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.

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

  1. 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 – @snurb_dot_info – Image by campoalto
  2. 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. 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. 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. 5. (Kelly & Etling, 2009)
  6. 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. 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. 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. 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. 10. #ausvotes on Twitter (17 July-24 Aug. 2010)
  11. 11. #ausvotes: Mentions of the Party Leaders
  12. 12. #ausvotes: Keyword Co-Occurrence
  13. 13. #ausvotes: Key Election Themes
  14. 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. 15. Applications: Australian Blogosphere (partial)
  16. 16. Applications: vs. Billboard • Tracking listening patterns: • Billboard = sales charts • = 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. 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 ( • Establishing trustworthiness based ( on extent of peer review • Highlighting most hotly debated (edited) sections of text
  18. 18. For More Ideas:
  19. 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. 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. 21.