Table of contents

Stance Detection in Natural Language Processing

A graduate-level path from linguistic foundations and neural modeling to robust, deployable stance detection systems

Read each section in order. Every title can be opened as a TheoryTrace document.

• Cover
• Copyright
• How to read this book
• Introduction
• Chapter 1: The Problem of Stance Detection
• Chapter 2: Linguistic and Pragmatic Foundations
• Chapter 3: Mathematical Foundations for NLP Models
• Chapter 4: Data, Annotation, and Task Design
• Chapter 5: Text Preprocessing and Classical Feature Engineering
• Chapter 6: Classical Machine Learning Baselines
• Chapter 7: Distributed Representations and Neural Sequence Models
• Chapter 8: Transformer Architectures for Stance Detection
• Chapter 9: Target-Aware and Context-Aware Modeling
• Chapter 10: Evaluation Methodology and Experimental Design
• Chapter 11: Domain Adaptation and Cross-Target Generalization
• Chapter 12: Data Scarcity, Weak Supervision, and Semi-Supervised Learning
• Chapter 13: Prompting and Large Language Models
• Chapter 14: Argumentation, Evidence, and Reasoning
• Chapter 15: Conversation, Social Networks, and User-Level Stance
• Chapter 16: Multilingual and Cross-Lingual Stance Detection
• Chapter 17: Robustness, Bias, Fairness, and Ethics
• Chapter 18: Interpretability and Model Diagnostics
• Chapter 19: Building a Research-Grade Stance Detection System
• Chapter 20: Deployment, Monitoring, and Research Frontiers
• Conclusion