VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility
Large language models (LLMs) are increasingly used for election information, yet little is known about how external web content shapes the political information they present. Generative engine optimization (GEO) rewrites web content to improve its visibility in generated responses. To investigate its potential risks in voting scenarios (VoteGEO), we introduce VoteGEO-Bench, a benchmark covering the 2024 U.S. presidential election and the 2026 Japanese House of Representatives election, combining election-related queries, historical webpages, and voter personas. We first analyze sources cited by generative engines and find that many citations come from lower-cost owned and open-platform so...
| Authors | Shiqi Zhang, Xiaokui Xiao |
|---|---|
| Primary category | cs.IR |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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