SPADE: A Big Five-Mturk Dataset of Argumentative Speech Enriched with Socio-Demographics for Personality Detection

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 6405–6419 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 6405 ♠ SPADE: A Big Five-Mturk Dataset of Argumentative Speech Enriched with Socio-Demographics for Personality Detection Elma Kerz1, Yu Qiao1, Sourabh Zanwar1, Daniel Wiechmann2 1RWTH Aachen University, 2University of Amsterdam yu.qiao@rwth-aachen.de, sourabh.zanwar@rwth-aachen.de, elma.kerz@ifaar.rwth-aachen.de, d.wiechmann@uva.nl Abstract In recent years, there has been increasing interest in automatic personality detection based on language. Progress in this area is highly contingent upon the availability of datasets and benchmark corpora. However, publicly available datasets for modeling and predicting personality traits are still scarce. While recent efforts to create such datasets from social media (Twitter, Reddit) are to be applauded, they often do not include continuous and contextualized language use. In this paper, we introduce ♠ SPADE, the first dataset with continuous samples of argumentative speech labeled with the Big Five personality traits and enriched with socio-demographic data (age, gender, education level, language background). We provide benchmark models for this dataset to facilitate further research and conduct extensive experiments. Our models leverage 436 (psycho)linguistic features extracted from transcribed speech and speaker-level metainformation with transformers. We conduct feature ablation experiments to investigate which types of features contribute to the prediction of individual personality traits. Keywords: Automatic personality detection, language-based personality, argumentative speech, Big Five, dataset 1. Introduction People’s personality comprises a set of individual dif- ferences, often referred to as psychological traits and/or dimensions, that describe and explain their behavior, emotions, motivation, and thought patterns (Funder, 2001; Wilt and Revelle, 2015). Personality traits are not observable directly, but are reflected in the recur- ring behavioral patterns of people and can thus be de- rived from them. These traits are associated with a vari- ety of important life outcomes and decisions (Ozer and Benet-Martinez, 2006). Specifically, they have been repeatedly linked to individual (e.g., well-being), in- terpersonal (e.g., relationship satisfaction), and social- institutional outcomes (e.g., career choice and career success) (Soto, 2019). In addition, attitudes and so- cial behavior toward a particular person depend sig- nificantly on the impression others have of that per- son (Uleman et al., 2008). In view of its importance in capturing the essential aspects of a person, increas- ing attention is being paid to the development of mod- els that can leverage data on human behavior to au- tomatically predict personality. Language data - i.e. data obtained from verbal behavior, including written text or audio recordings - is a key type of such data. Even in the early years of psychology, a person’s use of language was seen as a distillation of his or her un- derlying drives, emotions, and thought patterns (Freud, 1915; Rorschach, 1921; Allport, 1942). In the 1960s, the so-called General Inquirer was introduced, which emerged as the first general and systematic approach to psychological language analysis (Stone et al., 1962). The introduction of Linguistic Inquiry and Word Count (LIWC) (Pennebaker and King, 1999; Pennebaker et al., 2007) was a milestone in transparent …

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