This topic builds directly on Building Evidence: Data Science in the Age of AI and assumes familiarity with the foundations introduced there: how data represent the phenomenon being studied, how samples are constructed, and how patterns can be measured without claiming more than the evidence supports. Social media listening puts those methods to work on a particularly difficult form of political data. It begins with a deceptively simple question: what are people saying?
Before we can answer it, we have to decide which people, which platforms, which posts and which kinds of speech count as data. APIs, commercial datasets, scraping and platform search each produce different windows onto the conversation, shaped by technical constraints and design choices. None of them is the public itself, but each is a partial representation that must be made explicit if we are to treat it as evidence.
Once collected, the data can be filtered, classified and organised into issues, positions, communities, places and changes over time. Network analysis can show how conversations cluster and travel. New language models can process volumes that would previously have been impossible, but sophisticated analysis does not make a biased sample representative.
I have spent years analysing online political behaviour and now build pipelines that turn large collections of posts and media into structured, reviewable evidence. I can help you construct a small listening workflow—from defining a question and collecting data to categorisation, network analysis and a short political brief. The aim is to learn how to turn online conversation into useful intelligence while making its blind spots and uncertainties explicit.