Synthetic Data Generation for Communication Research in the Age of Large Language Models

Abstract

This chapter provides a comprehensive introduction to synthetic data generation for communication research, outlining both foundational methods and emerging large language model (LLM)-based approaches. I begin by reviewing traditional techniques – including rule-based templates, statistical text models, agent-based simulations, and deep-learning generators such as generative adversarial networks and variational autoencoders – that have historically enabled researchers to mimic linguistic style and model information diffusion. I then examine how LLMs expand these capabilities by producing naturalistic language, adopting personas, simulating interactions, and tailoring communication scenarios to specific contexts and audiences. Key considerations such as data realism, diversity, privacy, memorization risks, and ethical constraints are discussed, alongside strategies for evaluating synthetic datasets. This chapter concludes with hands-on demonstrations of LLM-driven synthetic communication pipelines, including instruction prompting, persona modeling, debate simulations, and hybrid LLM-agent systems. Together, these tools equip researchers to responsibly create and analyze synthetic datasets for studying communication dynamics.