In his Brands-Only Summit Pre-Conference presentation, Boston College and MIT-Sloan Management Review's Gerald Kane presents research that measures social media as a complex adaptive system. He shares his findings based on a study of Wikipedia that relate to measuring ever-evolving social networks.

1. Measuring social media as
a complex, adaptive system
GERALD KANE
BOSTON COLLEGE AND MITSLOAN
MANAGEMENT REVIEW
OCTOBER 2729, 2014 ORLANDO SOCIALMEDIA.ORG/SUMMIT2014

2. 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

3. 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.

4. Data
is
key
differentiator
for
companies

5. Approaches
to
Measurement

6. …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.

7. If
we
don’t
account
for
these
…
…we
might
miss
something
important.

8. 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.

9. 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.

10. 12
10
Promotion Demotion
Percentage Change in Proportional Risk Average Experience (standardized)
8
6
4
2
0
-­‐2
-­‐1.5
-­‐1
-­‐0.5
0
0.5
1
1.5
2
2.5

11. Impact
of
Social
Business
on
companies.
(Kane
et
al.
2014,
MIT-­‐SMR)
100.00%
90.00%
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
1
2
3
4
5
6
7
8
9
10
1)
PosiGvely
impact
business
outcomes
2)
SB
IniGaGves
meet
objecGves

12. ALS
Ice
Bucket
Challenge

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

14. 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.

15. 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

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

17. 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

18. 20
18
16
14
12
10
8
6
4
2
0
Qtr2
Qtr3
Qtr4
Qtr1
Qtr2
Qtr3
Qtr4
Qtr1
Qtr2
Qtr3
Qtr4
Qtr1
Qtr2
Qtr3
Qtr4
Qtr1
Qtr2
Qtr3
Qtr4
Qtr1
Qtr2
Qtr3
Qtr4
Qtr1
Qtr2
Qtr3
Qtr4
2004
2005
2006
2007
2008
2009
2010
Number
of
Delibera.ons
Sum
of
ChaoGc
GeneraGng
Sum
of
Joint
Shaping
Sum
of
Defensive
Filtering

19. Duration
of
Contribution
for
Top
20
Contributors
(contributors
rank
ordered
by
activity
level)
!
!
E'
S'
M'
W'
R'
T'
B'
RN'
C'
N'
AC'
D'
CO'
TD'
AT'
CE'
L'
SO'
Z'
Q''
#!of!Edits!
2/13/2004' 5/13/2006' 8/10/2008' 11/8/2010'
Top$20$Par*cipants$
Dura=on'of'Par=cipa=on'
Highest
Activity
Moderate
Activity
!Promotion(#1(((Demotion(((Promotion(#2(
!

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

21. 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.

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

24. 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).

25. 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.”

26. Learn more about past and
upcoming events
SOCIALMEDIA.ORG/EVENTS
OCTOBER 2729, 2014 ORLANDO SOCIALMEDIA.ORG/SUMMIT2014