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arXiv:cs.AI· Haofei Xu, Umar Iqbal, Jacob M. Montgomery·· 4 小时前AI 评分73

Google AI Overviews 大规模测量研究:激活率、来源质量、断言忠实度与出版商影响

Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact

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一项发表于 IMC 2026 的大规模纵向测量研究,在 40 天窗口内向 19 个主题类别发出 55,393 条热门查询,系统测量 Google AI Overviews(AIOs)。

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Abstract:Google AI Overviews (AIOs) are arguably the most widely encountered deployment of generative AI, reaching over 2 billion users who may not realize the answers they see are AI-generated. Where search engines have traditionally surfaced ranked sources and left users to evaluate them, AIOs synthesize and deliver a single answer - giving Google unprecedented editorial control over what users read and know. We present a large-scale longitudinal measurement study, issuing 55,393 trending queries across 19 topical categories over a 40-day window (March 13 - April 21, 2026). We report four main findings. First, overall AIO activation is 13.7%, rising to 64.7% for question-form queries, while politically sensitive topics see markedly lower rates. Second, AIO-cited domains are more credible than co-displayed first-page results, yet nearly 30% do not appear in those results at all, indicating a source selection mechanism distinct from Google's ranking algorithm. Third, decomposing responses into 98,020 atomic claims, 11.0% are unsupported by the cited pages - with omission the dominant failure mode - and source quality and claim fidelity are largely independent. Fourth, well over half of AIO-cited pages carry display advertising, meaning publishers lose revenue when AIOs suppress the click-through, even as Google's own sponsored ads continue to appear on the same page. Together, these findings document a rapid transformation of the online information ecosystem whose consequences for epistemic security remain poorly understood.
Comments: Accepted to the 2026 ACM Internet Measurement Conference (IMC 2026)
Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.14021 [cs.CY]
  (or arXiv:2605.14021v2 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2605.14021

arXiv-issued DOI via DataCite

Journal reference: Proceedings of the 2026 ACM Internet Measurement Conference (IMC '26), 2026, pp. 529-546
Related DOI: https://doi.org/10.1145/3777912.3839818

DOI(s) linking to related resources

Submission history

From: Haofei Xu [view email]
[v1] Wed, 13 May 2026 18:34:39 UTC (727 KB)
[v2] Thu, 1 Oct 2026 19:16:45 UTC (918 KB)

来源:arXiv:cs.AI · arxiv.org