Mining top managers’ personality traits from Twitter
(R2021-104598)

AUTHORS

Giovanni Visentin, ESCP Business School; Fabrizio Zerbini, SDA Bocconi; Sandrine Macé, ESCP Business School

KEYWORDS

CEO; Personality; Twitter

ABSTRACT

Although research in marketing has examined the link between top mangers’ personality traits and a wide range of strategic outcomes, the role of business leaders’ own social media in this relationship remains relatively unexplored. Building on recent advances in natural language processing tools, this paper describes the methodological procedures adopted to infer top managers’ personality traits from social media texts. We created a dataset of 305.500 tweets from 377 U.S-based CEOs that used Twitter from 2007 to 2019 and tested a machine learning algorithm that automatically recognizes Big Five personality traits from language. After checking its convergent and discriminant validity, we find that our linguistic tool can reliably predict CEOs’ personality traits. By understanding how specific personality traits are encoded in language used in social media, this study contributes to extending our understanding of the relationship between top managers’ individual characteristics and marketing strategic outcomes.