Presented by Jean Burgess, Axel Bruns, and Darryl Woodford at the Twitter Analytics workshop, University of Göttingen, 12 June 2014.

1. Mapping Online Publics: New
Methods for Twitter Research
Axel Bruns, Jean Burgess, and Darryl Woodford
ARC Centre of Excellence for Creative Industries and Innovation
Queensland University of Technology
Brisbane, Australia
a.bruns / je.burgess / dp.woodford @ qut.edu.au
http://mappingonlinepublics.net/

2. SOCIAL MEDIA RESEARCH AND ‘BIG DATA’
• Social media as the ‘big data’ moment in HASS
research
• But ‘big data’ + ‘social media’ almost always =
‘Twitter data’
• Computational social science – e.g. MSR NYC;
epidemiology; election & stock market prediction

3. Scott A. Golder*,
Michael W. Macy (2011)
Diurnal and Seasonal
Mood Vary with Work,
Sleep, and Day length
Across Diverse Cultures,
Science 333 (6051):
1878-1881

4. SOCIAL MEDIA RESEARCH AND ‘BIG DATA’
• ‘Computational turn’ in new humanities research: shift
from computational tools to a new computational
paradigm (Berry, 2012).
• Eg shift from ‘close’ to ‘distant’ reading (Moretti);
‘software studies’ (eg Fuller, 2008) and ANT
approaches to new media platforms; ‘natively’ digital
methods to diagnose patterns of social change
(Rogers, 2009).
• Intersections between data-driven social media
research & critical platform studies (Gillespie)

5. THE MAPPING ONLINE PUBLICS PROJECT
• Australian Research Council (ARC) Discovery Project (2010-13) – $410,000 +
ARC Centre of Excellence for Creative Industries and Innovation (CCI)
– First comprehensive study of Australian social media use
– Computer-assisted cultural analysis: tracking, mapping, analysing networked publics
– media, communication & cultural studies’ concerns; combining quantitative analytics
with qualitative analysis
• Mapping public engagement around politics, crisis, culture on Twitter as part of
the broader media ecology
• Various spin-offs: ARC Linkage, LIEF, Future Fellowship, ATN-DAAD, etc.

6. TWITTER AND ITS PUBLICS

7. HASHTAG PUBLICS: #QLDFLOODS (2011)
10 Jan 2011 11 Jan 2011 12 Jan 2011 13 Jan 2011 14 Jan 2011 15 Jan 2011

8. HASHTAG PUBLICS: #EGYPT @MENTIONS
1-28 Feb. 2011 15 June to 15 Sep. 2011
@mentions between users tweeting predominantly in Latin (blue) vs. Arabic (green) characters

9. PERSONAL PUBLICS
• How do personal publics (cf. Jan Schmidt) around social media
accounts form and dissolve?
– No clear data available through Twitter API
– But: API delivers lists of followers/followees
– And: account creation date for such accounts is known
 Method for approximating follower accession
(cf. Bruns, Woodford, Sadkowsky, First Monday 19.4)
– Benefits:
• Shows impact of key events on follower growth
• Shows following strategies of central account
• Can indicate influx of ‘fake’ followers (followback bots, etc.)
• Offers comparative tracking across selected population of accounts
– Limitations:
• Unable to determine un/refollowing

10. FOLLOWER ACCESSION

11. @TONYABBOTTMHR’S FAKE FOLLOWERS
(11/12 Aug. 2013)

12. TWITTER AND THE MEDIA ECOLOGY

13. TWITTER AND TV: TELEMETRICS
• The Importance of Hype
• Social Media Audiences
• From Sabermetric to Telemetrics
• The HypometerTM
• Work with Katie Prowd and partially funded by
QUTBluebox

14. THE IMPORTANCE OF HYPE
• Traditional ratings measure
what people have watched, but
have limited impact on what
people *will* watch.
• That is, they are divorced from
the “decision moment”.
• Yet, companies spend millions
promoting shows and
attempting to influence viewer
behaviour, both through TV ads
and through social media.
• How do we measure that?

15. SOCIAL MEDIA RATINGS
• Contemporary commercial social media ratings have the same limitation,
even if you trust their numbers
Cable
Channel vs
Major
Network?
Why is
having more
followers the
important
statistic?
2.5 hour
special
1 hour
show

16. BUT YOU NEED TO GET IT RIGHT
Beamly Screenshot: 31 May 2014

17. BUT YOU NEED TO GET IT RIGHT
Beamly Screenshot: 31 May 2014

18. BIG BROTHER USA VS AU (AUDIENCE)
• In US, for Big Brother (&
shows generally), there is little
correlation between viewers
and tweets.
• In Australia, for Big Brother
and other shows in our pilots,
high correlation between
viewer count and tweets.
• Applying US models to
Australia is not possible.
• Algorithms need to adjust for
their local environment and
industry context

19. SEASONAL MODELS • Blue Line represents the
ratio of total viewers,
Orange Line represents ratio
of tweets (to season average
per show).
• In both one run seasons
(top) and those with mid-
season break (bottom),
tweets are highly
exaggerated version of
traditional ratings model.
• In other words: Users tweet
much more around
premieres & finales than
regular shows. Metrics must
account for this.

