Paper by Axel Bruns, Jean Burgess and Tim Highfield, presented at The Arab Spring: A Symposium on Social Media and the Politics of Reportage, at Swinburne University, Melbourne (8 June 2012).
Inter and Intra-Language Engagement on Twitter in Arab Spring Hashtag Communities
1. Inter- and Intra-Language
Engagement on Twitter
in Arab Spring Hashtag Communities
Assoc. Prof. Axel Bruns, Dr. Jean Burgess, & Dr. Tim Highfield
ARC Centre of Excellence for Creative Industries and Innovation
Queensland University of Technology, Brisbane, Australia
a.bruns@qut.edu.au – je.burgess@qut.edu.au – t.highfield@qut.edu.au
@snurb_dot_info - @jeanburgess - @timhighfield
http://mappingonlinepublics.net/
http://mappingonlinepublics.net/
2. The Arab Spring and Twitter
o Twitter analysis:
o Tracking of key hashtags (#egypt, #libya) throughout 2011
o #egypt: 23 Jan. to 30 Nov. – 7.48m tweets, 445,000 unique users
o #libya: 16 Feb. to 30 Nov. – 5.27m tweets, 476,000 unique users
o Language differentiation:
o Fewer than 10 characters above ASCII 127 tweet is ‘Latin’
o More than 10 characters above ASCII 127 tweet is ‘non-Latin’
o User groups: ‘Latin’ (< 33%), ‘mixed’ (33-66%), ‘non-Latin’ (> 66%)
o User differentiation:
o Lead users: top 1% most active users
o Highly engaged users: next 2-10% active users
o Least active users: bottom 90% active users
http://mappingonlinepublics.net/
10. Comparing Different Phases
o Twitter activity patterns change over time:
o #egypt: 1-28 Feb. vs. 15 June to 15 Sep.
o #libya: 16 Feb. to 15 Mar. vs. 1 Aug. to 30 Sep.
o Early media attention vs. later developments
o Differences in Latin / mixed / non-Latin tweeting?
o Differences between most / least active users?
o Interactions between language groups?
http://mappingonlinepublics.net/
17. Findings
o Clear differences between #egypt and #libya:
o #egypt:
o Significant Latin participation at first, then strong shift towards non-Latin
o May indicate fading of #25Jan hashtag, shift to #egypt for ongoing discussion
o Lead users especially likely to send non-Latin tweets
o More mixed-language users in lead groups
o #libya:
o Latin-dominated throughout, small shift to non-Latin
o May point to limited domestic use of / access to Twitter
o Lead users especially likely to send Latin tweets
o Both:
o Latin users most likely to engage with non-hashtag users
(e.g. news organisations, other external sources)
o Non-Latin users (in #egypt) equally engaging with non-hashtag users and mixed-language
users
o To do:
o What URLs are being shared in each case?
o Are there differences between Latin / non-Latin users?
o Are there differences between more / less active users?
http://mappingonlinepublics.net/