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Studying	  Facebook	  via	  Data	  Extrac6on	  The	  Netvizz	  Applica6on	  Bernhard	  Rieder	  Universiteit	  van	  Amste...
Overview	  Compared	  to	  TwiGer,	  Facebook	  is	  difficult	  to	  study	  through	  data	  extrac6on	  but	  also	  has	...
Two	  kinds	  of	  quan6ta6ve	  analysis	  Sta$s$cs	  	  Observed:	  objects	  and	  proper$es	  Inferred:	  rela$ons	  Da...
Personal	  network	  Nodes:	  users	  /	  links:	  "friendship"	  Good	  star6ng	  point	  for	  learning	  network	  anal...
Personal	  "like"	  network	  Nodes:	  users	  &	  liked	  objects	  ("bipar6te	  graphe")	  	  /	  links:	  "liking"	  A	...
FB	  group	  "Islam	  is	  dangerous"	  Friendship	  network,	  color:	  betweenness	  centrality	  2.339	  members	  Aver...
FB	  group	  "Islam	  is	  dangerous"	  Friendship	  network,	  color:	  Interface	  language	  en_us,	  de,	  en_uk,	  it...
Mapping	  European	  Extremism	  (aggregate	  groups)	  Friendship	  rela6ons	  of	  18	  extreme-­‐right	  groups	  User	...
FB	  group	  "Islam	  is	  dangerous"	  Interac6on	  network	  
Facebook	  Page	  "ElShaheeed",	  June	  2010	  –	  June	  2011,	  (Poell	  /	  Rieder,	  forthcoming)	  7K	  posts,	  700...
New	  media	  plaSorms	  funnel	  prac6ces	  into	  reduced	  and	  largely	  formal	  "grammars	  of	  ac6on"	  (Agre	  1...
FB	  Page	  "ElShaheeed",	  June	  2010	  –	  June	  2011	  comment	  6mescaGer	  
FB	  Page	  "ElShaheeed",	  June	  2010	  –	  June	  2011	  comment	  6mescaGer,	  log10	  y	  scale	  
FB	  "ElShaheeed",	  June	  2010	  –	  June	  2011	  comment	  6mescaGer,	  log10	  y	  scale,	  likes	  on	  comments	  
FB	  page	  "Stop	  the	  Islamiza6on	  of	  the	  World"	  Number	  of	  posts	  and	  reac6ons	  
Facebook	  Page	  "ElShaheeed",	  June	  2010	  –	  June	  2011:	  scaGerplot	  comments	  /	  likes,	  per	  post	  type	  
FB	  page	  "Stop	  the	  Islamiza6on	  of	  the	  World"	  
FB	  page	  "Educate	  children	  about	  the	  evils	  of	  Islam"	  1.586	  likes,	  253	  users	  commen6ng	  or	  liki...
FB	  page	  "Educate	  children	  about	  the	  evils	  of	  Islam"	  Links	  have	  more	  comments,	  photos	  more	  li...
FB	  pages	  of	  New	  York	  Times	  and	  Wall	  Street	  Journal	  (aggregate	  pages)	  30	  latest	  posts,	  27K	  ...
Facebook	  page	  like	  network	  Seed:	  Stop	  Islamiza6on	  of	  the	  World	  Crawl	  depth:	  2	  
Studying	  extremism	  on	  Facebook	  Some	  examples	  from	  the	  Digital	  Methods	  Ini6a6ves	  data	  sprint	  on	 ...
Conclusions	  Netvizz	  exports	  a	  variety	  of	  data	  files	  in	  common	  formats	  for	  a	  variety	  of	  sec6on...
Thank	  You	  hGps://apps.facebook.com/netvizz/	  	  rieder@uva.nl	  hGps://www.digitalmethods.net	  hGp://thepoli6csofsys...
Studying Facebook via Data Extraction: a Netvizz tutorial at the Digital Methods Summer School 2013
Studying Facebook via Data Extraction: a Netvizz tutorial at the Digital Methods Summer School 2013
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Studying Facebook via Data Extraction: a Netvizz tutorial at the Digital Methods Summer School 2013

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Studying Facebook via Data Extraction: a Netvizz tutorial at the Digital Methods Summer School 2013