20. THE HYPOMETERTM
• iOS app developed as functional prototype
to act as a ‘modern TV Guide’ for Australian
television
• Calculates ‘hype’ via a proprietary algorithm
which accounts for national and industry
context
• Ongoing evaluation of both hype figures and
predictions vs. post-show TV ratings and
social media engagement.
• Clear trend towards ‘dynamic’ audiences; a
proportion of the population on whom
broadcasters should focus.

21. SOME EARLY DATA
Underpromoted
Overperforming
High performingUnderperforming

22. TWITTER AND THE NEWS: ATNIX
• Australian Twitter News Index (ATNIX):
– Long-term project to track all tweets sharing links to Australian news sites
– Tracking, link extraction, processing, analysis
• Outcomes include:
– Day-to-day (second-by-second) volume of links being shared
– Course-of-day / course-of-week activity patterns
– Trending stories, long-term issues
– Distribution of attention across sites, and change over time
– Impact of paywalls and other changes to site structure and functionality
– Mindshare? Marketshare?
• Further extensions:
– Translation to other mediaspheres: DETNIX, NOTNIX, SWETNIX
– Comparison with other platforms/practices: Facebook, Hitwise, internal data

24. TWITTER: THE BIG PICTURE

25. TWITTER IN AUSTRALIA (AND BEYOND)
• Putting Twitter into context:
– Do specific activities reach beyond pre-existing networks?
– Can we benchmark the phenomena we observe?
– How do individual events compare to each other?
• Research needs:
– Background information on underlying follower/followee network
structures in Australia
 How (and how far) does information travel, whom does it reach?
– Baseline data on everyday average Twitter activity patterns in Australia
 How extraordinary are extraordinary events, compared to “normal”?
– Standardised metrics for a wide range of events and occurrences
 Do similar events unfold similarly? Can we use this to identify them?

26. CRISIS EVENT COMPARISON
● #qldfloods 2011
● #qldfloods 2013
● #eqnz 2011
● #eqnz 2013

27. TWITTER GROWTH IN AUSTRALIA

28. FOLLOWER NETWORKS
Perth
Marketing / PR
Design
Web
Creative
Farming
Agriculture
Hardline
Conservatives
Conservatives
Journalists
ALP
Progressives
Greens
News
Opinion
News
NGOs
Social Policy
IT
Tech
Social Media
Tech
PR
Advertising
Real Estate
Property
Jobs
HR
Business
Business
Property
Parenting
Mums Craft
Arts
Food
Wine
Beer
Adelaide
Social
ICTs
Creative
Design
Fashion
Beauty
Utilities
Services
Net Culture
Books
Literature
Publishing
Film
Theatre
Arts
Radio
TV Music
Dance
Hip Hop
Triple J
Talkback
Breakfast TV
CelebritiesCycling
Union
NRL
Football
Cricket
AFL
Swimming
V8s
Evangelicals
Teaching
e-Learning
Schools
Christians
Hillsong
Teens
Jonas Bros.
Beliebers
@KRuddMP
@JuliaGillard
Follower/followee network:
~120,000 Australian Twitter users
(of ~950,000 known accounts by early 2012)
colour = outdegree, size = indegree

29. INFORMATION DISSEMINATION
Dissemination of Julia Gillard’s “misogyny” speech, 9 Oct. 2012
(from Bruns & Sauter, “Anatomie eines Trending Topics”, DGPuk Vienna, 8 Nov. 2013)

30. INFORMATION DISSEMINATION
Dissemination of Julia Gillard’s “misogyny” speech, 9 Oct. 2012
(from Bruns & Sauter, “Anatomie eines Trending Topics”, DGPuk Vienna, 8 Nov. 2013)

31. OUTLOOK

33. PRACTICAL CHALLENGES
• Data access, platform volatility
• Research ethics
– textual research using public texts, or ‘human subjects’ research
using personal data?
– Consequences of public/personal convergence, data
markets/open data movement, and ‘context collapse’ (boyd)
– Cross-national and cross-disciplinary differences, need for public
discussion

34. PRACTICAL CHALLENGES
• Better integration with existing social and cultural theory
& empirical work
– Mixed methods, especially integration of qualitative and
ethnographic approaches
• Propagation and regularisation of methods (and
consequences for research training)
– ‘Code literacy’ sufficient to engage with the material
consequences of software platforms

35. The map is not the territory
(Korzybski)

36. http://mappingonlinepublics.net/
@snurb_dot_info
@jeanburgess
@dpwoodford
@timhighfield
@tsadkowsky
@socialmediaQUT –
http://socialmedia.qut.edu.au/