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        <title>You Can Tell Who’s Asking: What the Web’s Questions Are Made Of, and Where They Come From</title>
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    <abstract>Questions scraped from the web are used across academia and industry as a proxy for what people want to know. Across QA training data, retrieval benchmarks, and content strategy, questions on a page are assumed to reflect human intent. We test this assumption at scale by extracting 13.4B question occurrences across 110 FineWeb snapshots (2013-2025), and report three findings. First, you can tell who is asking: provenance (the host/page of questions) leaves a signal in question form, and a logistic model can separate genuine user questions from templated/manufactured ones at AUC 0.725 via length and surrounding context rather than question type, though only 0.554 against commerce FAQ writing. Second, question frequency does not measure demand: the most-frequent questions are boilerplate/templated (over 70% of the top thousand), so occurrence counts measure how often a string was published and not how often it was asked. Third, over twelve years the genuine share of occurrences fell by 79% (42-56% after controlling for crawl composition), with question length and context decreasing. We present the first diachronic, occurrence-level measurement of web question provenance, and find the crawlable web’s questions have shifted from being asked by humans toward manufactured for machines to read.</abstract>
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%0 Conference Proceedings
%T You Can Tell Who’s Asking: What the Web’s Questions Are Made Of, and Where They Come From
%A Zhou, Calvin
%A McCloskey, Vincent
%A Srinivasan, Krishna
%Y Ortiz Suarez, Pedro
%Y Burchell, Laurie
%Y Ljubešić, Nikola
%Y Scherrer, Yves
%Y Laippala, Veronika
%Y Dinić, Vuk
%Y Vaughan, Thom
%S Proceedings of the 13th Web-as-Corpus Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-481-1
%F zhou-etal-2026-tell
%X Questions scraped from the web are used across academia and industry as a proxy for what people want to know. Across QA training data, retrieval benchmarks, and content strategy, questions on a page are assumed to reflect human intent. We test this assumption at scale by extracting 13.4B question occurrences across 110 FineWeb snapshots (2013-2025), and report three findings. First, you can tell who is asking: provenance (the host/page of questions) leaves a signal in question form, and a logistic model can separate genuine user questions from templated/manufactured ones at AUC 0.725 via length and surrounding context rather than question type, though only 0.554 against commerce FAQ writing. Second, question frequency does not measure demand: the most-frequent questions are boilerplate/templated (over 70% of the top thousand), so occurrence counts measure how often a string was published and not how often it was asked. Third, over twelve years the genuine share of occurrences fell by 79% (42-56% after controlling for crawl composition), with question length and context decreasing. We present the first diachronic, occurrence-level measurement of web question provenance, and find the crawlable web’s questions have shifted from being asked by humans toward manufactured for machines to read.
%U https://aclanthology.org/2026.wac-1.22/
%P 224-237

Markdown (Informal)

[You Can Tell Who’s Asking: What the Web’s Questions Are Made Of, and Where They Come From](https://aclanthology.org/2026.wac-1.22/) (Zhou et al., WAC 2026)

• You Can Tell Who’s Asking: What the Web’s Questions Are Made Of, and Where They Come From (Zhou et al., WAC 2026)

ACL

• Calvin Zhou, Vincent McCloskey, and Krishna Srinivasan. 2026. You Can Tell Who’s Asking: What the Web’s Questions Are Made Of, and Where They Come From. In Proceedings of the 13th Web-as-Corpus Workshop, pages 224–237, Budapest, Hungary. Association for Computational Linguistics.