  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.
