Towards Automatic Personality Prediction Using Facebook Like Categories

TL;DR

This paper demonstrates that Facebook Likes can be used to automatically predict personal traits such as ethnicity, political views, and personality, achieving high accuracy with a novel mapping and machine learning approach.

Contribution

It introduces a new method that maps Facebook Likes to categories and uses machine learning to predict diverse personal traits from digital behavior data.

Findings

Achieves 83% accuracy in distinguishing religious individuals

87% accuracy in identifying Asian vs. European ethnicity

81% accuracy in classifying emotional stability

Abstract

We demonstrate that effortlessly accessible digital records of behavior such as Facebook Likes can be obtained and utilized to automatically distinguish a wide range of highly delicate personal traits including: life satisfaction, cultural ethnicity, political views, age, gender and personality traits. The analysis presented based on a dataset of over 738,000 users who conferred their Facebook Likes, social network activities, egocentric network, demographic characteristics, and the results of various psychometric tests for our extended personality analysis. The proposed model uses unique mapping technique between each Facebook Like object to the corresponding Facebook page category/sub-category object, which is then evaluated as features for a set of machine learning algorithms to predict individual psycho-demographic profiles from Likes. The model , distinguishes between a religious and non-religious individual in 83% of circumstances, Asian and European in 87% of situations, and between emotional stable and emotion unstable in 81% of situations. We provide exemplars of correlations between attributes and Likes and present suggestions for future directions.