8. DISTRIBUTION OF TWEETING ACTIVITY
  9. 9. FOLLOWERS / FRIENDS DISTRIBUTION
  10. 10. FRIENDS:FOLLOWERS Followers Friends
  11. 11. FRIENDS:FOLLOWERS colour = average account age
  12. 12. FRIENDS:FOLLOWERS (DETAIL) colour = average account age 1:1 0.9:1
  13. 13. INCREASINGLY PUBLIC Protected accounts PUBLIC VS. PRIVATE
  14. 14. INCREASINGLY GLOBAL INTERFACE LANGUAGE
  15. 15. INCREASINGLY GLOBAL Languages / timezones / geo INTERFACE TIMEZONE
  16. 16. INCREASINGLY LIKE FACEBOOK?
  17. 17. http://mappingonlinepublics.net/ @snurb_dot_info @dpwoodford @tsadkowsky @jeanburgess @timhighfield @socialmediaQUT – http://socialmedia.qut.edu.au/ This research is funded by the Australian Research Council through Future Fellowship and LIEF grants FT130100703 and LE140100148.

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