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

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. 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. 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. CUMULATIVE GROWTH

5. DAILY GROWTH

6. TWITTER AND BREAKING NEWS

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. DISTRIBUTION OF TWEETING ACTIVITY

9. FOLLOWERS / FRIENDS DISTRIBUTION

10. FRIENDS:FOLLOWERS
Followers
Friends

11. FRIENDS:FOLLOWERS
colour = average account age

12. FRIENDS:FOLLOWERS (DETAIL)
colour = average account age
1:1
0.9:1

13. INCREASINGLY PUBLIC
Protected accounts
PUBLIC VS. PRIVATE

14. INCREASINGLY GLOBAL
INTERFACE LANGUAGE

15. INCREASINGLY GLOBAL
Languages / timezones / geo
INTERFACE TIMEZONE

16. INCREASINGLY LIKE FACEBOOK?

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.