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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