Private traits and attributes are predictable from digital records of human behaviorMichal Kosinski, David Stillwell, and Thore Graepel

Computational Social Media

Karolos Antoniadis

Presentation12th of March, 2020

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Private traits and attributes are predictable from digital records of human behavior

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Private traits and attributes are predictable from digital records of human behavior

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Private traits and attributes are predictable from digital records of human behavior

• openness• extraversion• age• sexual orientation• gender• ethnicity• etc.

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Private traits and attributes are predictable from digital records of human behavior

• openness• extraversion• age• sexual orientation• gender• ethnicity• etc.→

Problem

• Information about people might be predicted.

• For example, studies have shown that attributes can be predicted from browsing logs, used language.

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Problem

• Information about people might be predicted.

• For example, studies have shown that attributes can be predicted from browsing logs, used language.

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Question: Use basic digital records to automatically and accurately estimate personal attributes?

Contribution

With Facebook likes, we can accurately estimate a wide range of personal attributes (typically assumed private).

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Approach - Data

Objects: quotes, web sites, press articles, books, images, etc.

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Likes are shared with friends to express support, bookmarking, etc.

Approach - Data

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9 million unique objects liked by users.

A majority of the objects associated with very few users.

Discard likes with < 20 users and users with < 2 likes.

What remains?

Approach - Data

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55,814 Objects

58,4

66 U

sers

…

…

Approach - Data

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55,814 Objects

58,4

66 U

sers

…

…

user

object

Approach - Data

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55,814 Objects

58,4

66 U

sers

…

…

0 or 1user

object

Approach - Labels

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Personality traits with the International Personality Item Pool (IPIP).

Religion, political party, etc. from Facebook profile.

Ethnicity by looking at users’ pictures.

Two types: dichotomous and numeric.

Approach - Models

Reduce the dimensionality of the User-Like matrix with SVD.

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Use 100 components.

Build models that predict traits and attributes.

For numeric variables: linear regressionFor dichotomous variables: logistic regression

Approach - Overview

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Highest accuracy: gender & ethnicity

Lowest accuracy: divorced parents

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Results - Dichotomous Variables

Results - Numeric Variables

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Results - Predictive Likes

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Results - Predictive Likes

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Results

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Results - Power of Likes

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Even a single like resultsin nonnegligible accuracy.

Results - Overview

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Few users were associated with explicitly revealing Likes.

Less than 5% of gay users liked explicitly gay objects.

Results - Overview

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Likes can accurately predict individual traits and attributes.

Few users were associated with explicitly revealing Likes.

Less than 5% of gay users liked explicitly gay objects.

ConclusionPersonal attributes, ranging from sexual orientation to intelligence, can be automatically and accurately inferred using their Facebook’s likes.

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PROS CONS

• Improve products and services• Improve recommendations• New avenues in psychology

• Revealing without consent (danger)• What do we reveal?• Distrust in online services

ConclusionPersonal attributes, ranging from sexual orientation to intelligence, can be automatically and accurately inferred using their Facebook’s likes.

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PROS CONS

• Improve products and services• Improve recommendations• New avenues in psychology

• Revealing without consent (danger)• What do we reveal?• Distrust in online services

Thank y

ou!