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Election 2010: The View from Twitter

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Paper presented at the InASA 'Double Vision' conference, Sydney, 26 Nov. 2010.

Paper presented at the InASA 'Double Vision' conference, Sydney, 26 Nov. 2010.

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  • 1. Election 2010: The View from Twitter
    Image by campoalto
    Axel Bruns / Jean Burgess
    ARC Centre of Excellence for Creative Industries and Innovation, Brisbane
    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. Data Processing – Twitter
    Typical data structure (#ausvotes):
  • 19. Data Processing – Twitter
    Tools:
    Gawk – Scripting tool für CSV processing (open source)
    Excel – Data aggregation, pivot tables and charts
    Leximancer / WordStat – Keyword extraction, co-occurence matrices
    Gephi – Network analysis and visualisation (open source)
    # Extract @replies for network visualisation
    #
    # this script takes a CSV archive of tweets, and reworks it into network data for visualisation
    #
    # expected data format:
    # text,to_user_id,from_user,id,from_user_id,iso_language_code,source,profile_image_url,geo_type, # geo_coordinates_0,geo_coordinates_1,created_at,time
    #
    # output format:
    # from,to,tweet,time,timestamp
    #
    # the script extracts @replies from tweets, and creates duplicates where multiple @replies are
    # present in the same tweet - e.g. the tweet "@one @two hello" from user @user results in
    # @user,@one,"@one @two hello" and @user,@two,"@one @two hello"
    #
    # Released under Creative Commons (BY, NC, SA) by Axel Bruns - a.bruns@qut.edu.au
    BEGIN {
    print "from,to,tweet,time,timestamp"
    }
    /@([A-Za-z0-9_]+)/ {
    a=0
    do {
    match(substr($1, a),/@([A-Za-z0-9_]+)?/,atArray)
    a=a+atArray[1, "start"]+atArray[1, "length"]
    if (atArray[1] != 0) print $3 "," atArray[1] "," $1 "," $12 "," $13
    } while(atArray[1, "start"] != 0)
    }
    # filter.awk - Filter list of tweets
    #
    # this script takes a CSV or other list of tweets, and removes any lines that don't include RT @username
    # the script preserves the first line, expecting that it contains header information
    #
    # script expects command-line argument search={searchcriteria} _before_ the input CSV filename
    # enclose the search term in quotation marks if it contains any special characters
    #
    # e.g.: gawk -F , -f filter.awk search="(julia|gillard)" tweets.csv >filteredtweets.csv
    #
    # expected data format:
    # CSV or simple list of tweets, line-by-line
    #
    # output format:
    # same as above, listing only retweets
    #
    # Released under Creative Commons (BY, NC, SA) by Axel Bruns - a.bruns@qut.edu.au
    BEGIN {
    getline
    print $0
    }
    tolower($0) ~ search {
    print $0
    }
  • 20. Prelude: Leadership #spill
    23 June, 19:00-00:00:Speculation
    24 June, 08:00-15:00:Party Vote & Aftermath
  • 21. #spill Discussion Network (Node size: indegree [most @replies received]; node colour: outdegree [most @replies sent])
  • 22. #ausvotes: Overall Activity (17 July – 24 Aug. 2010)
  • 23. #ausvotes: Discussion Network(17 July to 25 Aug. 2010 / All @replies / Node size: Indegree / Node colours: betweenness centrality)
  • 24. #ausvotes: Mentions of the Parties (normalised per day)
  • 25. #ausvotes: Mentions of the Leaders (normalised per day)
  • 26. #ausvotes: Mentions of the Leaders (cumulative)
  • 27. #ausvotes: Key Themes
  • 28. #ausvotes: Key Themes (normalised per day)
  • 29. #ausvotes: Distractions (normalised per day)
    Labor’sTwibbon Campaign  RTs
  • 30. #ausvotes: Distractions
  • 31. Notes and Limitations
    Twapperkeeper relies on #hashtags
    Problem if #hashtags are inconsistent/unclear
    Follow-on @replies and retweets may not continue to use #hashtags
    Casual commenters may not use #hashtags in the first place
    May miss early developments – e.g. #hashtag standardisation
    Twitter as a subset of society:
    Broadband policy and Internet filter over-, asylum seekers underrepresented
    #hashtag use is a further sign of self-selection
    Need to look to Twitterfirehose for more comprehensive picture
    Need to track baseline activity to understand how exceptional #ausvotes was
    See more at mappingonlinepublics.net – up next: time-based animations...
    Or find us at @snurb_dot_info and @jeanburgess
  • 32. http://mappingonlinepublics.net/
    Image by campoalto
    @snurb_dot_info
    @jeanburgess