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LLM-Based Frame and Stance Annotation for 19th-Century Rumour Discourse in US and UK Newspapers

Large language models (LLMs) are increasingly used for lexicographic support, neology detection, and semantic categorization, yet their behaviour on historical newspapers remains under-evaluated. This short paper describes an ongoing project that extends a DH2026-accepted two-phase methodology for extracting and tracking rumours in historical newspapers. From large-scale US and UK corpora (PleIAs/US-PD-Newspapers; biglam/hmd_newspapers), the DH workflow produces gold-standard sentence-level rumour instances with proposition-like "rumour content" spans (Rumour_Content/Cleaned_Content) and extraction-pattern metadata. Building on these historically grounded units, we propose an LLM-centered benchmark and analysis pipeline for assigning topical frames and evidential stance to rumour propositions, and for auditing "temporal projection" when models introduce anachronistic modern misinformation framings. For controlled cross-variety comparison we construct a strictly balanced benchmark of 800 instances over two well-attested bins (1840–1859, 1860–1879) and both national varieties (200 per country per bin). We outline prompt conditions (text-only vs time-aware vs historically calibrated) and self-consistency voting to quantify label stability and error modes. A small manually annotated subset supports evaluation, while the main contribution is the benchmark design, prompts, and reproducible protocol enabling community feedback before full-scale results are finalized.