1.
Giving
is
Caring
Understanding
Dona1on
Behavior
through
Email
@
circa
Fall
2012
Yelena
Mejova,
Qatar
Compu1ng
Research
Ins1tute
Ingmar
Weber,
Qatar
Compu1ng
Research
Ins1tute
Venkata
Rama
Kiran
Garimella,
Aalto
University
Michael
C.
Dougal,
University
of
California,
Berkeley
@
Computer
Supported
Coopera1ve
Work
and
Social
Compu1ng
(CSCW14)
February
19,
2014

2.
Mo1va1on
Impact
real
life!
hTp://www.opensecrets.org/pres12/

3.
Mo1va1on
1. Can
we
detect
dona1ons
in
email?
2. Can
we
verify
sociological
theories
on
charitable
giving?

4.
What
could
affect
dona1on
behavior?
• Demographics
– “individual
capacity”:
educa1on
&
income1
– belonging
to
a
social
group2
• Interest
– seeing
as
more
needy
and
deserving3
• Solicita1ons
– influence
of
the
email
deluge4
• Social
network
– influence
/
homophily,
poli1cal
affilia1on5
• External
influence
– events
triggering
a
new
interest
or
awareness6

5.
1. Shier,
M.,
and
Handy,
F.
Understanding
online
donor
behavior:
the
role
of
donor
characteris1cs,
percep1ons
of
the
internet,
website
and
program,
and
influence
from
social
networks.
Interna'onal
Journal
of
Nonprofit
and
Voluntary
Sector
Marke'ng
(2012).
2. Tajfel,
H.,
and
Turner,
J.
An
integra1ve
theory
of
intergroup
conflict.
The
social
psychology
of
intergroup
rela'ons
33
(1979),
47.
3. Bekkers,
R.
Who
gives
what
and
when?
a
scenario
study
of
inten1ons
to
give
1me
and
money.
Social
Science
Research
39,
3
(2010),
369–381.
4. Chan,
M.
The
impact
of
email
on
collec1ve
ac1on:
a
field
applica1on
of
the
side
model.
New
Media
&
Society
12,
8
(2010),
1313–1330.
5. Bekkers,
R.,
and
Wiepking,
P.
A
literature
review
of
empirical
studies
of
philanthropy.
Nonprofit
and
Voluntary
Sector
Quarterly
40,
5
(2011),
924–973.
6. Olson,
M.
The
logic
of
collec've
ac'on:
Public
goods
and
the
theory
of
groups,
vol.
124.
Harvard
Univ
Pr,
1965.

6.
Data
• Collec1ng
“chari1es”
(total:
480)
– Scraping
Forbes
and
US
News
top
chari1es
lists
– Top
100
US
poli1cal
campaign
organiza1ons
– Chari1es
relevant
to
the
prominent
news
stories
in
the
1me
period
(Wikipedia
Current
Events)
hTp://www.forbes.com/lists/2011/14/200-­‐largest-­‐us-­‐chari1es-­‐11_rank.html
hTp://www.usnews.com/usnews/biztech/chari1es/lists/intl_deve-­‐lopment.htm
hTp://www.fec.gov/data/CommiTeeSummary.do?format=html&elec1on_yr=2012
hTp://en.wikipedia.org/wiki/Portal:Current_events
• Anonymized
Yahoo!
Mail
– user
agrees
to
research
– email
addresses
replaced
with
hashes
– fields:
from,
to,
1tle
• July
19
–
September
19,
2012

7.
Data
• Emails
from
charity:
matching
from
field
• Emails
thanking
for
dona1on:
manually
tuned
regex
(86%
assessed
precision)
– from:
info@barackobama.com
subject:
Thank
you
for
your
dona'on!
• Manually
categorized
chari1es
which
have
at
least
100
emails
in
dataset:
– Medical,
Humanitarian,
Poli1cs,
Environmental,
Chris1an/Religious,
Military,
Children,
Public
Broadcas1ng,
Animals,
Internet

8.
Data
• Donors
(≈100,000)
–
charity
thanked
them
for
a
dona1on
• Interested
(≈100,000)
–
got
email
from
a
charity
but
did
not
donate
• General
(≈10,000)
–
a
random
sample
of
the
rest
≈
1
billion
emails
total
• Is
the
email
treated
as
bulk
or
spam?
<10%
labeled
as
spam:
cancer.org,
lls.org,
redcross.org
>50%
labeled
as
bulk:
stjude,
dscc.org
>50%
labeled
as
spam:
wikimedia.org
• In
the
analysis,
we
pay
aTen1on
only
to
emails
which
reach
the
inbox

9.
Demographics

10.
Demographics
• Self-­‐reported
from
user
profiles
– age,
gender,
zip
code
• US
Census
to
es1mate
– %
of
Bachelor
degrees,
median
household
income
age
gender
(male=1)
%
bachelors
median
HHI

11.
Demographics
age
gender
(male=1)
In
agreement
with
the
US
Presiden1al
Elec1on
exit
polls:
– Younger
– Female
– Less
affluent
…
voters
favor
Obama
hTp://elec1ons.msnbc.msn.com/ns/poli1cs/
2012/all/president/#exitPoll
%
bachelors
median
HHI

