Big data psychology

•

6 likes•1,971 views

Cogeco Peer 1

Technology Education

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 Cambridge University =
expertise in wide variety of disciplines

DIGITAL FOOTPRINT
Also:
• Email / texts
• Verbal conversations
• Physical movement data
• Credit card records
• …

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).

Only Facebook Likes?
(Other digital footprints are even more informative)

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 &
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

Target anonymous users using
psychological profiles

Extraverted &
open to experience

Well organized &
competitive

Cooperative &
happy

High IQ

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, Margaret L. Kern, Johannes C. Eichstaedt, Adam M. Croom, Lyle H. Ungar, Martin E. P. Seligman, Michal Kosinski,
David Stillwell

RISKS
•
•
•
•

Digital withdrawal
Fake footprints
Legal issues
Creepy targeting

Thank You!
mk583@cam.ac.uk
Michal Kosinski
Cambridge University

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Big data psychology

1. Big Data Psychology Michal Kosinski mk583@cam.ac.uk Image source: Facebook

2. Cambridge University Psychometrics Centre • 30 years of experience in psychological assessment • Strategic Network of Cambridge University = expertise in wide variety of disciplines

5. DIGITAL FOOTPRINT Also: • Email / texts • Verbal conversations • Physical movement data • Credit card records • …

7. 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

9. How accurate is digital profiling?

10. 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).

11. Prediction Accuracy: Numeric Variables

12. Only Facebook Likes? (Other digital footprints are even more informative)

13. Applications in marketing

14. Detailed psychological profiles of each and every consumer (both in online and offline environment)

15. Psychological profiles of groups (customers/fans/audience/readers/etc)

16. 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

17. 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

18. Target anonymous users using psychological profiles

19. Extraverted & open to experience Well organized & competitive Cooperative & happy High IQ

20. Build your own predictive models

21. Adjust your offer and language on the individual & group level

22. 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

23.

29. RISKS • • • • Digital withdrawal Fake footprints Legal issues Creepy targeting

30. Thank You! mk583@cam.ac.uk Michal Kosinski Cambridge University