When does a Reddit thread become evidence in Google’s AI?

Research questions

Visibility in stages

“AI cites Reddit” mixes several different events. We measure four of them separately. Two further stages are named because they matter, but this study does not measure them.

What we measured

We took every link to an individual Reddit thread in five sets of answers.

• Google AI Overviews and AI Mode, 26 September 2026: the 800 US keywords of our AI Overview frequency study (486 AI Overviews) and AI Mode answers for 400 of them, as in version 1.0.
• ChatGPT, Gemini, Perplexity and Claude, 26 September 2026: the 80 buyer questions of our four-assistant study, as in version 1.0.
• Same keywords, two days later, 28 September 2026: 96 of the 800 keywords searched again, each in three wordings: the original keyword, a natural question and a longer rephrasing (from the frequency study’s version 1.1).
• Deep results, 28 September 2026: 576 searches with Google’s organic top 100 and the AI Overview on the same page (from our AI citations vs Google rankings study); 505 showed an AI Overview.
• Thread text: title, post, votes, date and full comment tree of all 1,855 threads involved, from the public Arctic Shift archive of Reddit. Reddit blocks automated access to its own pages, which is why version 1.0 could not measure these.

Every search stays in the results: an answer that cites no Reddit thread counts as an outcome, not a gap. Keywords that share a seed topic are not independent, so intervals for Google rates come from resampling the seed topics. Counts from small groups carry Wilson intervals.

Findings

How often each AI cites Reddit

The assistants answered 80 buyer questions; Google’s figures cover 800 searches, so the two sets differ. In our study of “Is this brand legit?” questions, ChatGPT did cite Reddit, in 11.4% of answers: whether an assistant turns to Reddit depends on the question.

Industry, not information need, carries the difference

Two model coders sorted every keyword by the need behind it (choosing between options, cost, how-to, facts, a local provider, a brand). They agreed on 84.6% (Cohen’s kappa 0.8). AI Overviews for “choice” searches cited Reddit most often (28.9%), cost searches least (8.9%). Once industry is held fixed, though, the information need adds nothing we can detect (joint test p = 0.61). 77.1% of the software AI Overviews were choice searches, against 11.4% in financial services, so the two cannot be separated in this sample. Keyword intent labels (commercial, informational, transactional, navigational) showed no clear difference either: 16.4% to 20.5%.

Stage 1 to 2: Google shows a thread, the AI cites it

Of the 243 Reddit threads shown on those results pages, the AI Overview cited 16.9%. Position mattered: 27.3% of threads ranking 1 to 3 were cited, 14.9% at 4 to 10 and 1.8% of threads shown only in the Discussions and forums box. AI Mode cited 5.6% of the shown threads.

Yet most citations did not come from the page. 53.9% of Reddit threads cited in AI Overviews and 79.1% of those cited in AI Mode appeared in neither the organic top 10 nor the forums box for the same search.

How deep does the AI reach?

On 505 searches with an AI Overview where we recorded Google’s organic top 100, Google ranked 1,142 Reddit threads. The AI Overview cited 5.9% of those at positions 1 to 3, 0.5% at 4 to 10 and none of the 798 at positions 11 to 100. Of the 27 Reddit citations on these searches, 9 were to threads ranking 1 to 3, 1 to a thread at 4 to 10 and 17 to threads outside the top 100.

Google’s AI does not work down its own ranking into Reddit. It either takes a thread from the very top, or reaches one that the ranking does not show at all. That points to a separate retrieval step, but this study cannot see that step directly.

What distinguishes a cited thread

We compared cited and uncited threads in two ways.

The main comparison holds the search fixed. It covers every Reddit thread that Google showed or its AI cited for the same search, on the 100 searches that had both cited and uncited threads (301 thread appearances, a conditional logistic model with one stratum per search).

Across all 1,964 appearances, cited threads had a median of 40 comments against 20 for uncited ones. 65.5% of cited threads had question titles, and so did 53.8% of uncited ones. In a joint model with the other features, comments kept their effect (odds ratio 2.99 per SD, 1.94 to 4.6). Nothing about prices, numbers, lists or instructions in the text mattered.

The second comparison matches each of 88 cited threads with up to three threads from the same subreddit whose titles share the search’s words and that no answer in our data cites (264 controls). Here the gaps are much larger: a median of 44.5 comments against 6, and more upvotes, older threads and longer text all predicted citation. These controls, however, are mostly threads Google would not show for the search at all. The larger gap therefore mixes the “shown” stage with the “cited” stage. The title-matching step also makes controls match the search words by design. We report this comparison in the data files but draw conclusions from the same-search comparison.

