arXiv cs.CY· Shiqi Zhang, Xiaokui Xiao·· 2 天前AI 评分34
VoteGEO-Bench：衡量生成引擎优化对选举信息可见度的影响
VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility
AI 导读
研究者提出 VoteGEO-Bench 基准，覆盖 2024 年美国大选与 2026 年日本众议院选举，结合选举查询、历史网页和选民画像，评估生成引擎优化（GEO）对选举信息可见度的影响。
正文 · 原文
Abstract: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 sources, while some webpages remain visible across days. We then evaluate 11 rewriting strategies in 4 categories, examining target specification, effectiveness across engines and elections, and the role of voter personas. Rewriting without specifying a target rarely increases target exposure, whereas target-specified rewriting more often does. Strategies that improve page organization or strengthen a position through argumentation yield greater gains than adding supporting evidence or changing wording. For the two strongest strategies, gains remain broadly positive across evaluated U.S. personas, but persona conditioning shows no consistent effect on these gains. Explicit partisan identities can create baseline exposure gaps larger than the gain from rewriting one webpage. When partisanship is implicit in the voter profile, baseline shifts depend on how strongly the engine responds to that signal.
| Subjects: | Information Retrieval (cs.IR); Computation and Language (cs.CL); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.21330 [cs.IR] |
| (or arXiv:2610.21330v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.21330 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shiqi Zhang [view email]
[v1]
Thu, 15 Oct 2026 06:18:55 UTC (106 KB)
来源：arXiv cs.CY · arxiv.org