System U: Computational Discovery of Personality Traits from Social Media for Individualized Experience

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Hundreds of millions of people leave digital footprints in public (e.g., social media/social networking sites and review sites). We are developing System U, which uses psycholinguistic analytics to automatically derive one's personality traits from their digital footprints. Such traits uniquely characterize an individual's psychological, cognitive, and affective style and properties, and can then be used to make hyper-personalized recommendations to individual to influence/intervene the actions of the individual. In this talk, I will give an overview of System U and describe how it automatically derives several types of personality traits from one’s tweets, including human basic value (one's belief + motives) and fundamental needs (e.g., ideals vs. practical). Moreover, I will present a set of validation studies that assess how accurate the System U-derived traits are compared to “ground truth” and how these derived traits actually influence recommendations and people’s behavior in the real world. I will also use live demos and concrete examples, ranging from precision marketing to individualized customer care, to demonstrate the applications of System U and discuss interesting research directions.

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System U: Computational Discovery of Personality Traits from Social Media for Individualized Experience

  1. 1. 1   System   Computa*onal  Discovery  of  Personality  Traits   from  Social  Media  for  Individualized  Experience           Michelle  Zhou   IBM  Research,  Almaden   mzhou@us.ibm.com  
  2. 2. 2   Outline   •  Mo*va*on   •  System  U  Overview  and  Live  Demo   •  Methodology   •  Valida*ons   •  Summary  
  3. 3. 3   “The  perfect  solu.on  is  to  serve   each  consumer  individually.   The  problem?  There  are  7   billion  of  them.”       Consumer  products  CMO,  Singapore   IBM  2011  CMO  Study  
  4. 4. 4   Model  personality  traits   dis*nguishing  individuals     [Ford’  05,  O’Brien  ’96,  Neuman  ’99,   Gosling  ’03,  Wholan’06]           Derive  personality  traits   for  hundreds  of  millions   of  individuals   Individualiza*on   at  Scale  
  5. 5. 5   Lengthy  standard   psychometric  tests           Reliability  and  freshness  of   test  results   Challenges   “Welcome  to  our  store,  would  you   like  to  take  a  personality  test?”    
  6. 6. 6   A  Silver  Lining   Psycholinguis*c  studies:  personality  from  text   [Tausczik  and  Pennybaker‘10,  Yarkoni  ‘10]             Hundreds  of  millions  of  people  leave  text  footprints  on   social  media   “I love food, .., with … together we … in… very…happy.” Word category: Inclusive Agreeableness
  7. 7. 7   System  U  in  a  Nutshell   Big  5   Values   Needs   Emo4on Style   A7tude   Psycholinguis*c   Analy*cs   InkWell   VisWell   Engagement   Recommenda*on   Personality   Portrait   Social  Media  
  8. 8. 8   System  U  >>>>>>  
  9. 9. 9   My   Psychological   Portrait  from   my  Facebook  
  10. 10. 10   My   Psychological   Portrait  from   my  Twicer  
  11. 11. 11   Methodology  
  12. 12. 12   Discovering  Big  5  Personality  Traits   •  Psychological  characteris*cs   reflec*ng  individual   differences   •  Consistent  and  enduring   •  Can  change   •  Link  to  many  aspects  of  one’s   life   –  Problem/emo*on  coping   –  Rela*onship  selec*on   –  Occupa*onal  proficiency   –  Team  performance   –  .  .  .   outgoing/energe*c   vs.  solitary/reserved   efficient/organized  vs.   easy-­‐going/careless   [O’Brien  ’96,  Neuman  ’99,  Gosling  ’03,  Wholan’06]  
  13. 13. Discovering  Fundamental  Needs   [Ford,  2005]   •  Fundamental  needs  are   universal  [Aaker  1995,   Maslow  1943]   •  Oken  change  with  life  events   •  Link  to  many  aspects  of  one’s   life   •  Brand/product  choices   •  Occupa*onal  choices   •  .  .  .    
