SlopShape: Identifying AI-Generated Commercial Web Content
cs.CL
Submitted: 2026-09-14
Updated: 2026-09-28
Comments: 20 pages, 5 figures. Verification artifacts and code: https://github.com/pulse-energy-eu/slopshape
Code: https://github.com/pulse-energy-eu/slopshape
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- GEO: Generative Engine Optimization
- The Rise of AI-Generated Content in Wikipedia
- Can AI writing be salvaged? Mitigating Idiosyncrasies and Improving Human-AI Alignment in the Writing Process through Edits
- Generative Engine Optimization: How to Dominate AI Search
- RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors
- NarraBench: A Comprehensive Framework for Narrative Benchmarking
- Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text
- Impact of AI Search Summaries on Website Traffic: Evidence from Google AI Overviews and Wikipedia
- Delving into LLM-assisted writing in biomedical publications through excess vocabulary
- Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
- The Widespread Adoption of Large Language Model-Assisted Writing Across Society
- DAMAGE: Detecting Adversarially Modified AI Generated Text
- DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
- GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education
- AI use in American newspapers is widespread, uneven, and rarely disclosed
- StoryScope: Investigating idiosyncrasies in AI fiction
- Can AI-Generated Text be Reliably Detected?
- Measuring AI "Slop" in Text
- A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism
- Diagnosing and Repairing Citation Failures in Generative Engine Optimization
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