A chatbot's assumed partisan persona shifts election-answer exposure more than rewriting a single webpage does, in VoteGEO-Bench (Zhang & Xiao 2026)

GEOgenerative-engine-optimizationelectionsLLMpolitical-persuasionarxiv

VoteGEO-Bench compares two distinct levers on the Democratic Party's measured "Share of Voice" (SOV) in U.S. election answers: assigning the chatbot a persona that explicitly identifies as a Democrat versus a Republican voter, and rewriting a single webpage in the retrieved pool using one of the benchmark's two strongest GEO strategies (Comparative Distinction) while targeting the Democratic Party. On every engine tested, the persona-driven baseline gap was larger than the rewrite-driven gain: the Democrat–Republican baseline gap reached 11.6, 3.4, and 3.0 percentage points on gpt-4o-mini, gpt-5-mini, and gemini-2.5-flash respectively, while Comparative Distinction increased Democratic SOV by only 2.20, 1.76, and 2.16 points under the Democrat persona on the same engines. The paper concludes that "explicit partisan identity can shift baseline exposure more than rewriting a single webpage."

This isolates two different levers on the same outcome: one external (a page author rewriting content, the GEO mechanism this benchmark otherwise studies) and one internal to the query itself (who the engine assumes it is talking to). The second moved the needle more, on every engine tested, than the best version of the first.