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NLP for Social Science
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Lecture Notes
Session 1 - Introduction to NLP for Social Science
1.1 Fundamentals of NLP and its Evolution
1.2 Overview of Generative LLMs
1.3 Ethical Considerations and Challenges in Using LLMs for Research
Session 2: Traditional NLP Techniques and Text Preprocessing
2.1 Text Cleaning, Normalization, and Representation
2.2 Basic NLP Tasks
2.3 Topic Modeling and Latent Dirichlet Allocation (LDA)
Session 3: LLMs for Data Annotation and Classification
3.1 Zero-shot Learning with LLMs
3.2 Few-shot Learning and Prompt Engineering
3.3 Comparing LLM Performance with Traditional Supervised Learning
Session 4: Generative Explanations and Summaries in Social Science
4.1 Using LLMs for High-Quality Text Generation
4.2 Social Bias Inference and Analysis
4.3 Figurative Language Explanation and Cultural Context
Session 5: Advanced Applications of LLMs in Social Science Research
5.1 Analyzing Large-Scale Textual Data
5.2 Misinformation and Fake News Detection
5.3 Future Directions and Emerging Trends
Extras
Extra 1: The Evolution and Impact of LLMs in Social Science Research
Extra 2: Text Representation and NLP Pipeline
Extra 3: Practical Considerations for Using LLMs in Social Science Research
Extra 4: Advanced Considerations for LLMs in Social Science Research
Labs
Lab Session 1: Introduction to NLP for Social Science
Lab Session 2: LLMs for Data Annotation and Classification
Lab Session 3: Applying Traditional NLP Techniques
Projects
NLP Analysis of Sierra Club Press Releases
Climate Risk Analysis of Sierra Club Press Releases
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