Measuring the Language of Self-Disclosure across Corpora

Abstract

Being able to reliably estimate self-disclosure - a key component of friendship and intimacy - from language is important for many psychology studies. We build single-task models on five self-disclosure corpora, but find that these models generalize poorly; the within-domain accuracy of predicted message-level self-disclosure of the best-performing single-task model (mean Pearson's r=0.69) is much higher than the respective across data set accuracy (mean Pearson's r=0.32), due to both variations in the corpora (e.g., medical vs. general topics) and labelling instructions (target variables: self-disclosure, emotional disclosure, intimacy). However, some lexical features, such as expression of negative emotions and use of first person personal pronouns such as'I' reliably predict self-disclosure across corpora. We develop a multi-task model that improves results, with an average Pearson's r of 0.37 for out-of-corpora prediction.