Is Big Five better than MBTI? A personality computing challenge using Twitter data
Authors: Fabio Celli, Bruno Lepri (Fondazione Bruno Kessler, 2018)
Topic: Textual Genres & Literature Linguistics

Abstract
Personality Computing from text has become popular in Natural Language Processing (NLP). For assessing gold-standard personality types, Big5 and MBTI are two popular models but still there is no comparison of the two in personality computing. With this paper, we provide for the ﬁrst time a comparison of the two models from a computational perspective. To do that we exploit two multilingual datasets collected from Twitter in English, Italian, Spanish and Dutch.

Introduction
The last decade has been characterized by the rise of personality computing in Natural Language Processing (NLP) (Vinciarelli and Mohammadi, 2014): for example, several works have dealt with the automatic prediction of personality traits of authors from different pieces of text they wrote in emails, blogs or social media (Mairesse et al., 2007; Iacobelli et al., 2011; Schwartz et al., 2013) (Rangel Pardo et al., 2015). Personality computing is also broadening its application to many ﬁelds in academia as well as in industry, including security (Golbeck et al., 2011), human resources (Turban et al., 2017), advertising (Celli et al., 2017) and deception detection (Fornaciari et al., 2013). Historically, there are two popular but very different psychological tests to asses personality: (i) the Big Five (Costa and McCrae, 1985; Costa and McCrae, 2008), which is widely accepted in academia, and (ii) the Myers Briggs Type Indicator (MBTI) (Myers and Myers, 2010), which is very popular and widely used in industry. The Big Five model deﬁnes personality along 5 bipolar scales: Extraversion (sociable vs. shy); Emotional Stability (secure vs. neurotic); Agreeableness (friendly vs. ugly); Conscientiousness (organized vs. careless); Openness to Experience (insightful vs. unimaginative). In contrast, the MBTI deﬁnes 4 binary classes that combines into 16 personality types: Extraversion/Introversion, Sensing/Intuition, Perception/Judging, Feeling/Thinking. Correlation analyses of the personality measures showed that Big Five Extraversion was correlated with MBTI Extraversion-Introversion, Openness to Experience was correlated with Sensing-Intuition, Agreeableness with Thinking-Feeling and Conscientiousness with Judging-Perceiving (Furnham et al., 2003). A reason for the recently gained popularity of MBTI is the fact that it is easier to collect gold-standard labelled data about MBTI than about Big Five, as an MBTI type is a 4-letter coding (e.g., INTJ) that could be retrieved with simple queries. In a ﬁeld like personality computing, where data is costly and difﬁcult to collect, this is an enormous advantage. In this paper we address the question whether it is easier to predict Big Five or MBTI classes with a machine learning approach. To do so, we collect two Twitter datasets in English, Italian, Dutch and Spanish, one annotated with the Big Five personality types and one with MBTI. We believe that this work will be useful for the scientiﬁc community of personality computing to better understand the heuristic power of the two models when applied to machine learning tasks. The paper is structured as follows: in the next section we provide an overview of related works in the ﬁeld of personality computing in NLP, in Section 3 we describe the datasets we used, in Section 4 we report the results of our experiments and in Section 5 we draw some conclusions.

Conclusion
Conclusion Conclusion In this paper we provide for the ﬁrst time a comparison of Big Five and MBTI from a personality computing perspective. To do so we use two multilingual Twitter datasets, one annotated with Big Five classes and one with MBTI classes. For the ﬁrst time, we provide an evidence that algorithms trained on MBTI could have better performances than trained on the Big Five, although the Big Five is much more informative and has great variability in performance depending also on the algorithm used for the prediction. We let available the ﬁles used for the experiments2, in order to grant the replicability or improvement of the results. 2http://personality.altervista.org/fabio.htm