New Perspectives on Social Media: Putting Our ‘Known Unknowns’ on the Map

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

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

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  • 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?