Computational Discovery of Personality from Social Media
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The document discusses a system called System U that uses computational methods to discover personality traits, values, needs, and emotional styles of individuals from their social media posts. It aims to provide personalized experiences at scale. The system analyzes text using psycholinguistic analytics and models to predict traits according to frameworks like the Big 5 personality traits. It was validated through studies showing its predictions correlated well with standard personality surveys for most people. Further field studies on Twitter showed traits could help predict who would respond to recommendations or help others.
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Computational Discovery of Personality from Social Media
- 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 Outline • Mo*va*on • System U Overview and Live Demo • Methodology • Valida*ons • Summary
- 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 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 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 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 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 System U >>>>>>
- 9. 9 My Psychological Portrait from my Facebook
- 10. 10 My Psychological Portrait from my Twicer
- 11. 11 Methodology
- 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. 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. Discovering Values [Schwartz 2006] • Values capture personal beliefs and mo*vators • Values guide ac*ons
- 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 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 Deriving Psycholinguis*c Evidence Machine Learning Psycholinguis*c Lexicons Ideal … Goal 0.23 Special 0.35 … Half -‐0.26 [Yang & Li, 2013]
- 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 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 Valida*ons
- 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. 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. 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. 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. Study 1: Who Will Respond to Ads
- 26. Study 1: Who Will Respond to Ads Social message Fine Lifestyle message Fun message
- 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. 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. 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. 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. 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. 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 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 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 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 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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