Personality Profiling: How informative are social media profiles in predicting personal information?

TL;DR

This study evaluates the effectiveness of various machine learning models in predicting Myers-Briggs personality types from social media data, finding modest accuracy but identifying key informative features and addressing class imbalance issues.

Contribution

It compares four models for personality prediction from social media and introduces a statistical framework for feature importance and class imbalance correction.

Findings

SVM achieves 20.95% accuracy in predicting complete personality types

Logistic regression performs nearly as well but is faster

Certain features are significantly more informative for specific personality dimensions

Abstract

Personality profiling has been utilised by companies for targeted advertising, political campaigns and public health campaigns. However, the accuracy and versatility of such models remains relatively unknown. Here we explore the extent to which peoples' online digital footprints can be used to profile their Myers-Briggs personality type. We analyse and compare four models: logistic regression, naive Bayes, support vector machines (SVMs) and random forests. We discover that a SVM model achieves the best accuracy of 20.95% for predicting a complete personality type. However, logistic regression models perform only marginally worse and are significantly faster to train and perform predictions. Moreover, we develop a statistical framework for assessing the importance of different sets of features in our models. We discover some features to be more informative than others in the Intuitive/Sensory (p = 0.032) and Thinking/Feeling (p = 0.019) models. Many labelled datasets present substantial class imbalances of personal characteristics on social media, including our own. We therefore highlight the need for attentive consideration when reporting model performance on such datasets and compare a number of methods to fix class-imbalance problems.