Harnessing Large Language Models: Fine-Tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language

• Published in: 2024 IEEE 3rd Conference on Information Technology and Data Science (CITDS) pp. 1 - 6
• Main Authors: Saeid, Yasser, Neuburger, Felix, Krugl, Stefanie, Huster, Helena, Kopinski, Thomas, Lanwehr, Ralf
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.08.2024
• Subjects: Bidirectional control; charismatic leadership; computational linguistics; Data models; Encoding; Generative AI; Large Language Models; Leadership; Machine learning; natural language processing; Natural languages; Performance evaluation; political speeches; Psychology; Training; Transformers
• DOI: 10.1109/CITDS62610.2024.10791373

This work investigates the identification of Charis-matic Leadership Tactics (CLTs) in natural language using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. Based on an own extensive corpus of CLTs generated and curated for this task, our methodology entails training a machine learning model that is capable of accurately identifying the presence of these tactics in natural language. A performance evaluation is conducted to assess the effectiveness of our model in detecting CLTs. We find that the total accuracy over the detection of all CLTs is 98.96% The results of this study have significant implications for research in psychology and management, offering potential methods to simplify the currently elaborate assessment of charisma in texts.