The study measures Google AI Overviews (AIOs), noting they activate for 13.7% of queries overall and 64.7% of question-form queries, with lower rates for politically sensitive topics. AIOs cite more credible domains than typical search results, yet 30% of cited pages are not shown in the first-page results, indicating a distinct source selection process. Among 98,020 atomic claims, 11.0% lack support from cited pages, mainly due to omissions, and over half of cited pages contain display ads, reducing publisher revenue while Google’s ads remain.
By Haofei Xu, Umar Iqbal, Jacob M. Montgomery
arXiv:2603.16138v2 Announce Type: replace-cross
Abstract: Generative search systems are increasingly replacing link-based retrieval with AI-generated summaries, yet little is known about how these sy...
By Michelle Huang, Agam Goyal, Koustuv Saha, Eshwar Chandrasekharan
The study examines how conversational LLM agents—specifically ChatGPT, Claude, Grok, and DeepSeek—use Web search, combining real user interactions with controlled API experiments. It finds that agents differ in when they decide to search, how they craft queries, and which domains they favor, and that more frequent searching does not always improve answer quality. While most responses are grounded in search results, some claims are unsupported, raising attribution concerns.
By Mahsa Amani, Seungeon Lee, Abhisek Dash, Asmaa El Fraihi, Yunah Jang, Elisabeth Kirsten, Qinyuan Wu, Krishna P. Gummadi, Manish Gupta, Abhilasha Ravichander, Muhammad Bilal Zafar, Soumi Das
arXiv:2510. 11560v2 Announce Type: replace-cross Abstract: The advent of LLMs has given rise to generative search, a new search paradigm in which LLMs retrieve information from the web related to a query and synthesize it into a single, coherent response.
By Elisabeth Kirsten, Jost Grosse Perdekamp, Qinyuan Wu, Mihir Upadhyay, Krishna P. Gummadi, Muhammad Bilal Zafar
arXiv:2607. 07652v1 Announce Type: cross Abstract: Search engines have long allocated attention on the web by routing users from queries to websites.
By Qiaoni Shi, Kai Zhu, Kai Gu
arXiv:2606. 30905v1 Announce Type: cross Abstract: Community Notes, a bridging-based crowd-sourced fact-checking system, has emerged as a new mechanism for moderating misleading information on social media and has been adopted by major platforms including X, Facebook, Instagram, Threads, and TikTok.
By Soham De, Isaac Slaughter, Jiawei Guo, Qiao-Yun Cheng, Jiayuan Yan, Sruti Banerjee, Martin Saveski
The paper reports the first large‑scale empirical comparison of AI‑agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities. It finds that Moltbook shows extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross‑community author overlap (33.8% vs. 0.5%). Linguistically, AI‑generated content is emotionally flattened, more assertive than exploratory, and socially detached, leading to community‑level homogenization that is largely a structural artifact of shared authorship. At the individual level, AI agents are more identifiable than human users due to outlier stylistic profiles amplified by their extreme posting volume.
By Agam Goyal, Olivia Pal, Hari Sundaram, Eshwar Chandrasekharan, Koustuv Saha
arXiv:2607. 11039v1 Announce Type: cross Abstract: Young job seekers frequently turn to social media to compare themselves with peers and make sense of career possibilities.
By Pengping Tan, Baoquan Zhao, Zhenhui Peng
arXiv:2605. 12887v2 Announce Type: replace-cross Abstract: Web-enabled LLM agents are changing how online information influences search outcomes.
By Hengwei Ye, Jiasheng Mao, Zhenhan Guan, Zheng Tian
arXiv:2607. 20328v1 Announce Type: cross Abstract: This study empirically analyzed generative AI as an emerging discovery pathway to academic library resources.
By Hae Min Kim, Stacy Stanislaw
arXiv:2609.00319v1 Announce Type: cross
Abstract: Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compos...
By Phuong Anh Nguyen, Jill Noorily, Matthew Flathers, Haruka Notsu, Laura Ospina-Pinillos, Tommy Nguyen, Samantha Clark, Aoife Keane, Grace Thompson, John Torous
The paper "Query Implied Generative Engine Optimization" introduces QI‑GEO, a method that infers user intent directly from documents to enhance visibility in Generative Search Engines. By approximating a document’s intent space, QI‑GEO identifies missing yet relevant content, improving objective scores by up to 15.9% and subjective scores by up to 17.6% on GEO‑Bench datasets. The approach yields nearly twice as many citation gains as losses, demonstrating that document‑derived intent approximations can boost content visibility without explicit query inputs.
By Shilpa Ramakrishna, William B. Andreopoulos