Thread score vs AI citation: 11.7K cited Reddit pages and the order ledger
The median score was 12 across 11.7K cited Reddit pages. Explore score bands, order overlap, paired score changes, and limits.
Originally published October 7, 2026
The median score was 12 among 11,681 measured cited Reddit pages. Order overlap and score changes are separate observations, not an AI service-effect estimate.
July 9–October 6, 2026 (UTC) · 11,681 scored cited pages; 850 normalized upvote orders; 640 normalized downvote orders
- Median observed score
- 1211,681 measured source pages
- Upvote orders with indexed targets
- 29Of 850 normalized orders
- Median paired score change
- 22.5374 upvote outcome records
The typical scored Reddit page in this cited sample was not viral. The median observed score was 12 across 11,681 measured cited source pages. Separately, 29 of 850 normalized Signals upvote orders had targets found in Parse's citation-source index. Neither result establishes that buying votes changes AI recommendations.
The measurements come from Parse, the AI answer index Dimitry Apollonsky also runs; the analysis and interpretation are Signals'. The aggregate order observations come from Signals' ledger. This is an observational study, not a controlled test of a Signals service.
The citation panel runs July 9 through October 6, 2026; ledger coverage is stated separately below. Signals runs an aged Reddit account marketplace plus an editorial network for AI brand mentions across Reddit, Quora, Product Hunt, and Threads.
Modest scores were common among measured cited pages
The scored sample contains 11,681 Reddit source pages with usable numeric scores in their selected latest snapshots. The median was 12, while the lower and upper reported percentiles were 2 and 98. These are properties of the observed cited-page sample, not thresholds that a thread must reach before an engine can cite it.
The median measured cited-page score was modest
Observed thread score percentiles · score points · July 9–October 6, 2026 (UTC)
View data table
| 10th percentile | 2 |
|---|---|
| Median | 12 |
| 90th percentile | 98 |
11,681 cited source pages with numeric snapshot scores. Source: Signals order ledger and Parse index.
Scores are measured at crawl time, not necessarily citation time; URLs may refer to the same thread.
Scores were captured at crawl time, which can differ from the time of citation. A source page can also represent a URL variant of a discussion found elsewhere in the sample. We therefore label the unit source pages rather than claiming a fully deduplicated count of unique Reddit conversations.
The result is enough to challenge a blanket viral-score requirement. It is not enough to say that modest scores improve citation chances. We did not sample the universe of uncited Reddit threads, so the analysis has no denominator for calculating citation probability across score levels.
The score bands describe the sample rather than a ranking curve
The figure groups measured pages into score bands and reports their counts. It includes 1,106 pages with a score at most one and 1,140 above one hundred. The middle bands carry the rest of the sample. These counts show where the observed pages fall; they do not show the chance that a page in each band gets cited.
Cited pages occurred across score bands
Scored cited pages · pages · July 9–October 6, 2026 (UTC)
View data table
| Score at most 1 | 1,106 |
|---|---|
| Score 2 to 10 | 4,185 |
| Score 11 to 100 | 5,250 |
| Score over 100 | 1,140 |
11,681 source pages. Source: Signals order ledger and Parse index.
No uncited-thread denominator is available, so these are counts rather than citation probabilities.
That probability question would require both cited and uncited pages in each band, measured at comparable times. Thread age, subreddit, topic, moderation state, and page content would also need attention. A large number of cited pages in a band could simply reflect a large underlying population of pages with similar scores.
The operator takeaway is to inspect relevant low-score discussions rather than reject them automatically. The content still needs to answer the buying question and identify the product accurately. A score band cannot perform that relevance check, and this study does not convert one into a suggested vote-order quantity.
The ledger overlap is a separate measurement
We normalized eligible vote-order URLs to Reddit thread identifiers and matched them against Parse's source index. The join found 29 indexed targets among 850 normalized upvote orders and 1 among 640 normalized downvote orders. Repeated orders remain separate orders, with distinct-target counts preserved in the raw output and figure context.
