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Mapping Twitter Communities
       Presented at the MRS ‘Maximizing the Value of Big Data’ conference, London, January 2013




                                                                                        Kyle Findlay (TNS)
                                                                                        Paul Oosterveld (TNS)
                                                                                        Timothy Wilson (Independent)
My understanding of ‘big data’

  Lots of                Resource-             Resource-
                         intensive to          intensive to
  data                   collect               process
  (often unstructured)



  Meta view of           A new world of        …through
                         insights, technique
  old, familiar          s, understanding…
                                               primary
  systems                                      research???
The tools

              Machine learning   Integration of
 Text         and artificial                      Next-gen
                                 multiple data
 analysis     intelligence
                                 sources          databases

 Etc . etc.
 etc.
What we are really   …is mapping complex
doing here though…   systems
What’s the similarity between…



 This?




                                 http://vimeo.com/2543720
What’s the similarity between…




                                       And this?



                            http://www.youtube.com/watch?v=YadP3w7vkJA
And
What’s the similarity between…             this?




                            http://www.youtube.com/watch?v=2guKJfvq4uI
…?

     Networks
Why am I telling
you all this?

        Let’s look at some
        of our primary
        research…

                   …a case study
We mapped the South African Political Twittersphere
Who are the popular   Who is influencing          What is the nature        What sub-
 and/or influential    whom within a               of their influence i.e.   communities exist
 individuals in my     specific topic of           how are they              within my online
 network?
                       interest to me?             influencing people?       community?



 What topics are       What are people saying      What do these sub-        Who is sharing
                       about my                    communities believe
 people talking        brand, political            and how do they relate    my content
 about?                party, charity, music, et   to my brand/              online?
                       c.?                         organisation/etc?



 What are the          Are they saying             How far is word of        How is my
 emerging topics       mostly positive or          my brand or               messaging being
 and trends?           negative things?            negative publicity        shared online?
                                                   being spread?




Relevance?
A paradigm shift…
                                 Participants not respondents
        Market                   (CASRO)


        researchers              “Private gardens” vs. public spaces

        misunderstand
        social media             “Representative sample” what?

        research…

Source: Barber, Michael, 2011
Actual tweets




We collected two types of data


Twitter user data
Tweets data summary   Number of tweets collected:
                        @helenzille = 28,500
                        @PresidencyZA = 3,500
                        @SAPresident = 2,900


                      Time period: Sept 2011 – March 2012
Target
                                               nodes




                                Source nodes

                Collected         64,357

                Cleaned           58,349




                Distance <= 4     37,695
                                                        2.5 million

                Distance <= 3     20,460

                Distance <= 2     12,753

                Distance <= 1      4,479

                Distance = 0         3




            Time period: April 2012



Twitter user data summary
3.0   2.0   1.0   Ego
Topic modelling
                       Hashtags
Types of analyses


                       Structural network
                              Betweeness centrality
                              Authority (HITS)
                              Community detection
                       Influence networks


                    Image sources:   Knowledge Matters. http://www.durantlaw.info/category/miscellaneous/strategy
                                     Gutiérrez-Pérez, JA, et al. 2011. Application of graph-spectral methods in the vulnerability assessment of water supply networks
Actual tweets




We collected two types of data
@SAPresident + @PresidencyZA
@helenzille
We collected two types of data


Twitter user data
The South African political
Twittersphere
Structural network by
       follower count
Top 10 by
follower count
Structural network by
betweeness centrality
Top 10 by
betweeness centrality
Structural network by
            Authority
Top 10 by
Authority
Black
         influentials

                            News media

Technology


                                 Proudly SA
                                      & DA


              Sportsmen &
              celebrities




                                              Community detection
                                                             Louvain
Community detection
Louvain
Top 10 sportsmen & celebrities
Follower count




Helen Zille            Bryan Habana    John Smit         Albie Morkel    Mark Boucher
157,642                100,141         84,529            94,593          84,564




Cricket South          South African   Trevor Immelman   Steve Hofmeyr   94.7 Highveld
Africa 61,536          Rugby 60,280    53,777            39,351          Radio 37,554
Top 10 sportsmen & celebrities
Betweeness centrality




Rob van Vuuren         Arno Carstens   Cape Talk 567   Alex van Tonder   Sam Wilson
16,610                 11,562          11,685          10,065            6,756




Karen Zoid             Lead SA         Jeannie D       Creative Cape     The Foodie (David)
14,410                 18,786          38,858          Town 6,259        5,219
Top 10 news media
                                                                               Follower count




Yaseen Theba       Mail & Guardian   South African       BBC Africa     Redi Tlhabi
77,191             57,320            Presidency 53,413   49,425         45,866




