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

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PhD Seminar …

PhD Seminar

Thursday 29 Oct., 9.30-11 a.m.

Seminar Room, Journalism & Media Research Centre, 1-3 Eurimbla St (corner High St), Randwick

The impact of user-generated content on a variety of media industries and practices is by now well understood from a conceptual perspective (e.g. Benkler 2006; Jenkins 2006; Bruns 2008). What remains less thoroughly explored is the possibility to utilise the affordances of Web 2.0 technologies themselves to generate large datasets that can be used to track and evaluate user participation practices in order to develop a solid evidence base for further research into social media, and further development of social media projects, technologies, and policies. This presentation outlines research possibilities across a number of social media spaces, and uses the example of a current research project studying the Australian political blogosphere to explore potential methodological approaches.

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  • 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 [email_address] http://snurb.info/ – @snurb_dot_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 - 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)
      • 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. MSM Patterns of Activity (Jan.-Aug. 2009) Bushfires Budget Artefact Qld Election Utegate Pt. 2?
  • 14. Blog Patterns of Activity (Jan.-Aug. 2009) Bushfires Budget Artefact Qld Election Obama Utegate Pt. 1 Utegate Pt. 2
  • 15. Opinion Patterns of Activity (Jan.-Aug. 2009) Australia Day Budget Qld Election Obama Utegate Pt. 2 Artefact
  • 16. Utegate in the Australian Blogosphere 19-24 June 2009 19 June 2009: Opposition Senator Abetz reads from alleged email from PM advisor to Grech during Senate enquiry 19 June 2009: Turnbull accuses Rudd of corruption and lying to parliament 22 June 2009: Federal Police raid Grech’s house and find email 22 June 2009: Email found to be fake, created by Grech
  • 17. Utegate in the Australian Blogosphere 4-5 August 2009 4 Aug. 2009: Grech admits forging email 4 Aug. 2009: Auditor-General’s report finds no wrongdoing by PM or Treasurer Acknowledgements: Data gathering and processing by Lars Kirchhoff and Thomas Nicolai (Sociomantic Labs, Berlin) Concept maps by Tim Highfield (QUT) (Preliminary stage for ARC Discovery project, 2010-12)
  • 18. 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 )
  • 19. 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
  • 20. For More Ideas: VisualComplexity.com
  • 21. _______ 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
  • 22. 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?
  • 23.
    • Where do you want to go from here?