Ordered targets had limited overlap with the observed index
Normalized orders with a target citation match · orders · Orders: December 2025–October 6, 2026; current-engine observations: May 24–October 6, 2026
| Upvote | 850 | 29 | 21 | 1 | 19 |
|---|---|---|---|---|---|
| Downvote | 640 | 1 | 0 | 0 | 0 |
Upvote 850 orders/705 threads; downvote 640 orders/474 threads. Source: Signals order ledger and Parse index.
Before and after can overlap. Earlier orders lack complete pre-period history. Missing placement timestamps are not zero baselines.
The ledger extends from December 2025 through October 6, 2026. “Ever indexed” means the target appeared in Parse's citation-source records before the cutoff; it does not imply complete observation of all AI engines or all user questions. Current-engine citation history is evaluated separately from May 24, 2026 onward.
Customer names and individual target URLs are not published. The reproducible receipt explains the normalized join and preserves aggregate results. Targets with malformed or unrecognized URLs are excluded from the normalized cohort instead of being counted as uncited. The limited overlap is part of the result, not a reason to substitute a different outcome.
Before and after require a placement timestamp
The before/after columns compare observed current-engine citation dates with the order's vendor-placement time. An order can have citations on both sides, so the columns are not mutually exclusive categories. Orders without a placement timestamp cannot support that comparison, and earlier orders may lack a fully observed pre-order period in the current-engine panel.
The observation window for this check begins May 24, 2026 and ends before October 7. It is not a uniform follow-up window for every order. A December order and a late-September order have very different timing relationships to that panel. Missing prior history therefore must not be presented as proof that the thread had never been cited.
The join reports chronology, not a treatment effect. We did not construct matched untreated threads or adjust for concurrent changes to the discussion. Even an observed after-only citation can have explanations other than the order. The table should be read as an audit of overlap and coverage.
Paired scores changed for a subset of orders
Among ledger orders with both starting and ending scores, the upvote group's median change was 22.5 points and the downvote group's was -3 points. The figure reports these as score differences, with their paired-measurement denominators. They are not verified counts of incremental votes caused by the service and do not measure AI naming.
Recorded scores changed in the paired ledger subset
Ending score minus starting score · score points · Ledger orders before October 7, 2026
| Upvote | 22.5 | 67.51 |
|---|---|---|
| Downvote | -3 | -0.86 |
Upvote: 374 paired scores; downvote: 418 paired scores. Source: Signals order ledger and Parse index.
This subset differs from the normalized-target cohort. Organic votes and other changes are not controlled.
The recorded change can include organic activity, moderation, measurement timing, and other behavior around the target. Orders without both scores are absent from this calculation. That makes the paired-score subset different from the normalized citation-overlap cohort, even though both originate in the ledger.
A useful campaign report keeps those fields separate: requested work, reported delivery, observed score, citation presence, and brand naming. A score change can be real while no citation is observed in the selected panel. Reporting the first does not fill in the second, and the absence of the second does not erase the first.
Comment engagement is not the same as thread score
A thread's score and the engagement on brand-matching comments are different measurements. The accompanying comments study found 79.4% comments-only page-brand pairs in its measured snapshot set. That result is about where the brand name appeared, not the score of the exact passage an engine used or a threshold for being read.
The comment engagement field aggregates matching comments, so a sum can combine several contributions. Treating it as one comment's karma would overstate the granularity of the data. Likewise, a thread-level citation does not reveal which comment, if any, supplied the brand name in the corresponding answer.
The practical review should therefore read the relevant text. Determine whether it answers the buyer question, supports its claim, and discloses the contributor's relationship when appropriate. The score distribution adds context to that review, but it cannot replace it or establish that manipulating engagement changes the engine's selection.
