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This paper presents two self-contained tutorials on stance detection inTwitter data using BERT fine-tuning and prompting large language models (LLMs).The first tutorial explains BERT architecture and tokenization, guiding usersthrough training, tuning, and evaluating standard and domain-specific BERTmodels with HuggingFace transformers. The second focuses on constructingprompts and few-shot examples to elicit stances from ChatGPT and open-sourceFLAN-T5 without fine-tuning. Various prompting strategies are implemented andevaluated using confusion matrices and macro F1 scores. The tutorials providecode, visualizations, and insights revealing the strengths of few-shot ChatGPTand FLAN-T5 which outperform fine-tuned BERTs. By covering both modelfine-tuning and prompting-based techniques in an accessible, hands-on manner,these tutorials enable learners to gain applied experience with cutting-edgemethods for stance detection.

Tutorials on Stance Detection using Pre-trained Language Models: Fine-tuning BERT and Prompting Large Language Models