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    <abstract>Generative search systems are increasingly replacing link-based retrieval with AI-generated summaries, yet little is known about how these systems differ in sources, language, and fidelity to cited material. We examine responses to 11,000 real search queries across five systems—vanilla GPT, Search GPT, Perplexity Search with Grok, Google AI Overviews, and traditional Google Search—at three levels: source diversity, linguistic characterization of the generated summary, and source-summary fidelity. We find that generative search systems exhibit significant source-selection biases in their citations, favoring certain sources over others. Incorporating search also selectively attenuates epistemic markers, reducing hedging by up to 60% while preserving confidence language in the AI-generated summaries. At the same time, AI summaries further compound the citation biases: Wikipedia and longer sources are disproportionately overrepresented, whereas cited social media content and negatively framed sources are substantially underrepresented. Our findings highlight the potential for answer bubbles, in which identical queries yield structurally different information realities across systems, with implications for user trust, source visibility, and the transparency of AI-mediated information access.</abstract>
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%0 Conference Proceedings
%T Answer Bubbles: Information Exposure in AI-Mediated Search
%A Huang, Michelle
%A Goyal, Agam
%A Saha, Koustuv
%A Chandrasekharan, Eshwar
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F huang-etal-2026-answer
%X Generative search systems are increasingly replacing link-based retrieval with AI-generated summaries, yet little is known about how these systems differ in sources, language, and fidelity to cited material. We examine responses to 11,000 real search queries across five systems—vanilla GPT, Search GPT, Perplexity Search with Grok, Google AI Overviews, and traditional Google Search—at three levels: source diversity, linguistic characterization of the generated summary, and source-summary fidelity. We find that generative search systems exhibit significant source-selection biases in their citations, favoring certain sources over others. Incorporating search also selectively attenuates epistemic markers, reducing hedging by up to 60% while preserving confidence language in the AI-generated summaries. At the same time, AI summaries further compound the citation biases: Wikipedia and longer sources are disproportionately overrepresented, whereas cited social media content and negatively framed sources are substantially underrepresented. Our findings highlight the potential for answer bubbles, in which identical queries yield structurally different information realities across systems, with implications for user trust, source visibility, and the transparency of AI-mediated information access.
%U https://aclanthology.org/2026.findings-emnlp.8/
%P 147-162

Markdown (Informal)

[Answer Bubbles: Information Exposure in AI-Mediated Search](https://aclanthology.org/2026.findings-emnlp.8/) (Huang et al., Findings 2026)

• Answer Bubbles: Information Exposure in AI-Mediated Search (Huang et al., Findings 2026)

ACL

• Michelle Huang, Agam Goyal, Koustuv Saha, and Eshwar Chandrasekharan. 2026. Answer Bubbles: Information Exposure in AI-Mediated Search. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 147–162, Budapest, Hungary. Association for Computational Linguistics.