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

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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.

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  • 1. Tracking Social Media Participation: New Approaches to Studying User-Generated Content
    Image by campoalto
    Dr Axel BrunsAssociate ProfessorARC Centre of Excellence for Creative Industries and InnovationQueensland University of Technologya.bruns@qut.edu.au – @snurb_dot_infohttp://snurb.info/ – http://mappingonlinepublics.net/
  • 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?
    • 8. Crawler approach: anything posted in the last 20 years
    • 9. Resulting in one static map – but what’s happening now?
    • 10. What map?
    • 11. Other ways to categorise these sites?
    • 12. Differences in activity, consistency
    • 13. Known unknowns – dynamics in the Iranian blogosphere:
    • 14. Sites appearing / disappearing?
    • 15. Increased / decreased activity?
    • 16. New linkage patterns:
    • 17. Stronger / weaker clustering?
    • 18. Move from one cluster to another?
    • 19. Change in topics, shift in emphasis, spread of information?
  • 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
  • 20. Applications: Twitter
    Questions:
    Who tweets, and what about?
    How do themes and topics change over time?
    How do #hashtags emerge?
    What do users share – inlinks and retweets?
    How do MSM stories influencethe discussion?
    How do follower networks and#hashtag communities intersect?
  • 21. #ausvotes on Twitter (17 July-24 Aug. 2010)
  • 22. #ausvotes: Mentions of the Party Leaders
  • 23. #ausvotes: Keyword Co-Occurrence
  • 24. #ausvotes: Key Election Themes
  • 25. Applications: Blogosphere
    Questions:
    (How) does the ‘A-List’ change over time?
    (How) does politicalalignment 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 bloggingan oppositional practice?
    Beyond left and right (beyond politics!): identification of blog genres based on textual / linkage patterns (qualitative follow-up necessary)
  • 26. Applications: Australian Blogosphere (partial)
    parenting
    politics
    food
    arts & crafts
    design and style
  • 27. 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 andslow burners
    Distinguishing casual listenersand committed fan groups
    Providing market informationto the music industry
    (Adjei & Holland-Cunz, 2008)
  • 28. 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
  • 29. For More Ideas: VisualComplexity.com
  • 30. _______ 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 communicationdesign and visual arts
  • 31. 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?
  • 32. http://mappingonlinepublics.net/