SA Breaking News   City Press        allAfrica.com       Mandy Wiener   Evita Bezuidenhout
38,487             36,376            32,788              31,296         30,139
Top 10 news media
                                                                         Betweeness centrality




Mandy Wiener      Sam Mkokeli        Ferial Haffajee   Mail & Guardian   Robyn Clark
31,296            3,033              29,530            57,320            1,598




Sipho Hlongwane   US South African   Verashni Pillay   The Big Issue     Stephen Grootes
9,093             Embassy 17,803     8,304             4,363             19,708
Top 10 technology
Follower count




Aki Anastasiou      Jaco van Wyk   Simon Dingle       Dave Duarte   Toby Shapshak
28,063              20,087         15,386             9,563         9,073




Finance24           Mxit           Matthew Buckland   Pieter Uys    Duncan McLeod
8,184               7,651          7,033              6,654         6,194
Top 10 technology
Betweeness centrality




Matthew Buckland        Dave Duarte    Aki Anastasiou   Raoul de Jongh   Toby Shapshak
7,033                   9,563          28,063           4,133            9,073




Saul Kropman            Mike Sharman   Uno de Waal      Pete Flynn       Cathryn Reece
2,530                   4,680          2,469            1,008            2,302
Top 10 black influentials
                                                                                 Follower count




Bonang Matheba   Julius Malema           Dineo Ranaka   Jacob Zuma       Black Coffee
231,696          (unconfirmed) 192,482   142,745        136,403          97,302




David Kau        Terry Pheto             Pabi Moloi     Claire Mawisa    Kuli Roberts
83,169           64,117                  66,841         54,562           57,352
Top 10 black influentials
                                                                         Betweeness centrality




Khaya Dlanga     Simphiwe Dana   TimesLIVE     Julius Malema            Xolisa Dyeshana
46,101           33,986          52,706        (unconfirmed) 192,482    4,747




Jason Von Berg   David Kau       Zama Ndlovu   Bonang Matheba           Mvelase Peppetta
4,445            83,169          6,718         231,696                  2,804
Top 10 proudly SA & DA
Follower count




Lindiwe Mazibuko         SA The Good News   Brand South Africa   SouthAfrica.info   City of Cape Town
30,460                   12,483             8,549                8,575              7,725




Ryan Coetzee             Mmusi Maimane      Tim Harris           DA Youth           Andrew Boraine
3,528                    4,917              2,792                3,239              2,214
Top 10 proudly SA & DA
Betweeness centrality




Lindiwe Mazibuko         Ryan Coetzee    Climate Smart   Tim Harris        Mmusi Maimane
30,460                   3,528           Cape Town 867   2,792             4,917




Solly Malatsi            Gareth van      Mbali Ntuli     Cape Town         Phumzile Van
853                      Onselen 2,085   1,903           Green Map 1,408   Damme 1,588
Influence is contextual
Actual tweets




We collected two types of data
#ANCYL
#ANCYL #malema #ANCYLmarch | 143 tweets
#POIB
#POIB #POSIB #stopthesecrecybill #secrecybill
#BlackTuesday | 654 tweets




                                                Interesting:
                                                @PatriciaDeLille:
                                                • High presence (mentions)
                                                • Low influence (interactions)
@hopeleighm
                             @PresidencyZA
                              @SAPresident




@helenzille and members
of the Democratic Alliance
Issues


  Scalability          Privacy          Data sourcing

  Resource intensive   Paradigm shift   Rise of walled gardens


  New skills                            Reseller limitations
Next steps




             +
A new kind of research                              Complex systems

                                             Influence and dynamics

                                                          Public data

                                             MR = computer science?




                         CONCLUSIONS
                                                    Primary research

Paradigm shift                                Representative what!?

                                       Participants, not respondents

                                       3rd party disruptive innovation
Thank you
@socialphysicist
Appendices
Top 10 sportsmen & celebrities
Authority




Helen Zille            Rob van Vuuren    John Smit    Arno Carstens   Cape Talk 567




Bryan Habana           Alex van Tonder   Sam Wilson   Karen Zoid      Vanessa Raphaely
Top 10 news media
                                                                               Authority




                                     South African
Mail & Guardian    Ferial Haffajee                   Mandy Wiener   Stephen Grootes
                                     Presidency




Evita Bezuidenhout Redi Tlhabi       Zapiro          Max du Preez   Philip de Wet
Top 10 technology
Authority




Aki Anastasiou      Matthew Buckland   Toby Shapshak       Dave Duarte   Simon Dingle




Duncan McLeod       Memeburn           Cherryflava Media   Pieter Uys    Mike Sharman
Top 10 black influentials
                                                                        Authority




