Paper presented by Axel Bruns and Jean Burgess at the symposium "Compromised Data", Toronto, 28 Oct. 2013.

1. Easy Data, Hard Data?
Twitter Research and the
Politics of Data Access
Axel Bruns and Jean Burgess
ARC Centre of Excellence for Creative
Industries and Innovation
Queensland University of Technology
Brisbane, Australia
a.bruns / je.burgess @ qut.edu.au
@snurb_dot_info / @jeanburgess

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. CHALLENGES
• Better integration between existing social and cultural
theory & empirical work
• Data access, platform volatility
– Easy data, hard data
– Dynamics politics of platform change an object of study in
themselves

9. EASY DATA, HARD DATA
•
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

10. HARD DATA
Leetaru et al., 2013:
“Network map showing locations of users retweeting other users (geocoded Twitter Decahose tweets 23 October 2012 to 30 November 2012)”
(http://firstmonday.org/ojs/index.php/fm/article/view/4366/3654)

11. BEYOND HASHTAGS
macro:
#hashtags
meso:
follower networks
micro:
@replies
• ad hoc publics,
often rapidly forming
and dissolving
• personal publics,
accumulating slowly
and relatively stable
• interpersonal
communication,
ephemeral
(Bruns & Moe, 2013)
•
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

12. ‘HARD’ TWITTER RESEARCH APPROACHES
•
Context on Twitter:
– Meso:
• Underlying follower/followee network structures
• Dissemination of information across the follower network
• Dynamics of follower relationships

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

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

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

16. MESO: FOLLOWER DYNAMICS

17. ‘HARD’ TWITTER RESEARCH APPROACHES
•
Context on Twitter:
–
–
–
Meso:
• Underlying follower/followee network structures
• Dissemination of information across the follower network
• Dynamics of follower relationships
Micro:
• @mentions between identified user populations
• Conversational patterns (e.g. @reply / retweet chains)
• Participant career types (listener, engager, influencer, celebrity, …)
Dynamic:
• Global / national / local volumes of overall Twitter activity
• Thematic fluctuations in day-to-day activity
• Patterns in URL dissemination

18. DYNAMIC: PATTERNS OF URL SHARING

19. DYNAMIC: HOW TWITTER BEGAN
Twitter IDs 1-1,000,000 (48,000 accounts still in existence)
(see http://mappingonlinepublics.net/2013/04/08/the-first-million-ids-on-twitter/)

20. ‘HARD’ TWITTER RESEARCH APPROACHES
•
Context on Twitter:
– Meso:
• Underlying follower/followee network structures
• Dissemination of information across the follower network
• Dynamics of follower relationships
– Micro:
• @mentions between identified user populations
• Conversational patterns (e.g. @reply / retweet chains)
• Participant career types (listener, engager, influencer, celebrity, …)
– Dynamic:
• Global / national / local volumes of Twitter activity
• Thematic fluctuations in day-to-day activity
• Patterns in URL dissemination
•
Twitter in context:
–
–
–
–
Broader patterns of social media adoption and use
Media coverage of Twitter as an indicator of societal perceptions
Interlinkages with other media channels: Twitter in transmedia events
Effects of Twitter, Inc.’s commercial positioning and technological development

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