Stance Detection on Nigerian 2023 Election Tweets Using BERT: A Low-Resource Transformer-Based Approach

AbstractThis paper investigates stance detection on Nigerian 2023 election tweets by comparing transformer-based and classical machine learning models. A balanced dataset of 2,100 annotated tweets was constructed, and BERT-base-uncased was fine-tuned to classify stances into Favor, Neutral, and Against. The model achieved 98.1% accuracy on an 80/20 split and an F1-score of 96.9% under 5-fold cross-validation. Baseline models such as Naïve Bayes, Logistic Regression, Random Forest, and SVM were also evaluated, with SVM achieving 97.6% F1. While classical methods remain competitive on curated datasets, BERT proved more robust in handling noisy, sarcastic, and ambiguous text, making it better suited for real-world applications in low-resource African NLP contexts.