Mapping Online Publics (Part 1)
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Mapping Online Publics (Part 1)

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Part 1 of the "Making Sense of Twitter: Quantitative Analysis Using Twapperkeeper and Other Tools" workshop, presented at the Communities & Technologies 2011 conference, Brisbane, 29 June 2011.

Part 1 of the "Making Sense of Twitter: Quantitative Analysis Using Twapperkeeper and Other Tools" workshop, presented at the Communities & Technologies 2011 conference, Brisbane, 29 June 2011.

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  • 1. Mapping Online Publics
    Axel Bruns / Jean BurgessARC Centre of Excellence for Creative Industries and Innovation, Queensland University of Technology
    a.bruns@qut.edu.au – @snurb_dot_info / je.burgess@qut.edu.au – @jeanburgesshttp://mappingonlinepublics.net – http://cci.edu.au/
  • 2. Project: New Media and Public Communication
    ARC Discovery (2010-12) – A$410.000
    Axel Bruns (CI), Jean Burgess (SRF) – QUT, Brisbane
    Lars Kirchhoff, Thomas Nicolai (PIs) – Sociomantic Labs, Berlin
    Project blog: http://mappingonlinepublics.net/
    Year 1 Year 2 Year 3
    Social network sources:
    Research tool development and baseline data
    Baseline information:
    • data extraction
    • 6. content creation statistics
    • 7. patterns in terms and themes
    • 8. baseline social networking map
    • 9. interconnections between social network spaces
    Content creation patterns
    Changes over time:
    • short-term statistics
    • 10. regular / seasonal patterns
    Cluster profiling:
    • common themes / patterns
    • 11. lead users
    Focus on specific events
    Cultural dynamics:
    • rapid spread of new ideas
    • 12. communication across clusters
    • 13. thematic discourse analysis
    • 14. relationship with main- stream media coverage
    Research tools:
    • network crawler
    • 15. content scraper
    • 16. content analysis
    • 17. network analysis
  • Methodology – Twapperkeeper
  • 18. Twapperkeeper Data Structure
  • 19. Analysis – Twapperkeeper
  • 20. Methodology – Twitter
  • 21. Key Tools
    Data capture:
    Twapperkeeper / yourTwapperkeeper
    Follow and capture all tweets including set keywords
    Export in standardised CSV / TSV format
    Data processing:
    Gawk
    Process CSV / TSV files – filter, extract, summarise
    Excel
    Statistical analysis and graphing
    Data visualisation:
    Gephi
    Static and dynamic network visualisation
  • 22. #hashtag- / Keyword-Based Datasets
    Hashtags:
    Crises and other unforeseen acute events – #qldfloods, #spill
    Foreseeable short-term events – #royalwedding, #comtech2011
    Longer-term and periodic events – #ausvotes, #qanda
    Hashtag communities – #auspol, #phdchat
    Ironic and emotive hashtags – #winning, #fail
    Hashtag memes – #ThanksGetUp, #tweetlikecharliesheen
    Keywords:
    Brands, celebrities, places – Qantas, Obama, Brisbane
    Abbreviations and other unique identifiers – NATO, NBA, NCC1701
    Markers for current themes – tsunami (vs. #tsunami)
    Twitter user names – captures tweets mentioning them
    What’s missing:
    Pre-filtering of matching tweets (e.g. by location of participating users)
    Capture of follow-on communication (if not using those terms)
    ‘Button’ retweets – not currently captured by yourTwapperkeeper
  • 23. #ausvotes: Overall Activity (17 July – 24 Aug. 2010)
  • 24. #ausvotes: Mentions of the Leaders
  • 25. #ausvotes: Mentions of the Leaders (cumulative)
  • 26. Keyword Co-Occurrence
  • 27. #ausvotes: Key Themes
  • 28. #ausvotes: Discussion Network17 July to 25 Aug. 2010 / All @replies / Node size: Indegree / Node colour: betweenness centrality
  • 29. Dynamic @reply Network Visualisations
    Dynamic visualisation:
    Showing @replies / retweets as they are made
    Connections fading again after a set timeframe, unless renewed
    Network structure either fixed or dynamically adjusted
    Rudd/Gillard leadership challenge, 23 June 2010:
    #spill discussion – from first rumours to confirmed challenge
    Visualised for 18:00 to midnight
    see dynamic animation on Mapping Online Publics
  • 30. Twitter and the 22 Feb. Christchurch Earthquake: #eqnz
    22/2 23 24 25 26 27 28 1/3 2 3 4 5 6 7
  • 31. Twitter and the Christchurch Earthquake: #eqnz @replies
    mainstream media
    authorities
    utilities
  • 32. Twitter and the Christchurch Earthquake: tweet types
  • 33. Twitter and the Christchurch Earthquake: tweet types
  • 34. Twitter and the Christchurch Earthquake: #eqnz @replies
    Changing @reply patterns with the move from rescue to recovery:
  • 35. Twitter and the Christchurch Earthquake: #eqnz Themes
  • 36. Twitter and the Christchurch Earthquake: #eqnz Themes
  • 37. Twitter and the Christchurch Earthquake
    Towards better strategies for social media in disasters:
    February 2011 earthquake building on lessons learnt in 2010
    #eqnz and key Twitter accounts already established
    Several key accounts sharing the load and dividing responsibilities
    More experienced use of Twitter by residents and authorities
    Clear shift in attention after the immediate rescue phase:
    Marked differences in list of most @replied/retweeted accounts
    Some tracking of current problems / issues / fears may be possible
    Decline in overall tweet volume / diversification of #hashtags?
  • 38. And now for something... – #royalwedding
  • 39. ...completely different – ‘Qantas’: @replies + #hashtags
  • 40. Understanding Australian Twitter Use
    What is the Australian Twitteruserbase?
    Large-scale snowballing project
    Starting from selected hashtag communities (e.g. #ausvotes, #qldfloods, #masterchef)
    Identifying participating users, testing for ‘Australianness’:
    Timezone setting, location information, profile information
    Retrieving follower/followee information for each account (very slow)
    Progress update:
    ~550,000 Australian users identified so far
  • 41.
  • 42. Football (rugby)
    Sports
    Football (soccer)
    Twitter Celebrities
    South Australia
    Wine
    Business, PR, Marketing
    Media, Journalism, Politics
    Follower/followee network:~40,000 Australian Twitter users(of ~440,000 known accounts so far) in-degree 20+, dark lines = mutual,colour = indegree, size = outdegree
    Music
  • 43. http://mappingonlinepublics.net/
    Image by campoalto
    @snurb_dot_info
    @jeanburgess