Training Generative AI on Social Media Data: Implications and Outputs - A Worked Out Example

This chapter presents a follow-along methodological exercise that shows how social media data can be used to study generative AI training and conditioning. It is written to be reproducible by both technical and non-technical readers and does not assume access to large commercial training pipelines. Using a simulation-based experimental approach, the chapter walks through collecting text from Reddit and Twitter (X), constructing two matched datasets of 2,500 texts per platform, and preparing those datasets for comparison through careful cleaning and documentation. Analysis is carried out in Orange Data Mining, using visual, screenshot-led steps to explore sentiment, basic linguistic patterns, and framing differences across platforms. The chapter then demonstrates prompt-based conditioning with a small open-source generative model, keeping prompts and generation settings fixed while varying only the platform-specific input text. The emphasis is on methodological clarity, transparency, and cautious interpretation rather than substantive claims about public opinion.