  14. 14. Discovering  Values   [Schwartz  2006]   •  Values  capture  personal  beliefs  and  mo*vators   •  Values  guide  ac*ons  
  15. 15. 15   Our  Methodology   1.  Large-­‐scale  psychometric  studies   2.  Deriva*on  of  psycholinguis*c   evidence  (lexicons)   3.  Online  predic*on  of  personality   traits  from  text  
  16. 16. 16   Large-­‐Scale  Psychometric  Studies   •  Designing  item-­‐based   psychometric  studies   •  Collec*ng  psychometric   scores  &  text  footprints   on  Amazon  Mechanical   Turk   I  tend  to  pursue  perfec*on  
  17. 17. 17   Deriving  Psycholinguis*c  Evidence   Machine  Learning   Psycholinguis*c   Lexicons   Ideal   …   Goal   0.23   Special   0.35   …   Half   -­‐0.26   [Yang  &  Li,  2013]  
  18. 18. 18   Online  Predic*on  of  Personality   Traits  from  Text   Predica*ve   Models   Personality  Traits   Social  Media  Posts   Big  5   Values   Needs   Emo*onal  Style   Aptude   …     “…  great  to  have  a  chauffer  who  can  help  us  accomplish  our  goals  …”   Chauffeur   Accomplish   Goal   Special   License   …   Ideal   0.37   0.94   0.23   0.35   0.13   …   1   1   1   0   0   …  
  19. 19. 19   Online  Predic*on  of  Personality   Traits  from  Text   Addi*onal  processing   –  Normalize  counts  with  total  words   –  Linear  combina*on  of  counts  with  learned  derived  co-­‐ efficient  to  compute  trait  scores   –  Normalize  trait  scores  to  give  percen*le  scores   “…  great  to  have  a  chauffer  who  can  help  us  accomplish  our  goals  …”   Chauffeur   Accomplish   Goal   Special   License   …   Ideal   0.37   0.94   0.23   0.35   0.13   …   1   1   1   0   0   …  
  20. 20. 20   Valida*ons  
  21. 21. How  good  are  our  results  compared  to   standard  psychometric  studies?   How  well  can  our  results  be  used  to  predict   or  influence  one’s  behavior?  
  22. 22. System  U  vs.  Standard  Surveys   •  Par*cipants   –  Invited  1325  Twicer  users  at  IBM,  650  responded,   and  256  completed   •  Method   –  Par*cipants  took  three  sets  of  psychometric  tests   •  50-­‐item  Big  5  (IPIP),  26-­‐item  basic  values  (Schwartz),  and     52-­‐item  fundamental  needs  (our  own)   –  Par*cipants  rated  how  well  each  type  of  the   derived  trait  matches  with  their  percep*on  of   themselves  
  23. 23. Results   •  RV-­‐Coefficient  correla*on  analysis  of  each  type  of  trait   •  Over  80%  of  popula*on,  their  correla*on  is  sta.s.cally   significant  (80.8%,  98.21%,  and  86.6%  for  Big  5  personality,   basic  values  and  needs)   [Gou  et  al.  CHI  2014]  
  24. 24. Field  Studies  on  Twicer   Who  are  more  likely  to  behave  as  asked   and  how?     – Respond  to  recommended  services   (“ads”)   – Answer  strangers’  ques*ons   – Help  strangers  spread  informa*on  (e.g.,   SOS)  
  25. 25. Study  1:  Who  Will  Respond  to  Ads  
  26. 26. Study  1:  Who  Will  Respond  to  Ads   Social  message   Fine  Lifestyle  message   Fun  message  
  27. 27. Study  1:  Who  Will  Respond  to  Ads   Method   – Iden*fied  7290  Twicer  users  who  twicer  about   traveling  to  NYC  in  the  near  future   – Computed  personality  traits  for  each  iden*fied   user   – Sent  one  of  the  three  messages  via  Twicer  to   each  person  
  28. 28. Study  1:  Who  Will  Respond  to  Ads   Results   •  Rela*onships  between  traits  and  responses   –  Avg  response  rates  for  some  top-­‐matched  are  impressive  (e.g.,  top  25%   Extrovert  for  social  msg  CTR  8.65,  following  9.12,  and  RFR  5.66)   •  Certain  personality  traits  resulted  in  significantly  higher   successful  responses   –  A  combina*on  of  high  openness  and  low  neuro*cism  presented  31%  and  45%   increase  in  clicking  and  following  rates      
  29. 29. Study  2:  Who  Will  Answer   Ques*ons   [Mahmud  et  al.,  IUI  2013]   Method   –  Model  a  person’s   ability,  willingness,   and  readiness  to   answer  ques*ons   –  Predict  one’s   likelihood  to  respond   –  Op*miza*on-­‐based   approach  to  answerer   selec*on  
