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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	
  
Mo1va1on	
  

Impact	
  real	
  life!	
  

hTp://www.opensecrets.org/pres12/	
  
Mo1va1on	
  
1.  Can	
  we	
  detect	
  dona1ons	
  in	
  email?	
  
2.  Can	
  we	
  verify	
  sociological	
  theories	
  on	
  charitable	
  giving?	
  
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	
  
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.	
  	
  
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	
  
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	
  
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	
  
Demographics	
  
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	
  
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	
  
Interest	
  
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	
  
Solicita1on	
  
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	
  
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	
  
Social	
  network	
  
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	
  
External	
  Influence	
  
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	
  
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	
  
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]	
  

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Giving is Caring: Understanding Donation Behavior through Email

  • 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  
  • 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  
  • 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  
  • 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  
  • 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  
  • 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]