Course Syllabus

Course Syllabus#

Course Description#

This course introduces social science researchers to Natural Language Processing (NLP) techniques, with a specific focus on leveraging Large Language Models (LLMs) for social science applications. Students will learn how to apply cutting-edge NLP methods to analyze textual data, extract insights, and address complex social science research questions.

Course Objectives#

By the end of this course, students will be able to:

  1. Understand fundamental NLP concepts and their relevance to social science research

  2. Apply traditional NLP techniques and modern LLM-based approaches to social science data

  3. Design and implement NLP-driven research projects in social science contexts

  4. Critically evaluate the potential and limitations of NLP methods in social science research

  5. Address ethical considerations in the use of AI and NLP in social science studies

Prerequisites#

  • Basic programming skills in Python

  • Familiarity with basic statistical concepts

  • Understanding of fundamental social science research methods

Course Structure#

The course consists of five sessions, each covering key aspects of NLP for social science:

Session 1: Introduction to NLP and Its Applications in Social Science#

  1. Fundamentals of Natural Language Processing and its evolution

  2. Overview of Generative Large Language Models (LLMs)

  3. Ethical Considerations and Challenges in Using LLMs for Research

Session 2: Traditional NLP Techniques and Text Preprocessing#

  1. Text Cleaning, Normalization, and Representation

  2. Basic NLP Tasks

  3. Topic Modeling and Latent Dirichlet Allocation (LDA)

Session 3: LLMs for Data Annotation and Classification#

  1. Zero-shot Learning with LLMs

  2. Few-shot Learning and Prompt Engineering

  3. Comparing LLM Performance with Traditional Supervised Learning

Session 4: Generative Explanations and Summaries in Social Science#

  1. Using LLMs for High-Quality Text Generation

  2. Social Bias Inference and Analysis

  3. Figurative Language Explanation and Cultural Context

Session 5: Advanced Applications of LLMs in Social Science Research#

  1. Analyzing Large-Scale Textual Data

  2. Misinformation and Fake News Detection

  3. Future Directions and Emerging Trends

Assessment#

  • Weekly coding assignments (40%)

  • Midterm project: Applying NLP techniques to a social science dataset (25%)

  • Final project: Designing and implementing an LLM-based social science study (35%)

Required Materials#

  • Python 3.7 or higher

  • Jupyter Notebook or Google Colab

  • Required libraries: NLTK, spaCy, Gensim, Transformers, TensorFlow or PyTorch

Additional Resources#

  • Online tutorials and documentation for Python NLP libraries

  • Research papers demonstrating NLP applications in social science

  • Guest lectures from experts in NLP and social science research

Schedule#

Week

Topic

Assignments

1

Introduction to NLP and LLMs

Python setup, basic NLP exercises

2

Traditional NLP and Text Preprocessing

Text cleaning and representation tasks

3

LLMs for Data Annotation and Classification

Zero-shot and few-shot learning exercises

4

Generative Explanations and Summaries

Text generation and bias analysis project

5

Advanced Applications and Future Trends

Final project proposal and implementation
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