  30. 30. Study  2:  Who  Will  Answer   Ques*ons   [Mahmud  et  al.,  IUI  2013]   Experiment  Results   –  Iden*fied  500  Twicer  users  each  for  two  domains   –  Sent  requests  to  100  random  users,  used  our  work  to  select   100  among  the  remaining  400  users     –  Compared  random,  baseline,  and  ours   TSA-­‐tracker-­‐1   TSA-­‐tracker-­‐2   Product   Baseline   42%   33%   31%   Live  Experiment Random  Selec4on Our  Algorithm TSA-­‐Tracker-­‐1 29% 66% Product 26% 60%
  31. 31. Study  2:  Who  Will  Spread   Informa*on  and  When   Method   –  Modeled  core  features  of  an  “informa*on  spreader”   •  Willingness,  readiness,  ac*vity  *me  pacern   –  Predicted  the  likelihood  to  respond  and  *me-­‐to-­‐act   [Lee  et  al.,  IUI  2014]  
  32. 32. Study  2:  Who  Will  Spread   Informa*on  and  When  [Lee  et  al.,  IUI  2014]   Experiment  Results   –  Randomly  selected  426  candidates  who  had  recently   tweeted  about  “bird  flu”  in  July  2013   –  Each  approach  selected  top  100  candidates         Approach   Retwee4ng   Rate   Random  People  Contact   4%   Popular  People  Contact   9%   Our  Approach   19%   Approach   Retwee4ng   Rate   Random  People  Contact   4%   Popular  People  Contact   8.7%   Our  Predic*on  Approach   18%   Our  Approach  +  Wait  *me   model   18.5%  
  33. 33. 33   Key   Applica*ons   Marke*ng   Determine  who,  what,  how,  and   when  to  target     Customer  Care   Agent-­‐Customer  match  making   Real-­‐*me  agent  assistant     Smarter  Workforce   Recruitment   Talent  iden*fica*on  and   development     Risk  iden*fica*on  and  mi*ga*on      
  34. 34. 34   Summary   •  Psycholinguis*c  analysis  derives  deep   understanding  of  individuals  at  scale   •  Derived  personality  traits  can  be  used  to   predict  and  influence  individuals’  behavior  in   the  real  world   •  Far-­‐reaching  implica*ons  on  crea*ng  hyper-­‐ personalized  social  recommender  systems    
  35. 35. 35   Acknowledgement   •  Jilin  Chen   •  Eben  Habor   •  Liang  Gou   •  Jalal  Mahmud   •  Nimrod  Megiddo   •  Jeff  Nichols   •  Aditya  Pal   •  Jerre  Schoudt   •  Barton  Smith   •  Ying  Xuan   •  Huahai  Yang   •  Hernan  Badenes   •  Mateo  Nicolas  Bengualid   •  Richard  Gabriel   •  Huiji  Gao   •  Chris  Kau   •  Mengdie  Hu   •  Kyumin  Lee   •  Tara  Machews   •  Ruogu  Yang   •  Tom  Zimmerman  
  36. 36. 36   References   •  Chen,  J.,  Hsieh,  G.,  Mahmud,  J.,  and  Nichols,  J.  Understanding  individuals  personal  values  from   social  media  word  use.  In  ACM  Proc.  CSCW  ’2014.     •  Ford,  J.  K.  Brands  Laid  Bare.  John  Wiley  &  Sons,  2005.     •  Gou,  L.,  Zhou,  M.X.,  and  Yang,  H.  KnowMe  and  ShareMe:  Understanding  automa*cally  discovered   personality  traits  from  social  media  and  user  sharing  preferences.  In  ACM  Proc.  CHI  2014.   •  Lee,  K.,  Mahmud,  J.,  Chen,  J.,  Zhou,  M.X.,  and  Nichols,  J.  Who  will  retweet  this?  Automa*cally   iden*fying  and  engaging  strangers  on  Twicer  to  spread  informa*on.  In  ACM  Proc.  IUI  ‘2014.   •  Luo,  L.,  Wang,  F.,  Zhou,  M.X.,  Pan,  X.,  and  Chen,  H.  Who’s  got  answers?  Growing  the  pool  of   answerers  in  a  smart  enterprise  Social  Q&A  system.  In  ACM  Proc.  IUI  ‘2014.         •  Mahmud,  J.,  Zhou,  M.X.,  Megiddo,  N.,  Nichols,  J.,  and  Drews,  C.  Recommending  Targeted  Strangers   from  Whom  to  Solicit  Informa*on  in  Twicer.  In  ACM  Proc.  IUI  ‘2013.     •  Schwartz,  S.  H.  Basic  human  values:  Theory,  measurement,  and  applica.ons.  Revue  francaise  de   sociologie,  2006.     •  Tausczik,  Y.  R.,  and  Pennebaker,  J.  W.  The  psychological  meaning  of  words:  LIWC  and  computerized   text  analysis  methods.  Journal  of  Language  and  Social  Psychology  29,  1  (2010),  24–54.   •  Yang,  H.,  and  Li,  Y.  Iden*fying  user  needs  from  social  media.  IBM  Tech.  Report  (2013).   •  Yarkoni,  T.  Personality  in  100,000  words:  A  large-­‐scale  analysis  of  personality  and  word  use  among   bloggers.  J.  research  in  personality  44,  3  (2010),  363–373.    
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