发表机构
University of Pennsylvania; Bodhium Labs（宾夕法尼亚大学; Bodhium 实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
通过分析134亿次网络问题出现，发现来源可识别、频率不反映需求，且真实问题占比十二年间下降79%，网络问题已从人类提问转向为机器制造。
AI 中文摘要
从网络抓取的问题在学术界和工业界被广泛用作人们想知道什么的代理指标。在问答训练数据、检索基准和内容策略中，页面上的问题被假定反映人类意图。我们通过提取110个FineWeb快照（2013-2025年）中的134亿次问题出现，大规模测试了这一假设，并报告三项发现。首先，你能看出谁在提问：来源（问题的主机/页面）在问题形式中留下信号，逻辑模型可以通过长度和周围上下文而非问题类型，将真实用户问题与模板化/制造的问题区分开来，AUC为0.725，但针对商业FAQ写作仅为0.554。其次，问题频率不衡量需求：最频繁出现的问题是样板/模板化的（前一千个中超过70%），因此出现次数衡量的是字符串被发布的频率，而非被提问的频率。第三，在十二年间，真实出现的份额下降了79%（在控制爬取组成后为42-56%），问题长度和上下文也在减少。我们首次提供了网络问题来源的历时性、出现级别测量，并发现可爬取网络上的问题已从由人类提问转向为机器阅读而制造。
英文摘要
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.
CommentsAccepted to the 13th Web as Corpus Workshop (WaC-13) at EMNLP 2026. 14 pages, 4 figures. Code and data: https://github.com/bodhiumlabs/tell-whos-asking