12.
Interest

13.
Interest
• Classify
email
1tles
into
topical
categories
using
manually-­‐compiled
keyword
regexes
(avg
precision:
86.2%):
for
par1cular
topic
PBS
WGBH
incoming
for
par1cular
topic
outgoing

14.
Solicita1on

15.
Solicita1on
•
Does
increase
in
solicita1on
prompt
more
dona1ons?
1.
2.
3.
Compute
number
of
dona1ons
per
day
for
each
charity
Divide
into
three
terciles:
low,
medium,
high
Compute
Cohen’s
kappa
with
incoming
mail
from
charity
43%
of
organiza1ons
have
Cohen’s
kappa
>
0.3
That
is,
there
is
a
moderate
to
high
rela1onship
between
solicita1ons
and
dona1on

16.
Solicita1on
• Can
we
detect
this
on
a
personal
scale?
– Compute
probability
that,
given
the
user
will
donate
to
an
organiza1on,
that
he
or
she
donates
within
some
number
of
days
of
receiving
a
solicita1on
– Compare
to
a
uniform
baseline
On
average,
cumula1ve
probability
of
a
dona1on
a{er
a
solicita1on
is
higher
by
8%
na1onalmssociety.org
wycliffe.org
lls.org
opera1onsmile.org
intervarsity.org
feedthechildren.org
dscc.org
worldvision.org
tbn.org
wikimedia.org
marchofdimes.com
mdausa.org
ronpaul2012.com
greenpeace.org
irusa.org
Medical
Chris1an
Medical
Medical
Chris1an
Humanitarian
Poli1cs
Humanitarian
Chris1an
Internet
Medical
Medical
Poli1cal
Environmental
Humanitarian
48.99
37.98
33.82
29.41
18.78
18.26
15.39
13.88
13.82
12.99
11.82
11.45
8.92
7.89
7.5
increase
in
dona1on
probability

17.
Social
network

18.
Social
network
• Is
there
a
rela1onship
between
your
dona1ons
and
those
of
your
friends?
1.
2.
3.
4.
For
each
donor,
find
other
Yahoo
users
they
exchanged
emails
with
Normalize
number
of
friends
by
sampling
with
replacement
to
get
100
friends
Within
those
friends
we
can
find
other
donors
and
interested
users
Measure
strength
of
rela1onship
by
minimum
emails
sent
or
received
Donors
to
poli1cal
causes
have
almost
no
interac1on
with
others
who
donated
to
the
opposite
side
Spike
in
7th
bucket
(closest
friends)
is
greater
than
buckets
1-­‐6
at
p
<
0.01
each
bucket
contains
the
same
number
of
users

19.
External
Influence

20.
Dona(on
Volume
1.2
1
0.8
0.6
0.4
0.2
0
Paul
Ryan
announced
as
VP
candidate
9/19/1
9/17/1
9/15/1
9/13/1
9/11/1
9/19/1
9/17/1
9/15/1
9/13/1
9/11/1
9/9/12
9/7/12
9/5/12
9/3/12
9/1/12
8/30/1
8/28/1
8/26/1
8/24/1
8/22/1
8/20/1
8/18/1
8/16/1
8/14/1
8/12/1
8/10/1
8/8/12
8/6/12
8/4/12
8/2/12
Solicita1ons
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
Solicita1ons
0
1.2
1
0.8
0.6
0.4
0.2
0
Solicita(on
Volume
1.2
Solicita(on
Volume
T-­‐shirt
promo
“I
built
this”
9/9/12
9/7/12
9/5/12
9/3/12
9/1/12
8/30/1
8/28/1
8/26/1
8/24/1
8/22/1
“Photo
going
around
on
Facebook”
8/20/1
8/18/1
8/16/1
8/14/1
8/12/1
8/10/1
8/8/12
8/6/12
8/4/12
8/2/12
Dona1ons
7/31/1
7/29/1
7/27/1
7/25/1
Dona1ons
7/31/1
7/29/1
7/27/1
7/25/1
1
7/23/1
7/21/1
1.2
7/23/1
7/21/1
7/19/1
0
7/19/1
Dona(on
Volume
External
Influence
barackobama.com
Democra1c
Na1onal
Conven1on
Republican
Na1onal
Conven1on
miTromney.com

21.
Pu|ng
it
all
together
• Sample
data
to
mi1gate
influence
of
chari1es
with
more
representa1on
• Logis1c
regression
predic1ng
whether
user
donates
to
a
charity
For
each
friend
who
donates
to
a
charity,
the
likelihood
for
the
user
to
also
donate
to
that
charity
goes
up
by
24%
all
coefficients
are
significant
at
p
<
0.01
except
I4
and
S4

22.
Conclusions
• Want
to
run
a
campaign?
– Email
campaigns
can
be
effec1ve
–
solicita1on
works!
– Tailor
to
your
campaign
to
your
audience
– Leverage
social
network
– Don’t
get
stuck
in
spam/bulk
folders
• What’s
next?
– Social
influence
or
homophily?
– How
to
tailor
solicita1ons?
The
Language
that
Gets
People
to
Give:
Phrases
that
Predict
Success
on
Kickstarter
Tanushree
Mitra
|
Eric
Gilbert
[images
by
Wikimedia]