                                         Julius Malema
Jacob Zuma    TimesLIVE   Khaya Dlanga                    Leanne Manas
                                         (unconfirmed)




Riaad Moosa   Zakes Mda   David Kau      Simphiwe Dana    Pabi Moloi
Top 10 proudly SA & DA
Authority




Lindiwe Mazibuko         Ryan Coetzee    SA The Good News   City of Cape Town   Mmusi Maimane




                                         Gareth van
Tim Harris               David Maynier                      Andrew Boraine      Gareth Morgan
                                         Onselen
Topic
modelling
Big Data: Mapping Twitter Communities

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Big Data: Mapping Twitter Communities

  • 1. Mapping Twitter Communities Presented at the MRS ‘Maximizing the Value of Big Data’ conference, London, January 2013 Kyle Findlay (TNS) Paul Oosterveld (TNS) Timothy Wilson (Independent)
  • 2. My understanding of ‘big data’ Lots of Resource- Resource- intensive to intensive to data collect process (often unstructured) Meta view of A new world of …through insights, technique old, familiar s, understanding… primary systems research???
  • 3. The tools Machine learning Integration of Text and artificial Next-gen multiple data analysis intelligence sources databases Etc . etc. etc.
  • 4. What we are really …is mapping complex doing here though… systems
  • 5. What’s the similarity between… This? http://vimeo.com/2543720
  • 6. What’s the similarity between… And this? http://www.youtube.com/watch?v=YadP3w7vkJA
  • 7. And What’s the similarity between… this? http://www.youtube.com/watch?v=2guKJfvq4uI
  • 8. …? Networks
  • 9. Why am I telling you all this? Let’s look at some of our primary research… …a case study
  • 10. We mapped the South African Political Twittersphere
  • 11. Who are the popular Who is influencing What is the nature What sub- and/or influential whom within a of their influence i.e. communities exist individuals in my specific topic of how are they within my online network? interest to me? influencing people? community? What topics are What are people saying What do these sub- Who is sharing about my communities believe people talking brand, political and how do they relate my content about? party, charity, music, et to my brand/ online? c.? organisation/etc? What are the Are they saying How far is word of How is my emerging topics mostly positive or my brand or messaging being and trends? negative things? negative publicity shared online? being spread? Relevance?
  • 12. A paradigm shift… Participants not respondents Market (CASRO) researchers “Private gardens” vs. public spaces misunderstand social media “Representative sample” what? research… Source: Barber, Michael, 2011
  • 13. Actual tweets We collected two types of data Twitter user data
  • 14. Tweets data summary Number of tweets collected: @helenzille = 28,500 @PresidencyZA = 3,500 @SAPresident = 2,900 Time period: Sept 2011 – March 2012
  • 15. Target nodes Source nodes Collected 64,357 Cleaned 58,349 Distance <= 4 37,695 2.5 million Distance <= 3 20,460 Distance <= 2 12,753 Distance <= 1 4,479 Distance = 0 3 Time period: April 2012 Twitter user data summary
  • 16. 3.0 2.0 1.0 Ego
  • 17. Topic modelling Hashtags Types of analyses Structural network Betweeness centrality Authority (HITS) Community detection Influence networks Image sources: Knowledge Matters. http://www.durantlaw.info/category/miscellaneous/strategy Gutiérrez-Pérez, JA, et al. 2011. Application of graph-spectral methods in the vulnerability assessment of water supply networks
  • 18. Actual tweets We collected two types of data
  • 21. We collected two types of data Twitter user data
  • 22. The South African political Twittersphere
  • 23. Structural network by follower count
  • 26. Top 10 by betweeness centrality
  • 29. Black influentials News media Technology Proudly SA & DA Sportsmen & celebrities Community detection Louvain
