Election 2010: The View from Twitter

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/

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:
YouTube
Flickr
Twitter
blogs
Research tool development and baseline data
Baseline information:
data extraction
content creation statistics
patterns in terms and themes
baseline social networking map
interconnections between social network spaces
Content creation patterns
Changes over time:
short-term statistics
regular / seasonal patterns
Cluster profiling:
common themes / patterns
lead users
Focus on specific events
Cultural dynamics:
rapid spread of new ideas
communication across clusters
thematic discourse analysis
relationship with main- stream media coverage
Research tools:
network crawler
content scraper
content analysis
network analysis

Methodology – Twapperkeeper

Data Processing – Twitter
Typical data structure (#ausvotes):

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
}

Prelude: Leadership #spill
23 June, 19:00-00:00:Speculation
24 June, 08:00-15:00:Party Vote & Aftermath

#spill Discussion Network (Node size: indegree [most @replies received]; node colour: outdegree [most @replies sent])

#ausvotes: Overall Activity (17 July – 24 Aug. 2010)

#ausvotes: Discussion Network(17 July to 25 Aug. 2010 / All @replies / Node size: Indegree / Node colours: betweenness centrality)

#ausvotes: Mentions of the Parties (normalised per day)

#ausvotes: Mentions of the Leaders (normalised per day)

#ausvotes: Mentions of the Leaders (cumulative)

#ausvotes: Key Themes

#ausvotes: Key Themes (normalised per day)

#ausvotes: Distractions (normalised per day)
Labor’sTwibbon Campaign  RTs

#ausvotes: Distractions

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

http://mappingonlinepublics.net/
Image by campoalto
@snurb_dot_info
@jeanburgess

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Election 2010: The View from Twitter

by Axel Bruns

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