Giving  is  Caring  Understanding  Dona1on  Behavior  through  Email  

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  

circa  Fall  2012  @  

@  Computer  Supported  Coopera1ve  Work  and  Social  Compu1ng  (CSCW14)  February  19,  2014  

Mo1va1on  

hTp://www.opensecrets.org/pres12/  

Impact  real  life!  

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

Mo1va1on  

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)  

•  Anonymized  Yahoo!  Mail  –  user  agrees  to  research  –  email  addresses  replaced  with  hashes  –  fields:  from,  to,  1tle  

•  July  19  –  September  19,  2012  

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  

•  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  

Data  

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  

In  agreement  with  the  US  Presiden1al  Elec1on  exit  polls:  

–  Younger  –  Female  –  Less  affluent  …  voters  favor  Obama  

age   gender  (male=1)  

%  bachelors   median  HHI  

Demographics  

hTp://elec1ons.msnbc.msn.com/ns/poli1cs/2012/all/president/#exitPoll  

Interest  

Interest  •  Classify  email  1tles  into  topical  categories  using  manually-­‐compiled  

keyword  regexes  (avg  precision:  86.2%):  

incoming   outgoing  

for  par1cular  topic   for  par1cular  topic  PBS  WGBH  

Solicita1on  

Solicita1on  •  Does  increase  in  solicita1on  prompt  more  dona1ons?  

1.  Compute  number  of  dona1ons  per  day  for  each  charity  2.  Divide  into  three  terciles:  low,  medium,  high  3.  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   Medical   48.99  wycliffe.org   Chris1an   37.98  lls.org   Medical   33.82  opera1onsmile.org   Medical   29.41  intervarsity.org   Chris1an   18.78  feedthechildren.org   Humanitarian   18.26  dscc.org   Poli1cs   15.39  worldvision.org   Humanitarian   13.88  tbn.org   Chris1an   13.82  wikimedia.org   Internet   12.99  marchofdimes.com   Medical   11.82  mdausa.org   Medical   11.45  ronpaul2012.com   Poli1cal   8.92  greenpeace.org   Environmental   7.89  irusa.org   Humanitarian   7.5  

increase  in  dona1on  probability  

Social  network  

Social  network  •  Is  there  a  rela1onship  between  your  dona1ons  and  those  of  your  friends?  

1.  For  each  donor,  find  other  Yahoo  users  they  exchanged  emails  with  2.  Normalize  number  of  friends  by  sampling  with  replacement  to  get  100  

friends  3.  Within  those  friends  we  can  find  other  donors  and  interested  users  4.  Measure  strength  of  rela1onship  by  minimum  emails  sent  or  received  

Spike  in  7th  bucket  (closest  friends)  is  greater  than  buckets  1-­‐6  at  p  <  0.01  

Donors  to  poli1cal  causes  have  almost  no  interac1on  with  others  who  donated  to  the  opposite  side  

each  bucket  contains  the  same  number  of  users  

External  Influence  

External  Influence  

0  0.2  0.4  0.6  0.8  1  1.2  

0  0.2  0.4  0.6  0.8  1  

1.2  

7/19/1

7/21/1

7/23/1

7/25/1

7/27/1

7/29/1

7/31/1

8/2/12  

8/4/12  

8/6/12  

8/8/12  

8/10/1

8/12/1

8/14/1

8/16/1

8/18/1

8/20/1

8/22/1

8/24/1

8/26/1

8/28/1

8/30/1

9/1/12  

9/3/12  

9/5/12  

9/7/12  

9/9/12  

9/11/1

9/13/1

9/15/1

9/17/1

9/19/1

Solicita

(on  Vo

lume  

Dona

(on  Vo

lume  

Dona1ons   Solicita1ons  

0  

0.2  

0.4  

0.6  

0.8  

1  

1.2  

0  

0.2  

0.4  

0.6  

0.8  

1  

1.2  

7/19/1

7/21/1

7/23/1

7/25/1

7/27/1

7/29/1

7/31/1

8/2/12  

8/4/12  

8/6/12  

8/8/12  

8/10/1

8/12/1

8/14/1

8/16/1

8/18/1

8/20/1

8/22/1

8/24/1

8/26/1

8/28/1

8/30/1

9/1/12  

9/3/12  

9/5/12  

9/7/12  

9/9/12  

9/11/1

9/13/1

9/15/1

9/17/1

9/19/1

Solicita

(on  Vo

lume  

Dona

(on  Vo

lume  

Dona1ons   Solicita1ons  

barackobama.com  

miTromney.com  

“Photo  going  around  on  Facebook”  

Paul  Ryan  announced  as  VP  candidate  

T-­‐shirt  promo  “I  built  this”  

Republican  Na1onal  Conven1on  Democra1c  Na1onal  Conven1on  

Pu|ng  it  all  together  •  Sample  data  to  mi1gate  influence  of  chari1es  with  more  

representa1on    •  Logis1c  regression  predic1ng  whether  user  donates  to  a  charity  

all  coefficients  are  significant  at  p  <  0.01  except  I4  and  S4  For  each  friend  who  donates  to  a  charity,  the  likelihood  for  

the  user  to  also  donate  to  that  charity  goes  up  by  24%  

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]