- Who are the Influentials? - Structural Social Influence Model of Facebook

WOM

A Facebook Word-of-Mouth for the Emergence of Collective Network: A

Cyber-field Study

Kyounghee “Hazel” Kwon (PhD)

What this project about…

�  Social  Network  Sites  as  the  prevalent  Web  2.0  Service  

�  A  culture  of  sharing  on  SNS  

�  Interpersonal/relational  sharing  produces  social  information  

�  Social  information  produces  social  influence    

Facebook Social Information

Levels of Influence Studied… �  Individual  Level:  Personal  Influence  (the  “Influentials”)  

 �  Social  Network-­‐Level:  Structural  Social  Influence  

   

Taking  Network  Approach  is  advantageous  and  contributory  to  the  limited  CMC  literature  that  focus  on  intra-­‐individual  psychological  processing          

Social Network Approach with Facebook Data

�  FB  Friends  supported  by  computerized  relational  tools:  Intensive  representation  of  +300  active  personal  networks  

�  Full  visualization  of  ego-­‐networks  using  FB’s  API:  overcome  (1)  exclusion  of  weak  ties  (2)  imperfect  recall  

� A  cyber-­‐behavioral    field  study:  Mobilizing  a  campus  advocacy  network  on  FB  through  WOM    

�  Recruit  “opinion  leader”  (OL)  players  �  OL…  (1)  sent  the  group  invitation  message  to  their  college  friends  

(2)  did  the  survey  about  themselves  (3)  let  the  researcher  access  to  the  personal  network  data  in  FB  :friends  names  list  +  sociometric  (optional)    

�  In  the  end  of  project,  the  researcher  matched  those  who  joined  the  group  with  the  names  identified  in  the  name  list.  

STUDY 1 Individual Level: Personal Influence

�  Personal  influence  becomes  critical  for  others’  decision  making  process  

�  “More  influential”  than  others?  �  Profiling  opinion  leaders  has  been  a  major  topic  in  diffusion,  marketing,  &  political  comm  

�  Four  approaches:  sociometric,  key  informant’s  rating,  self-­‐designating,  observation  

� Compare self-designating and observation

methods in identifying the influentials � Characterize the influentials in FB context �  Emphasis on social attributes      -­‐  Innovators  are  not  always  the  influentials  (Rogers,  1995.  p.388)  

     -­‐  Must  have  “follower  groups”          -­‐  Equivalent  terms:  Maven,  buzzer,  navigator,  social  connector,  network  hub  

     -­‐  How  to  measure    FB-­‐specific  social  attributes?    

1. Personality Trait

2. Gregariousness

3. Social Activities

4. Cosmopoliteness

H1

H2

H3

H4

•  Weimann’s ‘Personality Strength Index’ (PS index) 1. Personality Trait

•  Facebook interaction, updating others’ profiles & popularity,

2. Gregariousness

•  Membership in FB group (general and topic specific), contact range

3. Social Activities

•  Network heterogeniety (more in next slide) 4. Cosmopoliteness

�  Self-­‐designated  OL-­‐ship:  King  and  Summers’  OL  scale  (KS  scale)  (M=7.05  out  of  10,  SD  =  2.17)  

�  Observed  OL-­‐ship:  number  of  members  mobilized  by  each  OL  player’s  invitation  (M=23.51)  (Duplicated  invitees  excluded:  1711  out  of  7486,  22.86%).  Transformed  due  to  severe  skewness  (M=2.81,  SD  =  1.49)    

1.  Density  (D):  the  extent  to  which  

friends  are  known  to  one  another  within  OL’s  ego  network      

2.  Clustering  Coefficient  (CC)  :  The  extent  to  which  acquainted  friends  share  mutual  friends    

       (averaging  Ci,  where  Ci  

is  the  density  of  a  sub-­‐graph  consisting  of  a  set  of  neighboring  nodes  that  are  directly  connected  to  the  focal  node  i  and  the  subsequent  edges.)  

i

I’s neighboring nodes=5, subsequent edges=2

Ci = 0.2

3.  Girvan-­‐Newman  community  structure        -­‐  “Edge-­‐betweenness”        (1)  calculate  edge-­‐betweenness  for  all  edges      (2)  remove  the  edge  of  the  highest  betweenness      (3)  recalculate  for  the  remaining  edges      (4)  repeating  from  (2)  until  no  edge  remains      (5)  produce  number  of  sub-­‐groups  (Girvan  &  

Newman,  2002)                The  more  sub-­‐groups  are  identified,  the  

more  the  personal  network  is  heterogeneous        

Personality

Gregarious

Social Activity

Personality

Gregarious

Social Activity

Bad model fit!

