Media Ecologies and Methodological Innovation: The Case of Twitter

  • 1,138 views
Uploaded on

Paper presented at the CCI Symposium, Sydney, 16 Nov. 2011.

Paper presented at the CCI Symposium, Sydney, 16 Nov. 2011.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
1,138
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
8
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. Media Ecologies and MethodologicalInnovation: The Case of TwitterAssoc. Prof. Axel BrunsQueensland University of Technologya.bruns@qut.edu.au – http://snurb.info/ – @snurb_dot_infohttp://mappingonlinepublics.net/
  • 2. Background: Related Projects• CCI Project: – Media Ecologies and Methodological Innovation (Axel Bruns, Jean Burgess, Kate Crawford, Gerard Goggin, Terry Flew, John Hartley + RA Frances Shaw)• ARC Discovery Project (2010-13): – New Media and Public Communication (AB & JB + Sociomantic Labs, RAs: Caro Jende, Tim Highfield, Jen Lofgren, Mirka Streckhardt)• ATN-DAAD Projects (2011-12): – Social Media Monitoring: Analysis of Social Networks for Enterprises’ Issue Management (AsNIM) (AB & JB + Tanya Nitins, University of Münster: Stefan Stieglitz, Nina Krüger, Tobias Brockmann et al.) – Extending Computer-Aided Methods for the Analysis of Blogging and Microblogging Discourses and Publics (AB & JB + Stephen Harrington, University of Düsseldorf: Katrin Weller, Cornelius Puschmann et al.)• ASSA-ISL Project (2010-11): – Flood and Fire: Understanding the Structure and Process of Public Communication during Times of Crisis (AB & JB + National Cheng Chi University, Taipei: Pai-Lin Chen, Tsai-Yen Li, Yu-Chung Cheng et al.)• ARC Linkage Project (2012-14): – Social Media in Times of Crisis: Learning from Recent Natural Disasters to Improve Future Strategies (AB, JB, KC, TF + Queensland Department of Community Safety, Eidos Institute, Sociomantic Labs)• Website: http://mappingonlinepublics.net/
  • 3. Focus on Twitter• Real-time public communication: – Social media coverage as a first draft of the present – Especially Twitter: flat, open, self-organising network – First-hand, unfiltered, direct insights into Australians’ views – Rich data on specific events and on long-term trends• Readily available data: – Access to rich data (and metadata) through standard APIs – Especially on Twitter, limited immediate ethical concerns – Ephemeral content which is lost to posterity unless archived – ‘Big data’, but far from unmanageable
  • 4. Key Outcomes: Individual Event Publics
  • 5. Filipinos Marketing / PR Adelaide Perth / PR Wine News / Business Latika Bourke Food Journalism / Politics / News Australia on TwitterAnnabel Crabb Mumbrella Leigh Sales Fashion / Style / Parenting Malcolm Turnbull Marie Claire ABC News Crikey Fashion / Magazines Celebrities / Media Arts Joe Hockey Mia Freedman Laurie Oakes Sunrise on 7 Tony Abbott Music / Triple J Julia Gillard Matt Preston Kevin Rudd Triple J Teens / TV Hits Wil Anderson TV Football (Soccer) 7pm Project (follower/followee network – 140,000 most connected AFL Radio NRL Australia users, of 550,000 Cricket Hamish and Andy Teens Sports processed so far)
  • 6. Key Outcomes: Classifying Acute Events Unforeseen Crises Counterculture? Televised Events
  • 7. ‘Big Data’ Challenges: Teamwork• Team-based research approaches: – Interdisciplinary: media, communication and cultural studies; social science; informatics; mathematics; statistics; journalism; crisis communication; communication design; computer science; data visualisation; … – Exploratory: rapid prototyping of research methods and tools; use of emerging technology at the bleeding edge; following the data without a clear and specific research goal in mind (beyond ‘mapping online publics’ in general) – Flexible: dealing with real-time data may mean ‘ambulance chasing’ (e.g. #eqnz, #tsunami, #qantas); rapid data analysis and online publication well ahead of journal publication turnarounds; application across wide range of thematic and research domains – Collaborative: towards natural sciences-style lab-based research models; team research and multi-authored publications; individual sub-projects developed and driven by specific team members
  • 8. ‘Big Data’ Challenges: Graduate Training• New postgraduate and postdoctoral skillsets: – Postgraduate and postdoctoral recruitment: need for high-level undergraduate/honours project units to enthuse and encourage promising students; need to recruit well beyond standard media and communication fields means need to be visible in those fields (why would a computer scientist or statistician want to work with us?) – Postgraduate training: need to be able to supervise highly multidisciplinary research projects means multidisciplinary supervision teams; lab-style collaborative research projects means exploration of collaborative postgraduate research and cohort supervision – Risky research: changeable technological frameworks and reliance on third parties means whole PhD projects may be wiped out by a single Twitter API change; lack of university ethics guidelines means need to develop own research ethics and/or follow external standards (e.g. AoIR Ethics Guide)
  • 9. ‘Big Data’ Challenges: Infrastructure• Tools and support for ‘big data’ research: – Data capture: some available (open source) tools; need for customisation and further development; need for reliable, always-on capture infrastructure (and IT support); API changes likely to break existing frameworks; truly big, long term data access can be costly (may need industry partnerships?) – Data processing: need for significant computing power to process and visualise large data corpora; need for computer scientists to help develop customised processing tools addressing specific research questions – Data storage: even Twitter datasets can get very big; no standard solutions for short- and medium-term storage; what about long-term archiving of significant records of public communication (National Library)? – Research Dissemination: publications on real-time events need to be faster than standard journal cycles; need to embrace rapid publication of results and analysis online; also need to share tools (e.g. as open source); but what about sharing datasets to enable independent verification of results?
  • 10. ‘Big Data’ Challenges: Collaborations• Emerging field of research needs shared approaches: – International comparisons: parallel research projects to explore national differences and overlaps – e.g. Twitter and elections; Twitter and crisis communication; … – National consortia: shared infrastructure and datasets for particularly large- scale projects – e.g. comprehensive tracking and analysis of public communication by Australians on Twitter – General sharing of methods and tools: natural sciences-style frameworks for sharing tools and methods (and datasets?) to enable independent verification of research results; development of shared standards for data formats; researcher exchanges and internships – Industry collaborations: e.g. application partnerships with domain partners (media organisations, government departments, etc.); data capture, processing, and storage partnerships with major technology partners (Google, Microsoft, …); perhaps even partnerships with Twitter itself (?); but also need to consider research ethics implications of such partnerships
  • 11. http://mappingonlinepublics.net/@snurb_dot_info@jeanburgess http://mappingonlinepublics.net/