SOCIALMEDIA.ORG/SUMMIT2014ORLANDOOCTOBER 27–29, 2014

Measuring social media asa complex, adaptive system

GERALD KANEBOSTON COLLEGE AND MIT-SLOANMANAGEMENT REVIEW

Measuring  Social  Media  as  a  Complex  Adaptive  System  

Gerald  C.  Kane,  @pro0ane  Associate  Professor        Guest  Editor,  Social  Business  Boston  College        MIT-­‐Sloan  Management  Review  Gerald.kane@bc.edu        gckane@mit.edu  

The  opportunity…  •  Social  media  allows  interacGons  on  size  and  scope  not  previously  possible.  

•  “Digital  trace”  allows  unprecedented  opportuniGes  to  measure  and  analyze  these  behaviors.      

•  It’s  what  got  me  interested  in  SM  –  Facebook  &  Wikipedia.  

Data  is  key  differentiator  for  companies  

Approaches  to  Measurement  

…The  problem  The  resulGng  interacGons  are  oSen  complex….  1.  Non-­‐linear  2.  Co-­‐evoluGon  3.  Self-­‐organizaGon  4.  Emergent  dynamics  …which  can  create  problems  for  measuring  them  effecGvely.  

If  we  don’t  account  for  these  …  

…we  might  miss  something  important.    

The  “canary  in  a  coal  mine.”  •  Founded  in  2001  

•  4.3M  English  arGcles  •  6th  most  heavily  trafficked  website.  

• 15  years  of  excellent  data  for  studying  how  people  collaborate  online.  •  Can  learn  much  about  implicaGons  for  measurement  in  social  media.  

Insight  #1:  Non-­‐linear  (Ransbotham  and  Kane  2011,  MISQ)

•  More  may  be  be`er,  but  only  to  a  certain  point.  •  How  does  membership  turnover  affect  arGcle  development?  

•  Most  online  community  research  usually  assumes  membership  retenGon  is  posiGve  (e.g.  Ma  and  Agarwal  2007,  Butler  2001).  

•  Yet,  research  on  organizaGonal  turnover  suggests  that  some  moderate  amount  of  turnover  is  beneficial  (e.g.  March  1991).  

•  Study:  2065  Featured  ArGcles  between  2001-­‐2009  (3M  revisions,  186  GB  data)  

•  Findings:  Moderate  turnover  beneficial  in  online  communiGes  for  both  likelihood  creaGng  and  retaining  knowledge.  

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1)  PosiGvely  impact  business  outcomes  2)  SB  IniGaGves  meet  objecGves  

ALS  Ice  Bucket  Challenge  

Takeaway  #1  Avoid  Oversimplifying:  Understanding  and  managing  social  media  is  rarely  as  simple  as  you  think  it  is  or  want  it  to  be.  

Insight  #2:  Co-­‐evolution  (Ransbotham,  Kane,  Lurie  2012,  Marketing  Science)  

•  Changes  in  one  part  of  the  plamorm  can  affect  outcomes  in  others.  •  The  proverbial  bu`erfly  flapping  its  wings.  

•  Does  turnover  have  effect  beyond  focal  arGcle?    Is  “quality”  contagious?  •  Collaborators  may  join  new  communiGes  when  leave  old  ones,  transferring  knowledge  from  one  community  to  another.  

•  Like  a  bee  pollinaGng  flowers,  contributors  can  spread  knowledge  from  one  community  to  another.  

•  Study:  40K  contributors  to  16K  medical  arGcles  on  Wikipedia  2001-­‐2009  (2M  revisions,  50GB  data).  •  Created  2-­‐mode  affiliaGon  network  of  arGcles  and  shared  collaborators.  

•  Finding:  Centrality  in  both  local  (degree)  and  global  (closeness)  centrality  predicts  quality  and  popularity  of  content.  •  Online  collaboraGon  may  involve  mulGple  interdependent  communiGes.  

Squares = authors  Circles = articles

Red = Featured Articles  Orange = A-quality Articles Yellow = Good Articles

Light Blue = B-quality Articles  Dark Blue = Start-quality articles

Results

Takeaway  #2  Small,  unexpected  changes  in  one  part  of  the  social  media  environment  can  o@en  have  a  big  impact  on  another.  

Insight  #3:  Emergence  (Kane,  Johnson,  Majchrzak,  Management  Science)  

•  Can  order  evolve  without  any  management  intervenGon  or  formal  leadership  structure?  

•  Study:  In-­‐depth  case  study  of  8K  edits  from  3K  contributors  to  AuGsm  arGcle  from  2001  –  2010.  •  One  of  handful  of  arGcles  that  promoted  to,  demoted  from,  and  re-­‐promoted  to  featured  arGcle  status.  

•  Among  most  heavily  visited  arGcles,  recognized  by  outside  sources  for  quality.  

•  Finding:  CommuniGes  are  both  structured  AND  emergent,  depending  on  the  stage  of  development.      •  DeliberaGon  types  occurred  in  ways  similar  to  soSware  development  lifecycle,  despite  li`le  formal  coordinaGon  mechanism.  

•  Knowledge  arGfact  served  as  coordinaGng  mechanism  

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Sum  of  Joint  Shaping  

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Duration  of  Contribution  for  Top  20  Contributors  (contributors  rank  ordered  by  activity  level)    

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Takeaway  #3  Social  environments  evolve  over  Ame  —  someAmes  rapidly.    

Insight  #4:  Dynamics  (Kane  and  Ransbotham,  ICIS  2013)  

•  Feedback  loops  can  develop,  where  two  characterisGcs  can  mutually  reinforce  one  another.  

•  Dynamics  of  online  peer  producGon  have  never  been  tested.  •  Presumed  that  content  leads  to  viewers,  more  viewers  lead  to  be`er  content.  

•  Does  this  dynamic  exist,  does  it  change  over  Gme?  •  Study:  Same  sample  of  16K  medical  arGcles,  40K  contributors  used  earlier.  •  3SLS  regression,  using  “protected”  as  the  idenGficaGon  variable  (i.e.  affects  contribuGons  but  not  viewership).  

•  Findings:  We  find  evidence  for  hypothesized  collaboraGon  dynamics,  but  a`enuates  over  Gme.  •  Age  of  an  arGcle  is  posiGvely  related  to  viewership,  but  negaGvely  related  to  contribuGon  acGvity.  

•  Anonymity  improves  both  contribuGon  and  viewership.  

Takeaway  #4  Watch  for  sudden  expansions  or  contracAons  in  acAvity,  and  adjust  to  compensate.  

Implications  for  Organizations  • CombinaGon  of  qualitaGve  and  quanGtaGve  data  is  powerful.  

• Embrace  paradox  between  leading  and  following  –  including  customer  communiGes.  

• Provide  Gme  for  employees  to  learn  new  ways  of  working  –  internal  use  for  external  experience.  

•  Its  not  mainly  about  the  technology  –  culture  is  key.    

•  Look  for  leadership  examples  outside  business  (e.g.  military,  non-­‐profits).  

To  Conclude…  •  InteracGons  on  social  media  exhibit  characterisGcs  of  complex  adapGve  systems  •  Non-­‐linear  •  Co-­‐evoluGon  •  Self-­‐organizaGon  •  Emergent  dynamics  

•  If  we  do  not  account  for  these  complex  features,  we  risk  making  mistakes  in  our  analysis  and  interpretaGon  of  our  data.  •  “With  great  data,  comes  great  responsibility.”  

SOCIALMEDIA.ORG/SUMMIT2014ORLANDOOCTOBER 27–29, 2014

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