This presentation walks through an end-to-end pipeline for mapping controversies in a 5,816-article French press corpus on advertising and media, built on Gemma-4 31B with thinking mode and a 256K-token context. Stage 1 casts the model as a Latourian sociologist and extracts 5–7 claims per article; Stage 2 embeds the 34,867 resulting claims with BGE-large and clusters them via UMAP → HDBSCAN, surfacing six dense controversies — greenwashing, privacy and tracking, generative AI, sexism in advertising, social-vs-traditional media, and the obsolescence of the traditional advertising model. Stage 3 re-reads only the articles of each cluster under a cluster-specific domain-expert persona, extracting companies with a role (e.g. accused / committed / defending / praised), a direct-quote evidence, and a 0–10 relevance score. The results recover the expected antagonists TotalEnergies on greenwashing, Google and Meta on privacy and surface sharper findings: Patagonia as the lone credible counter-example in greenwashing coverage, Aubade as a genuinely contested actor across the sexism debate, and a near-unanimous pro-AI stance that reveals the absence of live controversy rather than its presence. The contribution is methodological: a reusable three-stage design in which clustering supplies the topical lens that a single general prompt cannot.