The source sample is selected and retrospective
Only cited pages with usable latest-snapshot score metadata enter the score distribution. Unmeasured pages may differ, and the selected snapshot can postdate an answer in the citation window. The score therefore describes a later observation of a cited source, not necessarily the value visible when the engine selected that page.
The analysis also retains source-page URLs as the unit for the score figures. The ledger join, by contrast, collapses URL variants to the thread identifier extracted from the comments path. Those choices fit their respective questions, but their counts should not be combined as if they described one common population of unique threads.
A stronger score-effect design would measure comparable threads before the citation outcome and include uncited controls. It would also preserve thread age and observation timing. Without that design, the honest claim is that modest scores occur in this cited sample, not that a particular score causes or prevents a citation.
The counting checks keep the ledger and index separate
We reconciled the scored-page count with the sum of the score-band counts. The ledger overlap was independently recomputed with an existence-based match against the normalized source targets. The receipts preserve both formulations, the score-change aggregates, and the exact observation rules, without committing private customer identifiers or order-target URLs into the public article.
The before/after overlap uses actual observed citation dates, not source creation dates as a proxy for every event. The ever-indexed check is separately labeled because it answers a broader presence question. Missing placement times and unrecognized URLs remain coverage limitations rather than being assigned fabricated event dates.
These checks establish internal consistency, not a causal result. Both formulations rely on the same underlying order and source records. The upvote guide explains how to use the evidence when reviewing a proposal without presenting a delivery receipt or a visible score change as an AI recommendation outcome.
The useful next test needs a stable comparison
The current evidence supports keeping thread relevance, score, and AI outcomes separate. A future effect study would need a stable question panel, a reliable placement time, comparable untreated targets, and enough follow-up. It would also need to account for changes in the thread and brand that occur alongside the order rather than attributing all subsequent movement to it.
Start with the upvote guide for the specification and reporting fields. The other Signals research studies address content and source composition. They can help choose what to inspect, but they do not supply missing treatment controls for the score question.
The result should remain publishable even when overlap is small. A limited or absent observed citation is information about coverage and outcome, not a failure of the research. The vendor and buyer both benefit from a report that preserves it instead of replacing the AI objective with whichever delivery metric looks strongest.
Questions about scores and citations
The main distinction is between a description of cited pages and an estimate of what changes citation chances. This study supplies the former and a separate ledger audit. Keeping that boundary visible makes the data useful without converting observed scores, incomplete before/after coverage, or repeated orders into a causal model they cannot support.
Is the median score an ideal target?
No. It describes selected cited source pages at crawl time. The study does not establish an optimal score or vote quantity.
Does an after-order citation prove the order worked?
No. The timing is observational, follow-up differs between orders, and there is no matched untreated control in this analysis.
Are no observed citations a measured zero effect?
They are an observed absence in the stated panel, not an estimate of a causal effect across all AI search.
How we measured
Read-only production queries frozen at October 7, 2026. Answers are ChatGPT Search and Google AI Mode in the stated UTC window, excluding gpt-5-mini and gpt-5-3-mini records while retaining blank model labels. Citation IDs are expanded from eligible answers. Parse and Soar source domains and subdomains are excluded. Latest content snapshots are selected strictly before October 7. SQL, raw output, and number trace are committed under docs/research-receipts for this slug. Score pages require numeric score metadata. The ledger is read separately; URL paths are normalized to /comments/\<thread-id>/ with case normalization, ignoring query strings, suffixes and host variants. Only aggregate results are published. Ever indexed uses source first-seen before cutoff; before/after uses current-engine citation dates around vendor placement. Score changes use paired outcome scores.
What this cannot tell us
- No uncited Reddit control group or matched untreated orders; no causal service effect is estimated.
- Crawl-time scores can postdate citations; score figures count source URLs rather than deduplicated threads.
- Orders repeat threads, some URLs fail normalization, some timestamps are missing, and pre-period coverage is incomplete.
- The tracked question panel is not a random sample of all AI search.
- This observational analysis does not establish a causal effect of a Signals service.
