As traditional surveys grow more expensive and response rates collapse, large language models offer a provocative alternative: inferring public opinion from the digital traces people already leave behind. This hands-on workshop walks participants through PoSSUM — our Protocol for Surveying Social Media Users with Multimodal LLMs — the AI polling method whose state-by-state forecasts of the 2024 US presidential election tracked, and at times outperformed, the leading poll aggregators. We move through the full pipeline: designing the digital interview, building a “silicon” sample of social-media users, and producing bias-corrected estimates with multilevel regression and post-stratification (MrP) in R, validated against ground-truth election results. A featured sub-theme is our Swiss “Silicon Politicians” study, which predicts how individual politicians and citizens vote in referendums from their social-media traces alone — and showcases the app we built around it. Throughout, we keep the harder question in view, drawing on the recent APSA report Public Opinion in the Age of AI: when does simulating respondents enrich measurement, and when does it risk manufacturing the opinion it claims to observe?

Aimed at pollsters, political scientists, and survey methodologists comfortable with R; no machine-learning background required.