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Big Data: Mapping Twitter Communities

Socialphysicist
Socialphysicist

A deck presented at the MRS 'Maximising the Value of Big Data' conference in London, January 2013. Presents my view of big data and the potential it gives us for mapping the systems that we deal with on a day-to-day basis. Big data holds the promise of providing us with a meta-view of the systems that we all think we are so familiar with. I think we will find that the woods look nothing like the trees.

Big Data: Mapping Twitter Communities

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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
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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