This document summarizes a study on word-of-mouth (WOM) and social influence on Facebook. The study examined WOM at both the individual and network levels. At the individual level, it looked at identifying influential users and their characteristics. It found that an observation method of influence was more valid than self-designation. At the network level, it developed a structural social influence model of Facebook WOM incorporating direct contact, interpersonal contagion, and network embeddedness. The model was tested on a network of nearly 4,000 users to understand how social network factors influence WOM.

1. A Facebook Word-of-Mouth for the
Emergence of Collective Network: A
Cyber-field Study
- Who are the Influentials?
- Structural Social Influence Model of Facebook
WOM
Kyounghee “Hazel” Kwon (PhD)

2. 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

3. Facebook Social Information

4. 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

5. 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

6. — A
cyber-­‐behavioral
field
study:
Mobilizing
a
campus
advocacy
network
on
FB
through
WOM

8. — 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.

9. STUDY 1
Individual Level: Personal Influence

10. — 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

11. — 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?

12. H1
1. Personality Trait
2. GregariousnessH2
H3
3. Social Activities
H4
4. Cosmopoliteness

13. 1. Personality Trait
• Weimann’s ‘Personality Strength Index’ (PS index)
2. Gregariousness
• Facebook interaction, updating others’ profiles &
popularity,
3. Social Activities
• Membership in FB group (general and topic specific),
contact range
4. Cosmopoliteness
• Network heterogeniety (more in next slide)

14. — 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)

15. 1. Density
(D):
the
extent
to
which
friends
are
known
to
one
another
within
OL’s
ego
network

16. 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.)
2.
i
I’s neighboring nodes=5,
subsequent edges=2
Ci = 0.2

17. 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

18. Personality
Gregarious
Social Activity

19. Personality
Gregarious
Social Activity
Bad model fit!

21. — 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”

22. STUDY 2
Network Level

23. 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

24. 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

25. Structural Social Influence Effect
1. Direct Contact(DC)
2. Interpersonal Contagion
H1
H2
3. Network Embeddedness (structural cohesion)
H3
Also
tested
interaction
effect
between
each
of
elements
(H4)

26. Methods
— Used
the
whole
network
that
aggregates
72
ego
networks
— (N
=
3,971)

27. 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.)

28. 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).

29. 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)

31. Interaction Effects

32. 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

33. 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

34. 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)

35. Thank you~!