Version 1.0 noted that a third of cited threads had question titles. That is true, but question titles are nearly as common among the Reddit threads Google showed and the AI skipped, so it is not a signal of citation.

Which communities

Citations and Shown are each type’s share of Google’s Reddit citations and of the Reddit threads on the results pages; the last column is the share of shown threads that the AI Overview cited.

Two model coders classified all 548 subreddits in the data. They agreed on 89.4% (kappa 0.86). Specialist practitioner communities are those where people who do the work answer questions about it, such as plumbers, project managers and security staff. They supply half of Google’s Reddit citations, and a thread from one that Google showed was cited 19.3% of the time, against 7.7% to 10.3% for other types.

That fits the idea that Google’s AI favors communities that act as specialist knowledge bases rather than social media in general. But when the search is held fixed, the specialist effect is not statistically clear (odds ratio 1.83, 95% interval 0.73 to 4.55). Specialist communities cluster in the industries where Reddit is cited most: they supplied 58.7% of the Reddit threads shown in software searches and 50.0% in home services, against 17.2% in financial services and 2.7% in retail. The idea remains a hypothesis this sample cannot confirm.

The most-cited communities in Google’s answers on 26 September were r/askaplumber (14 citations, each on a different search), r/projectmanagers (9) and r/crmsoftware (7). 51.7% of the 58 cited communities were cited once.

What the thread is used for, and whether it supports the sentence

96.9% of Reddit citations in Google’s AI answers were attached to a specific sentence, and 24.8% of those sentences named Reddit in the text (“users on Reddit say…”). For each of the 250 sentences, two model coders read the sentence, Google’s snippet and the archived thread (post and top 10 comments). They then recorded what the thread contributed and whether it supported the sentence.

Overall, 36.4% of sentences were supported, 42.8% partly supported (often one commenter’s view stated as what “users” say) and 20.8% not supported. The coders agreed on 74.0% of support labels (kappa 0.61), and both called a sentence unsupported in 18.8%.

Recommendations were the weakest. Many cite a general “which tool do you use?” thread for a sentence praising a product the post and top comments do not mention. The coders saw only the top 10 comments. A text search of the complete comment trees found the option named in the sentence somewhere in the thread for 77.3% of the 132 sentences that name one. Of the 36 such sentences coded unsupported, 27.8% named an option that appears nowhere in the thread. The support figures describe what a reader of the top of the thread would find, not a final verdict on every comment.

How stable are Reddit citations?

We compared the 96 keywords searched on 26 and 28 September, and on 28 September in three wordings.

Answers are the AI Overviews that cited Reddit in the first search of each pair (the keyword on 26 September, or the plain keyword on 28 September for the two rewordings). The other columns show how many of those citations came back in the second search.

Where both dates showed an AI Overview (50 keywords), they agreed on whether to cite Reddit in 86.0%. The wording mattered more than the date. Question wordings cited Reddit in 35.6% of their AI Overviews and longer wordings in 35.7%, against 21.1% for the plain keywords that day. But they rarely cited the same thread or even the same community. The idea that AI answers are stable at the community level while threads vary is not supported here: both changed with the wording. These comparisons rest on 12 to 16 answers each, so the intervals are wide (for example 38.6% to 81.5% for “still citing Reddit” two days apart).

What this means

These points are our reading of the data rather than measured results.

• Google’s AI treats Reddit as a source of specific evidence, not as “social media” in general. It cites Reddit mostly in home services and software, mostly from communities of practitioners, and mostly for facts, recommendations and experience. Whether the specialist pattern survives with more data is the open question.
• A cited thread is usually one with a real discussion. Among threads Google already surfaces, the ones cited have more comments. Nothing on this page shows that adding comments to a thread would get it cited: that is an observational association, and the causal test has not been run.
• Google’s ranking is a partial guide. A Reddit thread at the top of Google’s results is often cited, one further down almost never, and many cited threads are not in the results at all. Tracking only the Reddit threads Google ranks misses most of what its AI uses.
• Treat any single Reddit citation as a snapshot. Two days or a rewording were enough to change which thread was cited. A monitoring report on Reddit citations needs repeated measurements, not one check.
• A citation is not an endorsement. About one in five sentences citing Reddit was not supported by the top of the thread, most often when the AI turned a general question thread into a product recommendation. The legitimate way for a business to be part of these threads is for its staff to take part honestly, as themselves, in the communities where its buyers ask questions. Posing as customers is not, and Reddit’s rules on impersonation and spam forbid it.

