Pangram, the Reddit Bot Detector repo, and the moderator threads in r/AskReddit all publish the same signals. Read them as a checklist of what NOT to do, and you have the brief for a resident network that mods don't pattern-match.
There are roughly five public sources that publish "how to spot an AI-generated Reddit account" guides. Pangram Labs has the most cited one. The open-source Reddit Bot Detector project on GitHub adds a couple more signals. Moderator AMAs in r/ModSupport name behavioral patterns that get accounts banned. A handful of academic papers have analysed the same signals.
If you read all of them as a single document, you have the operating playbook for a resident network that doesn't get caught.
The signals fall into four buckets. We'll walk each one and how a credible residents operation works around it. This is meant as transparency about how this work is actually done, not as a how-to for getting accounts banned.
The public bot-detection literature — Pangram, the Reddit Bot Detector repo, moderator AMAs in r/ModSupport — names four buckets of signals that flag AI-generated accounts: linguistic tells, formatting tells, behavioural tells, network tells. Read inverted, they become the operating brief for a resident network that mods don't pattern-match. The work is editorial discipline, not better models: strip AI-tic vocabulary, force sentence-length variance, age accounts properly, scatter activity across subs, human-review every comment before publish.
The rest of this piece walks each bucket and how a credible residents operation works around it.
These are the patterns that any halfway-competent AI classifier picks up first.
The AI-tic vocabulary. Words that LLMs over-use because their training data taught them to sound smart: delve, tapestry, nuance, landscape, realm, multifaceted, pivotal, garner, bolster, commendable. Use any of these in a casual Reddit comment and a human reader feels something off before they can articulate it.
Transition phrases that no human writes. "It's important to remember that...", "In conclusion...", "Furthermore, one might consider...", "Ultimately, it boils down to...". These are essay-grader phrases. Nobody writes like this in a comment thread.
Uniform sentence length. A real comment swings: 4 words, then 22, then 9. AI-generated text tends to settle at 15-25 words per sentence consistently. The variance is a stronger signal than the average.
Overuse of em-dashes and the rule of three. AI loves listing three things separated by em-dashes. Real human writing on Reddit uses em-dashes sparingly, and the "three things" pattern shows up much less often.
How a residents operation works around it:
This bucket is where most off-the-shelf "AI commenting" tools get caught immediately.
Headers inside a Reddit comment. Nobody writes a Reddit comment with H2 markdown headers. AI tools produce them by default because the underlying model was trained on structured documents.
Bulleted lists in casual replies. A two-sentence answer to a casual question doesn't need three bullet points. AI tools default to bullets because bullets feel "structured." Real Reddit comments are flowing text 90% of the time.
Perfectly structured advice posts where nobody asked for advice. A LinkedIn-style "Here are 5 things to consider..." reply to "anyone know if X exchange is good?" is the canonical bot tell.
Paragraph breaks every 2 sentences. Real Reddit comments alternate between dense paragraphs and short ones. Bot-generated content tends toward uniform spacing.
How a residents operation works around it:
This is where shallow operations get caught even if the writing is good.
Young account, high posting frequency. An account created last quarter that posts twice a day across three subs is the textbook signal. Real accounts have years of low-volume background activity before any "expert" period.
Low karma in the sub where the account suddenly becomes active. If the account has 5,000 lifetime karma but only 30 of it in r/CryptoCurrency, and now it's posting daily expert advice there, mods notice.
Cross-sub footprint that doesn't make sense. Same account giving "expert" answers in r/SEO, r/marketing, r/startups and r/CryptoCurrency within 24 hours. Real expertise tends to cluster in one or two adjacent communities.
Posting times that look automated. Comments dropping every 47 minutes on the dot, or only during specific 4-hour windows that don't match any plausible timezone.
How a residents operation works around it:
This is the deepest bucket and what separates credible operations from the rest.
High inter-post similarity within an account. Same account answering different questions with semantically near-identical sentences. Modern classifiers flag this within a few months of activity.
High inter-account similarity within a campaign. Two accounts on different subs both using the phrase "the only one I trust for fast USDT pulls" within a week is the smoking gun.
