Items from Psychometric Tests as Training Data for Personality Profiling Models of Twitter Users

TL;DR

This paper investigates using psychometric test items directly as training data for personality profiling models on Twitter, demonstrating that fine-tuned BERT classifiers can achieve performance comparable to in-domain data, especially with data augmentation.

Contribution

It introduces a novel approach of using psychometric test items as training data for personality profiling, addressing data scarcity and bias issues in social media analysis.

Findings

Comparable performance to in-domain training for 4/5 traits

T5-based data augmentation improves results

Psychometric test items are a viable resource for personality modeling

Abstract

Machine-learned models for author profiling in social media often rely on data acquired via self-reporting-based psychometric tests (questionnaires) filled out by social media users. This is an expensive but accurate data collection strategy. Another, less costly alternative, which leads to potentially more noisy and biased data, is to rely on labels inferred from publicly available information in the profiles of the users, for instance self-reported diagnoses or test results. In this paper, we explore a third strategy, namely to directly use a corpus of items from validated psychometric tests as training data. Items from psychometric tests often consist of sentences from an I-perspective (e.g., "I make friends easily."). Such corpora of test items constitute 'small data', but their availability for many concepts is a rich resource. We investigate this approach for personality profiling, and evaluate BERT classifiers fine-tuned on such psychometric test items for the big five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and analyze various augmentation strategies regarding their potential to address the challenges coming with such a small corpus. Our evaluation on a publicly available Twitter corpus shows a comparable performance to in-domain training for 4/5 personality traits with T5-based data augmentation.