A0213
Title: Automated question answering for unveiling leadership dynamics in U.S. presidential speeches
Authors: Krzysztof Rybinski - Vistula University Warsaw (Poland) [presenting]
Abstract: An innovative methodology is introduced utilising deep learning and automated question-answering techniques to explore leadership dynamics. The research analyses 989 speeches from all U.S. Presidents, applying a machine learning model to decipher the essence of effective leadership within the context of presidential rhetoric. This approach facilitates an interrogation of "What constitutes a great leader?" by extracting pertinent attributes from these presidential discourses. The study conducts a comprehensive statistical examination of the responses generated across historical epochs. Furthermore, the research employs a regression analysis that extends over 120 years, integrating the attributes of outstanding leadership with the Economic Policy Uncertainty (EPU) index specific to the United States. The findings indicate that periods marked by heightened uncertainty necessitate leaders with a charismatic approach, whereas servant leadership is more effective during times of reduced uncertainty. After an extensive validation and robustness assessment, it was concluded that the outcomes are steadfast, notwithstanding variations in key parameters of the deployed machine learning models. Moreover, these findings align coherently with the qualitative assessments of U.S. Presidential views on leadership undertaken in prior research, thus contributing novel insights into the intricate relationship between leadership qualities and historical and economic contexts.
