The Global Leader Personality Dataset (GLPD), 1946-2022

This data project generates the personality scores for over 10,000 global leaders spanning a period of 75 years (1946 – 2022). Speeches, organized by year, are bootstrapped 100 times at the sentence level, and are processed through psycholinguistic dictionaries -- LIWC and MRCPD. This step generates counts and proportions of word usage across all linguistic categories in the relevant dictionaries. These counts and proportions are plugged into a pre-trained Personality Recognizer Model (Mairesse et al. 2007) in line with similar previous work on US legislators (Ramey et al. 2017). The model returns the Big Five personality scores (openness, conscientiousness, extraversion, agreeableness, and emotional stability) via SMOreg (Support Vector Machine Regression) on a scale of 1 to 7, where the larger value indicates the speaker ranking higher on a relevant trait and vice versa. By analyzing thousands of speeches delivered at the United Nations General Debates (UNGD) to detect the personalities of global leaders, this project contributes to the growing body of first image research, particularly within the psychological approach in International Relations and its stream of Foreign Policy Analysis. Theoretically, it advances trait models in the psychological approach by building on the lexical theory of personality psychology to examine the traits of global elites. Decades of research have demonstrated that linguistic cues, such as utterances, not only convey semantic information but also reveal aspects of the speaker, including personality traits. Methodologically, to the best of my knowledge, this project represents the first attempt in International Relations to analyze the linguistic cues of global leaders using machine learning tools to assess their psychological characteristics based on the Big Five framework. With its stability across the adult lifespan and generalizability across domains and cultures, the Big Five provides a parsimonious tool to study the personality traits of global leaders, reducing concerns about context- and time-specificity that affect other established content-analytic at-a-distance methods, such as Leadership Trait Analysis (LTA). Additionally, this project and relevant works based on this dataset demonstrate that translated speeches can capture the nuances of native languages using existing psycholinguistic dictionaries, even without developing a language-specific coding scheme. Moreover, I show that the psycholinguistic properties of UNGD speeches are highly comparable to more spontaneous forms of text, such as media interviews, which are often considered to reflect a “truer” personality. These findings give scholars greater confidence in using widely available public statements for analysis, despite the scarcity of more spontaneous textual statements.