Top-of-thread replies vs threading into conversation. Bots default to top-level replies. Humans get into back-and-forth: replying, getting replied to, replying again.
Vote patterns that correlate too tightly. If an account posts and then five other accounts upvote within 90 seconds, the engagement looks coordinated to mod tools.
Comments that don't engage with the prior comment. A bot tends to answer the post, not the comment they're replying to. Real humans reply to the specific thing the previous person said.
How a residents operation works around it:
In May 2026 a Reddit user posted a now-circulating thread in r/SEO breaking down the profile of a single suspected GEO bot. The case is worth walking through because the account hits every bucket above at once.
Visible signals from the account page alone:
This account hits every bucket of the public detection checklist. Linguistic (essay-grader transitions, AI-tic phrasing). Formatting (long structured posts in reply slots). Metadata (1y account, 2 karma, 12 active subs). Behavioural (mod-removal pattern, expert-mode cross-posting).
The lesson isn't "this specific account got caught." The lesson is that the inverse of every flag above is the minimum bar for a residents operation. If your residents would show up to a human moderator looking like u/Personal-Method3958 after one year, the operation isn't ready to run.
The honest reason these checklists are public is that the platforms benefit when bad operations get caught. Reddit's mod tooling, Pangram's commercial product, and the open-source detectors all share an interest in making low-effort AI shilling unprofitable.
That's fine for our category. Low-effort AI shilling burns subs, gets accounts banned, and makes communities hostile to anything that smells like marketing. Operations that follow the inversion of these checklists do the opposite, they participate in subs in ways that match how real members participate, and the brand mention is incidental to that participation.
If your residents operation can't pass every bucket above, it shouldn't run. The asset you create is only worth as much as it doesn't get pattern-matched and removed. Every campaign that gets a thread banned costs more than ten campaigns that don't.
A short read on where the detection arms race is heading.
Multi-modal classifiers. Current detection looks at text only. The next generation will correlate text with posting history, posting times, vote patterns and cross-sub footprint as a single multi-modal score. The text-only signals stop being sufficient.
Stylometric fingerprinting per account. Each account will be expected to have a consistent linguistic fingerprint across months of activity. Operations that swap out writers without preserving the voice will get caught.
Community-driven moderation. The most effective detection in 2026 isn't algorithmic, it's mods who've been in the sub for years pattern-matching things that feel off. Operations that don't earn community trust first will lose to operations that do.
The inversion of the public checklist is still the foundation. But the foundation will only get harder to fake.
Are AI-generated Reddit comments illegal? No. They're against most subs' policies if undisclosed, and they get accounts banned, but they're not illegal. The risk is policy-level, not legal.
Can mods detect AI comments reliably? Increasingly yes. Combined-signal detection (text + metadata + behavior) catches most low-effort AI shilling. High-effort operations that follow the public-checklist inversion are still harder to detect by anyone.
What is Pangram? Pangram Labs is a commercial AI-content detection company. Their Reddit detector blog post is one of the most-cited public sources on the linguistic and behavioral signals of AI-generated comments.
What's the difference between a bot and a managed account? A bot posts automated content with no human review. A managed account is a real-aged Reddit account that posts content reviewed and approved by a human editor for fit. The behavioural signature is very different.
Can I just write Reddit comments with ChatGPT? Out of the box, no, the AI-tic vocabulary and formatting tells will get the comment flagged or downvoted by humans even before a classifier touches it. With editorial pass and per-account voice maintenance, the gap closes.
How do you avoid the "delve" problem? We maintain a blocklist of ~60 AI-tic words and phrases that the editorial layer flags before publish. The harder fix is the cadence and uniformity signals, which require per-account voice profiling.
Related reading: Reddit owns Google for crypto queries · How to get cited by Perplexity and AI Overviews
Want to see how your existing Reddit presence (or your competitors') stacks against the public detection checklist? Run an audit. Free, takes 30 minutes, shows you which signals are working in your favour and which are working against you. See our Reddit Resident Network for how we run the inverse of every bucket above.