The premise is elegant and the execution is unusually honest for this kind of investigation. A knife collector who runs New Knife Day, a scraper-powered site tracking Reddit knife discussions, fine-tuned a named-entity recognition model (GLiNER) to extract brands, models, and steels from comments across six knife subreddits. With 51,129 comments tagged by author, brand, and thread type, he asked a narrow question: do a small number of brand-heavy accounts write a disproportionate share of buying advice? The answer is yes, but not overwhelmingly. The top 5% of accounts by brand-heaviness — 49 accounts out of 987 with 10-plus comments — wrote 11.3% of brand mentions in buying threads, where a permutation test (1,000 random author-reassignments) predicted 7.9%, with a 95th-percentile ceiling of 10.1%. Only 2 of 1,000 shuffles matched the observed concentration. That is statistically significant. In raw terms, it means roughly 50 extra brand recommendations out of 1,471 come from these accounts. The geographic distribution of the signal matters more than the headline number. Two subreddits — r/chefknives and r/knifeclub — sit clearly above chance. r/knives, the largest community, is within half a point of the null expectation. Three coded brands show heavy tail-account concentration: Brand B003 (a chef's-knife brand) gets 31.2% of its buying-thread mentions from the tail where chance predicts 8%. Brand B004 sits at 26.1% versus 8.2%. This is the shape a few targeted campaigns would leave — and also the shape a few loud fan bases would leave. Here is where the investigation gets genuinely interesting and frustrating in equal measure. The author fetched full Reddit histories for 23 tail accounts behind the three flagged brands and 23 comparison accounts drawn from the same comment-count range. No feature separated the groups at p < 0.05 on a Mann-Whitney test. Tail accounts were the same age (median 4.5 years), posted in more subreddits (66 vs 47), spent less time in knife subs (3.2% vs 10.3%), and carried almost no store links (0.3%). If these are shill accounts, they are indistinguishable from normal Reddit users on every observable dimension. The methodological candor throughout is striking. The author documents that the analysis initially found nothing because the scraper captured comments too early; a refresh pass that re-fetched 3,607 posts older than 48 hours nearly tripled the corpus. The brands are coded precisely because concentration statistics are not evidence of payment. The permutation test is transparent and reproducible. The author explicitly states that public Reddit data can show concentration but cannot show causation. What this actually demonstrates is the fundamental identifiability problem with astroturfing detection. Commercial services like REDCmts ($9.99 per comment, $699.99 per hundred) and Soar sell aged, warmed accounts specifically designed to be indistinguishable from organic users. The product these vendors are selling is statistical invisibility. If the accounts are doing their job, the full-history analysis should find nothing — and it does. This means the concentration signal in buying threads is the only anomaly, and it is exactly the anomaly that genuine enthusiasm also produces. The practical takeaway for anyone who appends 'reddit' to their Google searches: the trust model is structurally vulnerable. A handful of accounts writing one in nine buying recommendations is not catastrophic, but it means the crowd-sourced wisdom Reddit users rely on has a soft underbelly. The author's NER-plus-permutation-test approach is a template others could apply to any product subreddit, and the fact that a single person with a fine-tuned model and a scraper can surface this signal suggests platforms could detect it at scale if they chose to.