  1. 1. Studying  Facebook  via  Data  Extrac6on  The  Netvizz  Applica6on  Bernhard  Rieder  Universiteit  van  Amsterdam  Mediastudies  Department  
  2. 2. Overview  Compared  to  TwiGer,  Facebook  is  difficult  to  study  through  data  extrac6on  but  also  has  important  advantages:    ☉  complicated  API,  very  complex  and  opaque  privacy  regime,  constant  changes,  etc.  ☉  rich  and  detailed  data,  access  to  full  6melines,  etc.    Goal:  lower  the  threshold  for  working  with  quan6ta6ve  and  computa6onal  approaches,  thereby  fostering  transversal  thinking;  open  the  walled  garden.    Netvizz  is  a  Facebook  applica6on  that  exports  a  variety  of  data  files  in  common  formats  for  a  variety  of  sec6ons  of  the  Facebook  plaSorm.    Humanists  and  social  scien6sts  are  oUen  interested  in  descrip6ve  sta6s6cs  rather  than  models  or  advanced  metrics;  data  stays  close  to  the  medium.  
  3. 3. Two  kinds  of  quan6ta6ve  analysis  Sta$s$cs    Observed:  objects  and  proper$es  Inferred:  rela$ons  Data  representa6on:  the  table        Visual  representa6on:  quan$ty  charts          Grouping:  class  (similar  proper$es)  Graph-­‐theory    Observed:  objects  and  rela$ons  Inferred:  structure  Data  representa6on:  the  matrix        Visual  representa6on:  network  diagrams          Grouping:  clique  (dense  rela$ons)  
  4. 4. Personal  network  Nodes:  users  /  links:  "friendship"  Good  star6ng  point  for  learning  network  analysis  
  5. 5. Personal  "like"  network  Nodes:  users  &  liked  objects  ("bipar6te  graphe")    /  links:  "liking"  A  post-­‐demographical  view  on  social  rela6ons  and  culture  
  6. 6. FB  group  "Islam  is  dangerous"  Friendship  network,  color:  betweenness  centrality  2.339  members  Average  degree  of  39.69  81.7%  have  at  least  one  friend  in  the  group  55.4%  five  or  more  37.2%  have  20  or  more  founder  and  admin  has  609  friends  
  7. 7. FB  group  "Islam  is  dangerous"  Friendship  network,  color:  Interface  language  en_us,  de,  en_uk,  it  dominate  
  8. 8. Mapping  European  Extremism  (aggregate  groups)  Friendship  rela6ons  of  18  extreme-­‐right  groups  User  names  are  unique!  (gephi  can  fuse  networks)  
  9. 9. FB  group  "Islam  is  dangerous"  Interac6on  network  
  10. 10. Facebook  Page  "ElShaheeed",  June  2010  –  June  2011,  (Poell  /  Rieder,  forthcoming)  7K  posts,  700K  users,  3.6M  comments,  10M  likes,  work  in  progress!  
  11. 11. New  media  plaSorms  funnel  prac6ces  into  reduced  and  largely  formal  "grammars  of  ac6on"  (Agre  1989);  data  is  therefore  very  clean,  very  complete,  and  very  detailed.    Can  be  imported  with  great  ease  into  standard  packages  for  sta6s6cal  (e.g.  R,  Excel,  Rapidminer)  or  network  analysis  (e.g.  gephi,  Pajek).  Data  and  tools  
  12. 12. FB  Page  "ElShaheeed",  June  2010  –  June  2011  comment  6mescaGer  
  13. 13. FB  Page  "ElShaheeed",  June  2010  –  June  2011  comment  6mescaGer,  log10  y  scale  
  14. 14. FB  "ElShaheeed",  June  2010  –  June  2011  comment  6mescaGer,  log10  y  scale,  likes  on  comments  
  15. 15. FB  page  "Stop  the  Islamiza6on  of  the  World"  Number  of  posts  and  reac6ons  
  16. 16. Facebook  Page  "ElShaheeed",  June  2010  –  June  2011:  scaGerplot  comments  /  likes,  per  post  type  
  17. 17. FB  page  "Stop  the  Islamiza6on  of  the  World"  
  18. 18. FB  page  "Educate  children  about  the  evils  of  Islam"  1.586  likes,  253  users  commen6ng  or  liking  on  last  200  posts  
  19. 19. FB  page  "Educate  children  about  the  evils  of  Islam"  Links  have  more  comments,  photos  more  likes.  
  20. 20. FB  pages  of  New  York  Times  and  Wall  Street  Journal  (aggregate  pages)  30  latest  posts,  27K  users  liking  or  commen6ng  (user  ids  are  unique!)  
  21. 21. Facebook  page  like  network  Seed:  Stop  Islamiza6on  of  the  World  Crawl  depth:  2  
  22. 22. Studying  extremism  on  Facebook  Some  examples  from  the  Digital  Methods  Ini6a6ves  data  sprint  on  an6-­‐Islamism  and  right  wing  extremism.    Four  aspects  of  SNS  we  wanted  to  study:  ☉  Coordina6on,  social  networking,  and  social  support  for  extremists  ☉  Broadcas6ng  and  mobiliza6on  channel  for  extremists  ☉  Expressions  from  diffuse  publics  ☉  Debate  and  encounter  around  Islam  
  23. 23. Conclusions  Netvizz  exports  a  variety  of  data  files  in  common  formats  for  a  variety  of  sec6ons  of  the  Facebook  plaSorm  and  can  be  used  in  many  different  research  designs.      Netvizz  aGempts  to  lower  the  threshold  for  quan6ta6ve  work  on  Facebook,  allowing  for  closer  connec6ons  with  qualita6ve,  interpreta6ve  thinking.    Easy  access  to  visualiza6on  techniques  is  crucial  for  this  approach.  
  24. 24. Thank  You  hGps://apps.facebook.com/netvizz/    rieder@uva.nl  hGps://www.digitalmethods.net  hGp://thepoli6csofsystems.net  "Far  be@er  an  approximate  answer  to  the  right  ques$on,  which  is  oBen  vague,  than  an  exact  answer  to  the  wrong  ques$on,  which  can  always  be  made  precise.  Data  analysis  must  progress  by  approximate  answers,  at  best,  since  its  knowledge  of  what  the  problem  really  is  will  at  best  be  approximate."  (Tukey  1962)  

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