�  Incongruence  between  self-­‐designation  and  observation:  

 (1)  Observation  more  valid  method    (2)  Online  collective  action  as  requiring  less  informational  influence  ?  

 (3)  No  significance  regarding  self-­‐designation  OL  +  positive  effects  of  network  size  &  heterogeneity  =>  Facebook  infuentials  as  being  “CONNECTOR”  rather  than  “experts”  

STUDY 2 Network Level

Social structural influence on WOM �  WOM  widely  discussed  topic  in  online  environment  

�  Even  more  visible  in  SNS    �  Individual  aspect  widely  discussed  (e.g.  social  psychological  motive,  opinion  leadership)  

�  Few  studies  on  social  structural  aspect:  important  but  hard  to  measure    

Integrating models of social influence Social Information Processing

Model (Salancik & Pfeffer, 1978)

Social Contagion (Burt, 1987)

Network Diffusion (Valente, 1995)

Structural social

influence model of Facebook

Structural Social Influence Effect

1. Direct Contact(DC)

2. Interpersonal Contagion

3. Network Embeddedness (structural cohesion)

H1

H2

H3

Also  tested  interaction  effect  between  each  of  elements  (H4)  

Methods �  Used  the  whole  network  that  aggregates  72  ego  networks    

�  (N  =  3,971)  

Methods �  Network  Measures  (1) DC:    number  of  personal  recommendations  

an  individual  receives  (2) Contagion:  Personal  Network  Exposure  

(PNE)  (3) Embeddedness:  Krackhardt’s  simmelian-­‐

ties  (1998)  (the  total  frequency  of  a  person’s  being  co-­‐cliqued  with  others.)  

Descriptive �  882  invitees  joined  the  group  (22.2%)  �  Directly  contacted  by  a  single  inviter  (N=3060,  77.1%),  by  two  (N=648,  16.3%),  by  three  (N  =194,  4.9%),  by  four  (N=51,  1.35%),  by  five  (N  =  12),  and  more  than  six  inviter(N  =6)  

�  Network  exposure  to  one  group  member  (N=554,  14%),  two  (N=356,  9%),  three  (N=316,  8%),  four  (N=251,  6.3%),  five  (N=181,  4.6%),  and  more  than  five  (N=843,  21.1%)  (M  =  3.39)            As  a  proportion,  12%  social  contacts  are  group  members  on  average  (SD  =  .13).    

Descriptive �  Embeddedness:  12%  (N  =  556)  were  not  simmelian-­‐tied;  for  the  rest,  the  range  was  from  2  to  208  =>  log-­‐transformed  (M  =2.35,  SD  =1.3)  

   

Interaction Effects

Conclusions �  Three  mechanisms  of  structural  social  influence  on  FB  

�  DC  and  Contagion  effect  (Particularly  Contagion  effect  stronger).  

�  Interaction  effects  with  Embeddedness:          -­‐  DC  as  a  compensatory  influence  mode  for  those  less  integrated  with  others  

     -­‐  Contagion  intensified  when  an  influencee  is  integrated  within  a  network  

Discussions Altogether… �  WOM  is  a  multi-­‐level  influence  process  �  Not  merely  marketing  tactic;  a  fundamental  dynamic  to  explain  FB  and  other  Web  2.0  phenomena  

�  Network  level  of  assessment  is  valuable  in  Facebook  context  

�  Contributions:        (1)  structural  analysis  of  e-­‐WOM        (2)  behavioral  approach  to  CMC          (3)  interdisciplinary  collaboration  

Limitations… �   Simplified  field  study:  Needs  to  be  applied  to  more  complex  real  cases  on  SNS  (e.g.  sharing  drug  information,  fundraising  effect)  

�  Data  autocorrelations    �  Longitudinal  aspect  into  consideration  (PNE)    

�  Overcome  dichotomized  relational  aspect:  adopting  communication  history  archived  on  profile  wall    

�  Look  at  evolutionary  process  of  FB  group  (investigation  of  diffusion  process)    

Thank you~!