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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/
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/
#egypt
11 Feb.: Mubarak Resigns
#egypt
#egypt
#egypt
#libya
21 Feb.: first reports of unrest




                                   23 Aug.: Bab al-Azizia stormed
#libya
#libya
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/
#egypt

1-28 Feb. 2011       15 June to 15 Sep. 2011
#egypt: @mentions

1-28 Feb. 2011   15 June to 15 Sep. 2011
#egypt: @mentions

1-28 Feb. 2011   15 June to 15 Sep. 2011
#libya

16 Feb. to 15 Mar. 2011   1 Aug. to 30 Sep. 2011
#libya: @mentions

16 Feb. to 15 Mar. 2011   1 Aug. to 30 Sep. 2011
#libya: @mentions

16 Feb. to 15 Mar. 2011   1 Aug. to 30 Sep. 2011
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/
http://mappingonlinepublics.net/

@snurb_dot_info
@jeanburgess
@_StephenH
@DrTNitins
@timhighfield
@cdtavijit




                               http://mappingonlinepublics.net/

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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/
  • 7. #libya 21 Feb.: first reports of unrest 23 Aug.: Bab al-Azizia stormed
  • 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/
  • 11. #egypt 1-28 Feb. 2011 15 June to 15 Sep. 2011
  • 12. #egypt: @mentions 1-28 Feb. 2011 15 June to 15 Sep. 2011
  • 13. #egypt: @mentions 1-28 Feb. 2011 15 June to 15 Sep. 2011
  • 14. #libya 16 Feb. to 15 Mar. 2011 1 Aug. to 30 Sep. 2011
  • 15. #libya: @mentions 16 Feb. to 15 Mar. 2011 1 Aug. to 30 Sep. 2011
  • 16. #libya: @mentions 16 Feb. to 15 Mar. 2011 1 Aug. to 30 Sep. 2011
  • 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/