Invited presentation at Hong Kong Baptist University, 4 Dec. 2015.

1. New Approaches to Large-Scale
Social Media Analytics:
Investigating Twitter in Australia
Professor Axel Bruns
Digital Media Research Centre
Queensland University of Technology
Brisbane, Australia
a.bruns @ qut.edu.au
@snurb_dot_info
http://mappingonlinepublics.net/

2. QUT DIGITAL MEDIA RESEARCH CENTRE
The Digital Media Research Centre (DMRC) conducts
world-leading research that helps society understand and
adapt to the social, cultural and economic
transformations associated with digital media
technologies, and trains the researchers of tomorrow.
For more, see: http://www.qut.edu.au/research/dmrc

3. Journalism, Public
Communication &
Democracy
Economies, Policies
& Regulation
Digital Methods
Technologies &
Practices in
Everyday Life
DIGITAL
MEDIA
DMRC PROGRAMMES

4. RESEARCH PROJECT
• ARC Future Fellowship:
– Four-year project
– Axel Bruns (FF), Brenda Moon (Postdoc), Felix Münch (PhD1, 2014-2017);
PhD2 (2016-2018)
At the intersection of mainstream, niche, and social media, the
processes by which public opinion forms and public debate unfolds
are increasingly complex, and poorly understood. This project draws
on large datasets and innovative methods to develop a new model of
the Australian online public sphere.
• Also supported by ARC LIEF project:
– Two-year project (2014/15; QUT, Curtin, Deakin, Swinburne) to develop
comprehensive infrastructure for large-scale social media data analytics

5. BIG SOCIAL DATA
(http://siliconangle.com/blog/2011/08/09/twitter-unravel-the-mysteries-of-big-data/)

6. TWITTER: STATE OF THE FIELD
• Twitter research to date:
– Abundance of hashtag studies: volumetrics, keywords, networks, …
– Some studies profiling samples of the total userbase (e.g. celebrities, politicians)
– Some comprehensive (?) tracking of activities around key events and topics
– Some egocentric follower network maps, largely small-scale
– Almost absent: comprehensive follower network maps, longitudinal userbase development
trajectories, user career patterns from sign-up to listener/celebrity/…
• The political economy of Twitter research:
– Twitter API data access is shaped to privilege certain approaches
– Research funding is easier to obtain for specific, limited purposes
– Longitudinal, ‘big’ data access requires ongoing, substantial funding and infrastructure
– Exploratory, data-driven research is difficult to sell to most funding bodies
– Also related to divergent resources available to different scholarly disciplines
 Most ‘hard data’ Twitter research conducted by Twitter, Inc. and commercial research institutes

7. (https://blog.twitter.com/2012/dispatch-from-the-denver-debate)

8. BEYOND HASHTAGS
• Key needs in Twitter research:
– Understand how hashtags are situated in a wider communicative ecology on Twitter
– Document the day-to-day uses of Twitter, beyond and outside hashtags
– Trace the dynamics of Twitter as a platform for everyday quasi-private, interpersonal,
and/or public communication
– Track the impact of social and technological changes on these uses
• ad hoc publics,
often rapidly forming
and dissolving
macro:
#hashtags
• personal publics,
accumulating slowly
and relatively stable
meso:
follower networks
• interpersonal
communication,
ephemeral
micro:
@replies
(Bruns & Moe, 2013)

9. THE AUSTRALIAN TWITTERSPHERE
• Twitter in Australia:
– Strong take-up since 2009
– Centred around 25-55 age range, urban, educated, affluent users (but gradually broadening)
– Significant role in crisis communication, political communication, audience engagement, …
• Mapping the Twittersphere:
– Long-term project to identify all Australian Twitter accounts
– First iteration: snowball crawl of follower/followee networks
• Starting with key hashtag populations (#auspol, #spill, …)
• Map of ~1m accounts in early 2012
– Second iteration: full crawl of global Twitter ID numberspace through to Sep. 2013
(~870m accounts)
• Filtering by description, location, timezone fields
• Focus on identifiably Australian cities, states, timezones and other markers
• 2.8 million Australian accounts identified (by Sep. 2013)
• Retrieval of their follower/followee lists
• Best guess of account location based on timezone, location and description settings

