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Sprint projectSep 14, 2026Saudi Arabia

Whose Page Did You Count? Counting-Dependence and a Null Result in Measuring Attention to an AI Incident

Fatimah Emad Eldin · Team Baseline Drift

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Whose Page Did You Count? Counting-Dependence and a Null Result in Measuring Attention to an AI Incident

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How much attention did an AI incident get? The answer depends on a choice nobody states: which page you count. Re-measuring the July 2026 OpenAI/Hugging Face intrusion, the published finding that June’s export-control ban drew roughly seven times more attention inverts once the page that absorbed the attention is counted instead of the developer’s: the breached party’s page outdrew every June comparator tested, in both raw and baseline- normalised units. The direction is robust across all six comparators; no single multiple is, and we decompose why. Second, a season-matched placebo null puts the 30-day response of AI-risk concept pages at the 53.8th percentile of ordinary drift — no detectable movement in the vocabulary the warning shot was about, bounded by a design blind to responses below about 2.2× drift. Third, a cross-channel decay comparison that looks headline-grade does not survive its null: the incident’s discussion thread sits at the 33rd percentile of thirty comparable front-page threads, so that half-life measures the platform and not the event. Code, cached fixtures and a verify.py that recomputes every number accompany the report.

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  1. The paper convinces its readers that there was no significant media coverage of the OpenAI Hugging Face incident. It is transparent and provides what the author's assumed happended. The core limitation is that everything here is n=1: the practical recommendations are hypotheses drawn from a single event, and the paper is explicit that they should be read that way.

  2. I appreciated the author's critical look at how warning shots were covered, but the paper reads very LLM-heavy. I would recommend the author take a bit of time to put this analysis into their own words. Overall, this is an interesting way to measure impact, and I'd like to see it applied to more incidents.

Cite this project

@misc{eldin2026whose,
  title = {{Whose Page Did You Count? Counting-Dependence and a Null Result in Measuring Attention to an AI Incident}},
  author = {Fatimah Emad Eldin},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/whose-page-did-you-count-countingdependence-and-a-null-result-in-measuring-attention-to-an-ai-incident-xyi6}},
  url = {https://apartresearch.com/sprints/projects/whose-page-did-you-count-countingdependence-and-a-null-result-in-measuring-attention-to-an-ai-incident-xyi6}
}

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