This document discusses new approaches to researching social media at scale using data mining and network analysis techniques. It notes that while traditional qualitative and small-scale quantitative research on social media provides insights, there is a need to study phenomena like communities and user behaviors over large populations and extended timeframes. The document outlines tools like crawlers, scrapers, and network/text analyzers that can access and analyze massive amounts of publicly available social media data. It provides examples of how such techniques could shed light on dynamics in domains like blogs, music listening, and Wikipedia editing. The document calls for more interdisciplinary work at the intersection of cultural studies, computer science, and research technologies to fully realize the potential of these new approaches.

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?