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

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 train...

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Bibliographic Details
Published in2024 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
LanguageEnglish
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
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DOI10.1109/CITDS62610.2024.10791373

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Summary: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.
DOI:10.1109/CITDS62610.2024.10791373