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Here’s an introduction to the NLP for Social Science course:

Welcome to our course on Natural Language Processing (NLP) for Social Science. This cutting-edge course is designed to bridge the gap between advanced NLP techniques, particularly those leveraging Large Language Models (LLMs), and social science research methodologies.

Course Overview

In recent years, the field of NLP has undergone a revolutionary transformation with the advent of powerful generative LLMs. These models have opened up new possibilities for analyzing and understanding human behavior and societal phenomena at an unprecedented scale. This course aims to equip social science researchers with the knowledge and skills to harness these powerful tools effectively and responsibly.

Course Objectives

By the end of this course, you will:

1. Understand the fundamentals of NLP and its evolution from traditional techniques to modern LLM-based approaches.

2. Gain hands-on experience with both traditional NLP methods and state-of-the-art LLM applications.

3. Learn to apply NLP techniques to real-world social science research questions.

4. Develop critical thinking skills to evaluate the benefits and limitations of LLM-based approaches in social science contexts.

5. Understand the ethical considerations and challenges in using LLMs for research.

Course Structure

The course is structured into five sessions, each containing three lectures. We’ll begin with an introduction to NLP and its applications in social science, then move through traditional NLP techniques, LLM-based methods for data annotation and classification, generative explanations and summaries, and finally, advanced applications of LLMs in social science research.

Throughout the course, we’ll emphasize practical, hands-on learning. You’ll have the opportunity to work with Python and relevant NLP libraries, applying what you’ve learned to real social science datasets.

Why This Course Matters

As social scientists, we’re constantly seeking new ways to understand human behavior and societal dynamics. LLMs offer unprecedented capabilities in processing and analyzing vast amounts of textual data, from social media posts to historical documents. However, with great power comes great responsibility. This course will not only teach you how to use these tools but also how to do so ethically and responsibly, always keeping in mind the potential biases and limitations of these technologies.

We’re excited to embark on this journey with you, exploring the fascinating intersection of NLP and social science. Let’s dive in and discover how these powerful tools can transform our research and deepen our understanding of the social world.

Table of Contents

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

About

• Course Syllabus
• Who made this book?

Changelog

See the CHANGELOG for more information.

Contributing

Contributions are welcome! Please see the contributing guidelines for more information.

License

This project is released under the CC-BY-4.0 License.