Methodology

• Answers: Google AI Overviews (486, from 800 US keywords) and AI Mode (400) via DataForSEO SERP API, Google.com, United States, English, desktop, 26 September 2026. ChatGPT and Gemini consumer apps via DataForSEO LLM Scraper; Perplexity sonar and Claude Haiku 4.5 via DataForSEO LLM Responses API with web search, 26 September 2026. 96 keywords again in three wordings (288 searches) and 576 searches with the organic top 100, 28 September 2026, same settings. One request per search.
• Reddit citations: links to individual threads (addresses containing /comments/) from every reference, per-paragraph reference and inline link in the answer. The attached sentence is the answer line that carries the link.
• Shown by Google: the thread in the organic results (top 10, or top 100 for the deep set) or the Discussions and forums box of the same results page. AI Mode has no results list of its own, so its “shown” set is the regular results page for the same keyword.
• Thread data: Arctic Shift archive (post by ID, full comment tree), retrieved 28 September 2026; comments and votes are as archived, which may differ from the day of the search. 100 of the 1,855 posts were removed or deleted by the time of archiving.
• Features: comments (log), upvote score (log), age at the search date, title a question, length of post plus top 10 comments, share of the search’s words in the title and in the text, first-person words, price mentions, numbers, instruction verbs, lists.
• Comparisons: conditional logistic regression, one stratum per search (same-search design) or per matched set (same-community design), continuous features standardized, Holm correction across the features tested.
• Model coding: community type (548 subreddits), information need (1,568 keywords), and role and support of each attached sentence (250) were each coded independently by two models, Claude Opus and Claude Sonnet. The first coder’s label is used, and agreement is reported. No human coding was done.
• Intervals: Google rates resample seed topics (2,000 resamples, seed 20260928); other proportions use Wilson 95% intervals.
• Update schedule: quarterly. A week-over-week rerun of the AI Overview sample is planned with our volatility study, and this page will add those figures.

Limitations

• Observational: every result is an association. Threads were not changed to see whether their citation changed, so “more comments are cited” does not mean “adding comments gets a thread cited”.
• Comments and votes come from the archive, not from the day of the search, and threads keep growing, which may inflate differences for older threads.
• Support was judged by models against the post and top 10 comments, not by people and not against the whole thread; the full-thread text check covers only sentences that name an option.
• Stability rests on 96 keywords over two days, with 12 to 16 Reddit-citing answers per comparison.
• The deep top-100 searches are different queries (questions reformulated by AI assistants), not the 800 keywords.
• The assistants answered 80 questions each, so their rates rest on small counts. This study counts Reddit threads; our citation study counted any reddit.com link and reported Reddit in 18.1% of AI Overviews with sources.

Data and downloads

• Every Reddit thread appearance (cited or shown), same-community controls, thread features, community type, information need and the coded role and support: s21_reddit_threads_v11.csv and JSON
• The version 1.0 citation list: s21_reddit_citations.csv
• Every statistic on this page: stats.json
• Machine-readable methodology: methodology.json

The data is free to reuse with attribution (CC BY 4.0).

To cite: Underneath. (2026). When does a Reddit thread become evidence in Google’s AI? (Version 1.1). Underneath Research. https://underneath.agency/research/ai-reddit-citations-study

Frequently asked questions

How often do Google AI Overviews cite Reddit?

In our September 2026 sample of 800 US searches, 17.9% of AI Overviews cited at least one Reddit thread, and more than a third in home services and B2B software. When Google’s own results showed a Reddit thread, the AI Overview cited Reddit in 33.5% of cases.

Does ChatGPT cite Reddit?

For the 80 buyer questions we tested (“best X” and “best X in a city”), ChatGPT cited no Reddit threads. For “Is this brand legit?” questions it cited Reddit in 11.4% of answers.

Are the Reddit threads AI cites the ones that rank on Google?

Sometimes. A Reddit thread in Google’s top three is often cited, one ranked 11 to 100 almost never. 53.9% of the Reddit threads cited in AI Overviews were not on the results page, and on searches where we saw the top 100, 17 of 27 cited threads were outside it.

What makes a Reddit thread more likely to be cited?

Among threads Google already shows for the same search, cited threads had more discussion (a median of 40 comments against 20). Question titles, matching the search’s words and the kind of community did not reliably predict citation once the search was held fixed. This is an association, not a tested cause.

Do Reddit citations in AI answers support what the AI says?

Not always. Judged against the post and top 10 comments, 36.4% of sentences citing Reddit were supported, 42.8% partly and 20.8% not supported. Recommendations of named products were the least often supported.

Which subreddits do AI answers cite most?

In our data, r/askaplumber, r/projectmanagers and r/crmsoftware, followed by r/cybersecurity, r/projectmanagement and r/homeowners. Half of Google’s Reddit citations came from communities of practitioners answering questions about their own work.

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