Big Data Psychology
Michal Kosinski mk583@cam.ac.uk

Image source: Facebook
Cambridge University
Psychometrics Centre
• 30 years of experience in psychological
assessment
• Strategic Network of Camb...
DIGITAL FOOTPRINT
Also:
• Email / texts
• Verbal conversations
• Physical movement data
• Credit card records
• …
Get some data
related to an
individual
(purchase history,
Facebook profile, tweets,
email, web browsing
history)

Translat...
How accurate is digital profiling?
Validation study:
1. 60.000 US Facebook users
2. Detailed psycho-demographic profiles
3. Tested the accuracy of predictive...
Prediction Accuracy: Numeric Variables
Only Facebook Likes?
(Other digital footprints are even more informative)
Applications in marketing
Detailed psychological profiles of each
and every consumer
(both in online and offline environment)
Psychological profiles of groups
(customers/fans/audience/readers/etc)
Openness

Conservative &
traditional

Liberal &
artistic

Conscientiousness

Spontaneous &
impulsive

Well organized &
har...
Openness

Conservative &
traditional

Liberal &
artistic

Conscientiousness

Spontaneous &
impulsive

Well organized &
har...
Target anonymous users using
psychological profiles
Extraverted &
open to experience

Well organized &
competitive

Cooperative &
happy

High IQ
Build your own predictive models
Adjust your offer and language on the
individual & group level
Agency and Communion on Facebook: An Open Vocabulary Analysis of Gender (submitted) Gregory Park, H. Andrew
Schwartz, Marg...
Language used
Language used
Language used
Language used
Language used
RISKS
•
•
•
•

Digital withdrawal
Fake footprints
Legal issues
Creepy targeting
Thank You!
mk583@cam.ac.uk
Michal Kosinski
Cambridge University
Big data psychology
Big data psychology
Big data psychology
Big data psychology
Big data psychology
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Big data psychology

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Big data psychology

  1. 1. Big Data Psychology Michal Kosinski mk583@cam.ac.uk Image source: Facebook
  2. 2. Cambridge University Psychometrics Centre • 30 years of experience in psychological assessment • Strategic Network of Cambridge University = expertise in wide variety of disciplines
  3. 3. DIGITAL FOOTPRINT Also: • Email / texts • Verbal conversations • Physical movement data • Credit card records • …
  4. 4. Get some data related to an individual (purchase history, Facebook profile, tweets, email, web browsing history) Translate it to Facebook Graph ID (Oreo = 114998944652) Send it to ApplyMagicSauce API Get a detailed psychodemographic profile
  5. 5. How accurate is digital profiling?
  6. 6. Validation study: 1. 60.000 US Facebook users 2. Detailed psycho-demographic profiles 3. Tested the accuracy of predictive models Kosinski, M., Stillwell, D.J., Graepel, T. (2013) Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences (PNAS).
  7. 7. Prediction Accuracy: Numeric Variables
  8. 8. Only Facebook Likes? (Other digital footprints are even more informative)
  9. 9. Applications in marketing
  10. 10. Detailed psychological profiles of each and every consumer (both in online and offline environment)
  11. 11. Psychological profiles of groups (customers/fans/audience/readers/etc)
  12. 12. Openness Conservative & traditional Liberal & artistic Conscientiousness Spontaneous & impulsive Well organized & hard working Extraversion Contemplative & happy with own company Agreeableness Competitive & Working alone Team working & trusting Neuroticism Competitive & Working alone Laid back & relaxed Engaged with outside world
  13. 13. Openness Conservative & traditional Liberal & artistic Conscientiousness Spontaneous & impulsive Well organized & hard working Extraversion Contemplative & happy with own company Agreeableness Competitive & Working alone Team working & trusting Neuroticism Competitive & Working alone Laid back & relaxed Intelligence Low Engaged with outside world High
  14. 14. Target anonymous users using psychological profiles
  15. 15. Extraverted & open to experience Well organized & competitive Cooperative & happy High IQ
  16. 16. Build your own predictive models
  17. 17. Adjust your offer and language on the individual & group level
  18. 18. Agency and Communion on Facebook: An Open Vocabulary Analysis of Gender (submitted) Gregory Park, H. Andrew Schwartz, Margaret L. Kern, Johannes C. Eichstaedt, Adam M. Croom, Lyle H. Ungar, Martin E. P. Seligman, Michal Kosinski, David Stillwell
  19. 19. Language used
  20. 20. Language used
  21. 21. Language used
  22. 22. Language used
  23. 23. Language used
  24. 24. RISKS • • • • Digital withdrawal Fake footprints Legal issues Creepy targeting
  25. 25. Thank You! mk583@cam.ac.uk Michal Kosinski Cambridge University
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