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The work reports preliminary experiments that benchmark Personal-ITY, analyze which MBTI types are easier to predict, and evaluate the benefits of cross-dataset prediction for future research.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/personal-ity-a-novel-youtube-based-corpus-for-personality-prediction-in-italian/293362/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/personal-ity-a-novel-youtube-based-corpus-for-personality-prediction-in-italian/293362.png","ImageObject",300,407,{"name":42,"@type":43},"\tCallum ","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",14,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is Personal-ITY and what task does it support?","Question",{"text":63,"@type":64},"Personal-ITY is a new Italian corpus of YouTube comments annotated with MBTI personality traits. 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The corpus is built exploiting Distant Supervision, assigning MyersBriggs Type Indicator (MBTI) labels to YouTube comments, and can lend itself toa 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.  \n1 Introduction  \nWhen 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 thereis another important aspect: personality (Snyder, 1983; Parks and Guay, 2009) .  \nHuman Personality is a psychological construct aimed at explaining the wide variety of human behaviours in terms of a few, stable and measurable individual characteristics (Vinciarelli and Mohammadi, 2014) .  \nSuch characteristics are formalised in Trait Models, and there are currently two of these models that are widely adopted: Big Five (John and Srivastava, 1999) and Myers-Briggs Type Indicator (MBTI) (Myers and Myers, 1995) . 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  \nCopyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4 .0 International (CC BY 4 .0) .  \nsecond one, instead, considers 16 fixed personality types, coming from the combination of the opposite poles of 4 main dimensions (EXTRAVERTINTROVERT, INTUITIVE-SENSING, FEELINGTHINKING, PERCEIVING-JUDGING) . Examples of full personality types are therefore four letter labels such as ENTJ or ISFP.  \nThe 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 (Schwartz et al., 2013) . 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 (Youyou et al., 2015; Argamon et al., 2009; Litvinova et al., 2016; Whelan and Davies, 2006) .  \nPersonality 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.  \nAs 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 1.  \n2 Related Work  \nThere exist a few datasets annotated for personality traits. For the shared tasks organised within the Workshop on Computational Personality Recognition (Celli et al., 2013), two datasets annotated with the Big Five traits have been released in 2013  \n1 [https://github.com/elisabassignana/](https://github.com/elisabassignana/)[ ](https://github.com/elisabassignana/)Personal-ITY  \n\n| Corpus | Model | \\# user | Avg. |\n| --- | --- | --- | --- |\n| PAN2015 | Big Five | 38 | 1258 |\n| TWISTY | MBTI | 490 | 21.343 |\n| Personal-ITY | MBTI ","cbCaikFfs8OSh30A","https://ap.wps.com/l/cbCaikFfs8OSh30A","pdf",217390,"English","# Abstract\n# Introduction\n# Related Work","[{\"question\":\"What is Personal-ITY and what task does it support?\",\"answer\":\"Personal-ITY is a new Italian corpus of YouTube comments annotated with MBTI personality traits. It supports automatic personality prediction from written text.\"},{\"question\":\"How are MBTI labels assigned in the corpus?\",\"answer\":\"The corpus is built using Distant Supervision, assigning Myers-Briggs Type Indicator (MBTI) labels to YouTube comments.\"},{\"question\":\"What do the preliminary experiments focus on?\",\"answer\":\"The experiments provide a baseline for future work, show that some personality types are easier to predict than others, and discuss advantages of cross-dataset prediction.\"}]","Personal-ITY - A Novel YouTube-based Corpus for Personality Prediction in Italian | PDF",18]