Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language
Abstract
This work investigates the identification of Charismatic 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.
Keywords
Cite
@article{arxiv.2409.18984,
title = {Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language},
author = {Yasser Saeid and Felix Neubürger and Stefanie Krügl and Helena Hüster and Thomas Kopinski and Ralf Lanwehr},
journal= {arXiv preprint arXiv:2409.18984},
year = {2024}
}
Comments
The 2024 IEEE 3rd Conference on Information Technology and Data Science, CITDS 2024