  • 31. Top 10 sportsmen & celebrities Follower count Helen Zille Bryan Habana John Smit Albie Morkel Mark Boucher 157,642 100,141 84,529 94,593 84,564 Cricket South South African Trevor Immelman Steve Hofmeyr 94.7 Highveld Africa 61,536 Rugby 60,280 53,777 39,351 Radio 37,554
  • 32. Top 10 sportsmen & celebrities Betweeness centrality Rob van Vuuren Arno Carstens Cape Talk 567 Alex van Tonder Sam Wilson 16,610 11,562 11,685 10,065 6,756 Karen Zoid Lead SA Jeannie D Creative Cape The Foodie (David) 14,410 18,786 38,858 Town 6,259 5,219
  • 33. Top 10 news media Follower count Yaseen Theba Mail & Guardian South African BBC Africa Redi Tlhabi 77,191 57,320 Presidency 53,413 49,425 45,866 SA Breaking News City Press allAfrica.com Mandy Wiener Evita Bezuidenhout 38,487 36,376 32,788 31,296 30,139
  • 34. Top 10 news media Betweeness centrality Mandy Wiener Sam Mkokeli Ferial Haffajee Mail & Guardian Robyn Clark 31,296 3,033 29,530 57,320 1,598 Sipho Hlongwane US South African Verashni Pillay The Big Issue Stephen Grootes 9,093 Embassy 17,803 8,304 4,363 19,708
  • 35. Top 10 technology Follower count Aki Anastasiou Jaco van Wyk Simon Dingle Dave Duarte Toby Shapshak 28,063 20,087 15,386 9,563 9,073 Finance24 Mxit Matthew Buckland Pieter Uys Duncan McLeod 8,184 7,651 7,033 6,654 6,194
  • 36. Top 10 technology Betweeness centrality Matthew Buckland Dave Duarte Aki Anastasiou Raoul de Jongh Toby Shapshak 7,033 9,563 28,063 4,133 9,073 Saul Kropman Mike Sharman Uno de Waal Pete Flynn Cathryn Reece 2,530 4,680 2,469 1,008 2,302
  • 37. Top 10 black influentials Follower count Bonang Matheba Julius Malema Dineo Ranaka Jacob Zuma Black Coffee 231,696 (unconfirmed) 192,482 142,745 136,403 97,302 David Kau Terry Pheto Pabi Moloi Claire Mawisa Kuli Roberts 83,169 64,117 66,841 54,562 57,352
  • 38. Top 10 black influentials Betweeness centrality Khaya Dlanga Simphiwe Dana TimesLIVE Julius Malema Xolisa Dyeshana 46,101 33,986 52,706 (unconfirmed) 192,482 4,747 Jason Von Berg David Kau Zama Ndlovu Bonang Matheba Mvelase Peppetta 4,445 83,169 6,718 231,696 2,804
  • 39. Top 10 proudly SA & DA Follower count Lindiwe Mazibuko SA The Good News Brand South Africa SouthAfrica.info City of Cape Town 30,460 12,483 8,549 8,575 7,725 Ryan Coetzee Mmusi Maimane Tim Harris DA Youth Andrew Boraine 3,528 4,917 2,792 3,239 2,214
  • 40. Top 10 proudly SA & DA Betweeness centrality Lindiwe Mazibuko Ryan Coetzee Climate Smart Tim Harris Mmusi Maimane 30,460 3,528 Cape Town 867 2,792 4,917 Solly Malatsi Gareth van Mbali Ntuli Cape Town Phumzile Van 853 Onselen 2,085 1,903 Green Map 1,408 Damme 1,588
  • 42. Actual tweets We collected two types of data
  • 44. #POIB #POIB #POSIB #stopthesecrecybill #secrecybill #BlackTuesday | 654 tweets Interesting: @PatriciaDeLille: • High presence (mentions) • Low influence (interactions)
  • 45. @hopeleighm @PresidencyZA @SAPresident @helenzille and members of the Democratic Alliance
  • 46. Issues Scalability Privacy Data sourcing Resource intensive Paradigm shift Rise of walled gardens New skills Reseller limitations
  • 48. A new kind of research Complex systems Influence and dynamics Public data MR = computer science? CONCLUSIONS Primary research Paradigm shift Representative what!? Participants, not respondents 3rd party disruptive innovation
  • 51. Top 10 sportsmen & celebrities Authority Helen Zille Rob van Vuuren John Smit Arno Carstens Cape Talk 567 Bryan Habana Alex van Tonder Sam Wilson Karen Zoid Vanessa Raphaely
  • 52. Top 10 news media Authority South African Mail & Guardian Ferial Haffajee Mandy Wiener Stephen Grootes Presidency Evita Bezuidenhout Redi Tlhabi Zapiro Max du Preez Philip de Wet
  • 53. Top 10 technology Authority Aki Anastasiou Matthew Buckland Toby Shapshak Dave Duarte Simon Dingle Duncan McLeod Memeburn Cherryflava Media Pieter Uys Mike Sharman
  • 54. Top 10 black influentials Authority Julius Malema Jacob Zuma TimesLIVE Khaya Dlanga Leanne Manas (unconfirmed) Riaad Moosa Zakes Mda David Kau Simphiwe Dana Pabi Moloi
  • 55. Top 10 proudly SA & DA Authority Lindiwe Mazibuko Ryan Coetzee SA The Good News City of Cape Town Mmusi Maimane Gareth van Tim Harris David Maynier Andrew Boraine Gareth Morgan Onselen