The Ten Minutes That Shocked the World. Teaching Generative AI to Analyze the Trump-Zelensky Multimodal Debate

Today, foundation models simulate humans’ skills in translation, literature review, fact checking, fake-news detection, novel and poetry production. But Generative AI can also be applied to discourse analysis. This study instructs the Gemini 2.5 model to analyze multimodal political discourse. We selected some fragments from the Trump-Zelensky debate held at the White House on February 28, 2025, and annotated each sentence, gesture, intonation, gaze, and fa-cial expression in terms of LEP (Logos, Ethos, Pathos) analysis, to assess when speakers, in words or body communication, rely on rational argumentation, stress their own merits or the opponents’ demerits, or express and try to induce emotions in the audi-ence. Through detailed prompts, we asked the Gemini 2.5 model to run the LEP analysis on the same fragments. Then, considering the human’s and model’s annotations in par-allel, we proposed a metric to compare their respective analyses and measure dis-crepancies, finally tuning an optimized prompt for the model’s best performance, which in some cases outperformed the human’s analysis: an interesting application, since the LEP analysis highlights deep aspects of multimodal discourse but is highly time-consuming, while its automatic version allows us to interpret large chunks of speech in a fast but reliable way.