Personal-ITY: A Novel YouTube-based Corpus for Personality Prediction in Italian
Authors: Elisa Bassignana, Malvina Nissim, Viviana Patti (Università di Torino, University of Groningen, 2020)
Topic: Textual Genres & Literature Linguistics

Abstract
We present a novel corpus for personality prediction in Italian, containing a larger number of authors and a different genre compared to previously available resources. The corpus is built exploiting Distant Supervision, assigning Myers-Briggs Type Indicator (MBTI) labels to YouTube comments, and can lend itself to a variety of experiments. We report on preliminary experiments on Personal-ITY, which can serve as a baseline for future work, showing that some types are easier to predict than others, and discussing the perks of cross-dataset prediction.

Introduction
When faced with the same situation, different humans behave differently. This is, of course, due to different backgrounds, education paths, and life experiences, but according to psychologists there is another important aspect: personality AUTHOR. Human Personality is a psychological construct aimed at explaining the wide variety of human behaviours in terms of a few, stable and measurable individual characteristics AUTHOR. Such characteristics are formalised in Trait Models, and there are currently two of these models that are widely adopted: Big Five AUTHOR and Myers-Briggs Type Indicator (MBTI) AUTHOR. The first examines five dimensions (Openness to experience, Conscientiousness, Extroversion, Agreeableness and Neuroticism) and for each of them assigns a score in a range. The second one, instead, considers 16 fixed personality types, coming from the combination of the opposite poles of 4 main dimensions (Extravert-Introvert, iNtuitive-Sensing, Feeling-Thinking, Perceiving-Judging). Examples of full personality types are therefore four letter labels such as ENTJ or ISFP. The tests used to detect prevalence of traits include human judgements regarding semantic similarity and relations between adjectives that people use to describe themselves and others. This is because language is believed to be a prime carrier of personality traits AUTHOR. This aspect, together with the progressive increase of available user-generated data on social media, has prompted the task of Personality Detection, i.e., the automatic prediction of personality from written texts AUTHOR. Personality detection can be useful in predicting life outcomes such as substance use, political attitudes and physical health. Other fields of application are marketing, politics and psychological and social assessment. As a contribution to personality detection in Italian, we present Personal-ITY, a new corpus of YouTube comments annotated with MBTI personality traits, and some preliminary experiments to highlight its characteristics and test its potential. The corpus is made available to the community.

Conclusion
The experiments show that there is no single best model for personality prediction, as the feature contribution depends on the dimension considered, and on the dataset. Lexical features perform best, but they tend to be strictly related to the context in which the model is trained and so to overfit. The inherent difficulty of the task itself is confirmed and deserves further investigations, as assigning a definite personality is an extremely subjective and complex task, even for humans. Personal-ITY is made available to further investigate the above and other issues related to personality detection in Italian. The corpus can lend itself to a psychological analysis of the linguistic cues for the MBTI personality traits. On this line, it is interesting to investigate the presence of evidences linking linguistic features with psychological theories about the four considered dimensions (EXTRAVERT-INTROVERT, INTUITIVE-SENSING, FEELING-THINKING, PERCEIVING-JUDGING). First results in this direction are presented in (Bassignana et al., 2020).