Your SlideShare is downloading. ×
Tracking Social Media Participation: New Approaches to Studying User-Generated Content
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Tracking Social Media Participation: New Approaches to Studying User-Generated Content

3,157
views

Published on

Presented at the University of Oslo, 25 Oct. 2010, and the University of Bergen, 26 Oct. 2010.

Presented at the University of Oslo, 25 Oct. 2010, and the University of Bergen, 26 Oct. 2010.

Published in: Education, Technology, Business

0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
3,157
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
16
Comments
0
Likes
2
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Tracking Social Media Participation: New Approaches to Studying User-Generated Content Dr Axel Bruns Associate Professor ARC Centre of Excellence for Creative Industries and Innovation Queensland University of Technology a.bruns@qut.edu.au – @snurb_dot_info http://snurb.info/ – http://mappingonlinepublics.net/ Image by campoalto
  • 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 - State of the Art." 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) • Often in clear, standardised formats • New research tools: • Network crawlers (e.g. IssueCrawler) • Website scrapers / capture tools (e.g. Twapperkeeper) • Network analysers / visualisers (e.g. Gephi, Pajek) • Large-scale text analysers (e.g. WordStat, Leximancer)
  • 7. 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?
  • 8. 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
  • 9. Applications: Twitter • Questions: • Who tweets, and what about? • How do themes and topics change over time? • How do #hashtags emerge? • What do users share – in links and retweets? • How do MSM stories influence the discussion? • How do follower networks and #hashtag communities intersect?
  • 10. #ausvotes on Twitter (17 July-24 Aug. 2010)
  • 11. #ausvotes: Mentions of the Party Leaders
  • 12. #ausvotes: Keyword Co-Occurrence
  • 13. #ausvotes: Key Election Themes
  • 14. 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)
  • 15. Applications: Australian Blogosphere (partial)
  • 16. 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)
  • 17. 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
  • 18. For More Ideas: VisualComplexity.com
  • 19. _______ 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
  • 20. 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?
  • 21. http://mappingonlinepublics.net/