Matching Theory and Data with Personal-ITY: What a Corpus of Italian YouTube Comments Reveals About Personality
This work addresses personality detection for Italian language users, but it is incremental as it applies existing methods to new data.
The authors tackled personality detection in non-English languages by creating Personal-ITY, a corpus of Italian YouTube comments labeled with MBTI traits, and found that no single model is best for detection, with some traits easier to predict than others.
As a contribution to personality detection in languages other than English, we rely on distant supervision to create Personal-ITY, a novel corpus of YouTube comments in Italian, where authors are labelled with personality traits. The traits are derived from one of the mainstream personality theories in psychology research, named MBTI. Using personality prediction experiments, we (i) study the task of personality prediction in itself on our corpus as well as on TwiSty, a Twitter dataset also annotated with MBTI labels; (ii) carry out an extensive, in-depth analysis of the features used by the classifier, and view them specifically under the light of the original theory that we used to create the corpus in the first place. We observe that no single model is best at personality detection, and that while some traits are easier than others to detect, and also to match back to theory, for other, less frequent traits the picture is much more blurred.