10. DAILY GROWTH (GLOBAL)

11. MONTHLY GROWTH (AUSTRALIA)

12. MAPPING THE AUSTRALIAN USERBASE
• Mapping the Twittersphere:
– Filtered to include only accounts with (followers + followees) >= 1000
• 140k accounts, 22.8m follower/followee connections within this group
– Mapped using Gephi Force Atlas 2 algorithm (LinLog mode, scaling 0.0001, gravity 0.5)
– Qualitative interpretation of network clusters based on high-degree nodes in each cluster
– Exploration of key profile statistics (join date, number of tweets, tweeting rate)
• Activity Patterns:
– Data gathered on selected tweeting activities (hashtag participation, link sharing, …)
– Data filtered for participating accounts included in the 140k most connected users
– Data superimposed on underlying network map
• Applications:
– Combined analysis of network structures and tweeting activities
– Evaluation of user engagement across topics
– Comparative benchmarking across activities

13. Education
Agriculture
Literature
Adelaide / SA
Food
Wine
Beer
Parenting
Mums PR
Netizens
Marketing
Investing
Real Estate
Home Business
Sole Traders
Self-Help
HR / Support
Followback
Urban Media
Utilities
Advertising
Business
Fashion
Beauty
Arts
Cinema
Journalists
Politics
Hard RightLeftists
News
CyclingTalkback
Music
TV
V8s
UFC
NRL
AFL
Football
Horse Racing
Cricket
NRU
Celebrities
Hillsong
Perth
Pop
Media
Teen Idols
Cody Simpson
THE AUSTRALIAN TWITTERSPHERE

14. PROTECTED (~3.5%)

15. VERIFIED (~1.8%)

17. ACCOUNTS CREATED IN 2006

18. ACCOUNTS CREATED IN 2007

19. ACCOUNTS CREATED IN 2008

20. ACCOUNTS CREATED IN 2009

21. ACCOUNTS CREATED IN 2010

22. ACCOUNTS CREATED IN 2011

23. ACCOUNTS CREATED IN 2012

24. ACCOUNTS CREATED IN 2013

25. NUMBER OF TWEETS

26. LOCATION

27. USER ENGAGEMENT PATTERNS
• Datasets included:
– Q&A: political talkshow, Australian Broadcasting Corporation – #qanda, qanda (3 Sep. to 7 Oct. 2014)
– Big Brother: reality TV, Nine Network – #BBAU, #BBAU9, @BBAU9, #bigbrotherau (3 Sep. to 7 Oct. 2014)
– AFL Grand Final: Seven Network – #AFLGF, AFL, HAWvSYD … (27 Sep. 2014, tracked since 26 Sep.)
– ABC News: main public broadcaster – abc.net.au (June 2012 to Sep. 2014)
– news.com.au: mainstream news site – news.com.au (June 2012 to Sep. 2014)
– Daily Telegraph: tabloid newspaper – dailytelegraph.com.au (June 2012 to Sep. 2014)
– The Conversation: scholarly opinion site – theconversation.edu.au/.com (June 2012 to Sep. 2014)

29. AFL GRAND FINAL

30. Q&A

31. BIG BROTHER

32. AUSTRALIAN TWITTER NEWS INDEX (ATNIX)
• Data gathering:
– All tweets containing links to one of ~35 Australian news sites
– Includes tweets with shortened links (t.co, bit.ly, etc.)
– Continuous data gathering since August 2012 (with some outages)
• Analysis:
– Overall development of sharing patterns
– Marketshare of specific news organisations
– In-depth analysis of day-to-day trends in sharing activity
– Correlation with local, regional, and global events
• Similar projects in place for Germany and the Nordic countries

34. ABC NEWS

35. NEWS.COM.AU

36. DAILY TELEGRAPH

37. THE CONVERSATION

38. TRACKING AUSTRALIAN TWITTER ACTIVITY
• Next steps:
– Tracking 2.8m Australian Twitter users’ public tweets continuously
– Comprehensive dataset of Australian Twitter activities
– Full coverage beyond predetermined hashtags / keywords
– Important resource for rapid response analytics
– Valuable dataset for longer-term historical research
– Live tracker now operational, using Sep. 2013 userbase
– Need to regather full Australian userbase in 2015

39. AUSTRALIAN TWEETS PER DAY

40. LEADING HASHTAGS IN AUSTRALIA

41. MOST @MENTIONED USERS IN AUSTRALIA

42. Sydney Siege, 15/16 Dec. 2014

43. Peak: ~30.1% of tweets

44. Peak: ~37.1% of tweets

45. Paris Attacks, 13-15 Nov. 2015

46. http://mappingonlinepublics.net/
@snurb_dot_info
@socialmediaQUT – http://socialmedia.qut.edu.au/
@qutdmrc – https://www.qut.edu.au/research/dmrc
This research is funded by the Australian Research Council through Future Fellowship and LIEF
grants FT130100703 and LE140100148.