Training a model to identify AI-generated web content from structure alone
We ask whether AI-generated text can be identified one level deeper, from structural signatures: how information is presented, in what order, with what evidence, and in what voice. We replicate StoryScope (Russell et al., 2026), which showed such patterns for AI-generated fiction, on commercial content: 2,250 pre-ChatGPT human blog posts from 268 company domains against 11,250 AI mirrors from five frontier models. A 214-feature instrument, applied by an LLM and validated in a human gold-annotation session (human-human kappa 0.928, human-model 0.946), detects AI posts from its 187 structural features alone at 98.0 macro-F1 on held-out companies, unchanged (98.1) when every AI post is reworded by its own model.
- ▪We ask whether AI-generated text can be identified one level deeper, from structural signatures: how information is presented, in what order, with what evidence, and in what voice.
- ▪We replicate StoryScope (Russell et al., 2026), which showed such patterns for AI-generated fiction, on commercial content: 2,250 pre-ChatGPT human blog posts from 268 company domains against 11,250 AI mirrors from five frontier models.
- ▪A 214-feature instrument, applied by an LLM and validated in a human gold-annotation session (human-human kappa 0.928, human-model 0.946), detects AI posts from its 187 structural features alone at 98.0 macro-F1 on held-out companies, uncha
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,977 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2609.15369 |
| Publication time | Tue, 22 Sep 2026 13:00:49 +0000 |
| Retrieval time | 2026-09-22T13:13:51.615Z |
| Last seen | 2026-09-22T13:13:51.615Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | NBhaTXFGaEj6 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computation and Language arXiv:2609.15369 (cs) [Submitted on 14 Sep 2026 (v1), last revised 17 Sep 2026 (this version, v2)] Title:SlopShape: Identifying AI-Generated Commercial Web Content Authors:Jochen Madler (Sitefire) View a PDF of the paper titled SlopShape: Identifying AI-Generated Commercial Web Content, by Jochen Madler (Sitefire) View PDF HTML (experimental) Abstract:Word-level detectors identify unedited AI-generated text almost perfectly, but the literature documents their brittleness under rewording, and a word-level score neither characterizes a text nor identifies which AI model wrote it.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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