This paper describes the submission of team POINTERS to the UZH ArgMining 2026 Shared Task, which aims to recover the argumentation structure of UN and UNESCO resolutions by labeling paragraph types, assigning specific tags, and predicting relations between paragraphs. We take a generative approach, treating each resolution as a sequence of claim-evidence pairs connected by explicit reasoning strategies. First, each paragraph is classified as preambular or operative and assigned tags, with the model required to quote specific phrases to justify every decision. Second, for each paragraph, we first retrieve semantically related candidates using sentence transformers, then use reasoning strategies as a diagnostic scaffold to label the relation—supporting, complemental, contradictive, or modifying—along with a quoted, strategy-grounded rationale.

In times of crisis, the human mind is often a voracious information forager. It might not be immediately apparent what one wants or needs, and people frequently look for answers to their most pressing questions and worst fears. In that context, the pandemic has demonstrated that social media sources, like erstwhile Twitter, are a rich medium for data-driven communication between experts and the public.However, as lay users, we must find needles in a haystack to distinguish credible and actionable information signals from the noise. In this work, we leverage the literature on crisis communication to propose an AI-driven sensemaking model that bridges the gap between what people seek and what they need during a crisis. Our model learns to contrast social media messages concerning expert guidance with subjective opinion and enables semantic interpretation of message characteristics based on the communicative intent of the message author. We provide examples from our tweet collection and present a hypothetical social media usage scenario to demonstrate the efficacy of our proposed model.