Presentation at the Oxford Internet Institute Summer Doctoral Programme, Brisbane, 16 July 2009.

1. New Perspectives on Social Media:
Putting Our ‘Known Unknowns’ on the Map
Dr Axel Bruns
Senior Lecturer
Queensland University of Technology
a.bruns@qut.edu.au
http://snurb.info/

2. Researching Social Media
• Social Media:
Websites which build on Web 2.0 technologies to provide space
for in-depth social interaction, community formation, and the
tackling of collaborative projects.
Axel Bruns and Mark Bahnisch. "
Social Drivers behind Growing Consumer Participation in User-Led Content Generation: Volume 1 -
" Sydney: Smart Services CRC, 2009.

3. Researching Social Media
• Various existing research approaches:
– Qualitative:
• Processes and practices How? What?
• Content generated by users What?
• Sites and organisational structures How? In what context?
– Quantitative:
• User surveys (demographics, practices, motivations) Who? Why?
• Content coding (usually small-scale) What?
– Mostly small-scale – limited applicability?

4. Known (Un)knowns
• What we know:
– Behaviour of small social media communities
– Practices of lead users
– Structural frameworks for selected sites / site genres
– Broad demographics of social media users
• Some things we want to know:
– How does all of this work at scale?
– What about ‘average’ users?
– How do communities overlap / interact?
– Can we track developments over time?

5. (Kelly & Etling, 2009)

6. Mining and Mapping
• New research materials:
– Massive amounts of data and metadata generated by social media
– Mostly freely available online (Web / RSS / API access)
– Clear, standardised formats
• New research tools:
– Network crawlers
– Website scrapers
– Network analysers / visualisers
– Large-scale text analysers

7. Network Crawling and Analysis
• E.g. IssueCrawler:

8. Text Scraping and Analysis
• E.g. Leximancer:

9. (Kelly & Etling, 2009)

10. Asking Sophisticated Questions
• What timeframe?
●
Crawler approach: anything posted in the last 20 years
●
Resulting in one static map – but what’s happening now?
• What map?
●
Other ways to categorise these sites?
●
Differences in activity, consistency
• Known unknowns – dynamics in the Iranian blogosphere:
●
Sites appearing / disappearing?
●
Increased / decreased activity?
●
New linkage patterns:
●
Stronger / weaker clustering?
●
Move from one cluster to another?
●
Change in topics, shift in emphasis, spread of information?

11. Asking Sophisticated Questions
• Problems with current research approaches:
– Crawlers don’t distinguish site genres or link types
– Scrapers gather all text (including headers, footers, comments, …)
– Very few attempts to trace the dynamics of participation
– Many different ways to visualise these data
– Assumptions often built into the software, and difficult to change
• Alternative approaches:
– Gather large population of RSS feeds (and keep growing it)
– Track for new posts, and scrape posts only (retain timestamp)
– Extract links and keywords for further analysis
– Develop ways of identifying and visualising change over time
• Needs to be appropriate to research questions

12. Applications: Blogosphere
• Questions:
– (How) does the ‘A-List’
change over time?
– (How) does political
alignment change over time?
– How strong is cross-
connection across clusters?
– What topics are discussed
– e.g. compared with MSM?
– What happens when power (Adamic & Glance, 2005)
changes hands – is blogging
an oppositional practice?
– Beyond left and right (beyond politics!): identification of
blog genres based on textual / linkage patterns
(qualitative follow-up necessary)

13. Applications: last.fm vs. Billboard
• Tracking listening patterns:
– Billboard = sales charts
– last.fm = listening activity
– Comparing sales and use
of new releases
– Identifying brief flashes and
slow burners
– Distinguishing casual listeners
and committed fan groups
– Providing market information
to the music industry
(Adjei & Holland-Cunz, 2008)

14. Application: Wikipedia Content Dynamics
• Tracking editing patterns:
– Identifying stable/unstable content
in Wikipedia
– Highlighting controversy, vandalism,
sneaky edits
– Tracking consensus development
– Tracking responses to developing
stories (http://www.research.ibm.com/visual/projects/history_flow/capitalism1.htm)
– Establishing trustworthiness based (http://trust.cse.ucsc.edu/)
on extent of peer review
– Highlighting most hotly debated
(edited) sections of text

15. For More Ideas: VisualComplexity.com

16. _______ Science Emerges
• Web Science Research Initiative (Tim Berners-Lee et al.)
– Science, technology, computer engineering, …
– Limited inclusion of media, cultural, and communication studies
– Strong focus on Semantic Web, artificial ontologies
• Cultural Science + Cultural Science Journal (John Hartley et al.)
– Media & cultural studies, evolutionary economics, anthropology, …
– Limited inclusion of computer sciences, technology
– Strong focus on culture, innovation, evolutionary dynamics
• Data mining and visualisation
– Substantial commercial work on data mining
– Visualisation experiments in communication
design and visual arts

17. Looking Ahead
• Critical, interdisciplinary approaches
– Need to better connect cultural studies, computer science, research
technology developments
– Need to interrogate in-built assumptions of existing technologies
– Need to explore and investigate visualisation and analysis methods
– Need to develop cross-platform approaches and connect with more
conventional research
• Open questions
– Ethics of working with technically public, but notionally private data
– Potential (ab)use of data mining techniques and/or research results by
corporate and government interests
– What new knowledge can such research contribute?

18. Where do you want to go from here?