How to tell if a Reddit post is AI: which signs really got more common

How can you tell if a Reddit post is AI?

This data can't tell you whether any single post is AI. It shows how often popular signs appear. Of the 26 I measured, only the em dash is clearly more common in January to September 2026 than in the same months of 2019 to 2022.

If you've ever wondered how to tell if a Reddit post is AI, you've probably seen the list. The word "delve". The word "tapestry". "It's not just X, it's Y." Headings, bold labels and emoji bullet points. And the em dash, always the em dash.

These are signs I keep seeing named in guides and threads about AI writing. I've counted how often Reddit comments say AI slop. Do they really show up more in Reddit posts now than before ChatGPT?

So I took 5,253,515 text posts from 100 large subreddits. Those are the text posts with a real body of text that were still visible in the data, leaving out known bots and posts by accounts that had been deleted, from the first nine months of each year before ChatGPT came out, and from the same months of 2026. An estimated quarter (25.0%) of the posts the older years would hold if captured as in 2026 are missing, and "What this data can't tell you" explains why. For each sign I asked one narrow question. How much more or less common is it in 2026 than before?

There's a catch, though. Language drifts. Any word can get more or less common over a few years, as the 40 random words show, and the mix of subreddits shifts too. So I didn't just ask whether a sign went up. I also picked 40 words of moderate frequency at random, like "barber", "ibuprofen" and "parlor", and measured how far they moved. A sign only counts as rising if it clearly moved further than those words. Think of them as the background noise of a language that keeps changing. I set this rule after seeing the first results. Under the band the plan fixed, built from three control words and wide (0.9× to 1.7×), "in conclusion" would have counted as a real fall. I added a narrower within-subreddit band built from three control words (about 1.0× to 1.1×) while analysing. With it 7 other signs would have counted as rising and 5 as falling.

Here's every sign, with the shaded range that the random words covered:

The em dash is the only sign whose whole interval sits to the right of the shaded band. Two other dots lie above the top of the band, the emoji list (3.0×) and "in today's fast-paced/digital/world" (2.3×), but their intervals reach into it. Every other sign has its interval reaching into the band; "realm" sits just below it as a dot. That doesn't mean those signs never changed. It means I can't tell their change apart from the ordinary drift of words you'd never connect to AI.

Try itCheck how often a word or phrase shows up on Reddit, year by year: Historical search

Is the em dash a sign of AI on Reddit?

It's the only sign here that clearly grew. In January to September 2026, 5.12% of text posts of 200+ characters in 100 large subreddits had an em dash in the body, up from 0.69% in the same months of 2019 to 2022, about 7 times the old rate. That still doesn't make any one post with an em dash AI.

I tried to break this result, and it held each time:

• Comparing posts within each subreddit, so a shift in which subreddits post more can't explain it: 5.4×.
• Comparing the same summer months of 2023 and 2026, when posts were probably captured in a similar way (I assume the way posts are captured changed in mid-2023 and did not check month by month): 6.1×.
• Leaving out r/MakeNewFriendsHere, which makes up a much bigger share of the posts in 2026: 7.1×.
• Assuming the posts missing from the older years used em dashes three times as often as the rest: 4.9×.

But look at when it happened:

Not much happened when ChatGPT launched: the em dash rate in January to September 2023 was only 1.1× the old one, and the interval includes no change at all. Then it climbed, with jumps in August 2024 and April 2025. So if you're thinking "ChatGPT came out, em dashes went up", the timing doesn't back you up. The em dash share of text posts rose long after ChatGPT launched; this data can't say what drove it.

I've written about the em dash on Reddit on its own, with comments too. The title-or-body version matches that study almost exactly (5.49% and 5.48% for the first quarter of 2026); the headline numbers here count the body only.

Does it depend on how long the post is? Here's the same ratio by length:

Short posts came in at 9.1× and long ones at 4.1×. That looks like a big gap. But the two intervals overlap a little, so I can't say the short ones really changed more.

Which AI phrases and words got more common on Reddit?

None cleared the bar in the main comparison. "In today's fast-paced/digital/world" came out at 2.3×, and "it's not just X, it's Y" at 1.8×. But random words got that high too, so I can't separate those from drift.

Before I ran the numbers, I wrote down that "delve", "tapestry" and "testament to" would be at least twice as common in 2026. "Delve" (1.0×) and "testament to" (1.1×) weren't. "Tapestry" came out at 1.9×, but with about a hundred posts per window the interval can't settle it.

The famous words tell a different story if you go year by year. In 2024, "delve" was above its old rate (2.3×), but I did not test that against random-word drift in 2024. "Tapestry" was above its old rate the year before (4.9×), and I did not test that either. Then "delve" came back to its old rate:

By this year "delve" was back to 1.0× in the main comparison (0.7× within each subreddit, 0.4× in the summer version), so roughly where it started or lower. "Tapestry" is harder to call. It's rarer than you might think: only 108 posts used it in the four baseline periods against 103 in January to September 2026. With counts that small, the year-by-year intervals overlap. The summer-to-summer version (0.3×) points down but sits inside the drift band, so I can't call it a real fall.

How high "delve" went depends partly on one subreddit, as the 1.8× without r/MakeNewFriendsHere shows. "Let's dive in" and "let's break it down" were already 5.7× their old rate in 2023 and 7.4× in 2024 (the intervals overlap), 5.0× in 2025 and 1.6× in 2026.

Month by month, you can see the bump in "delve" start in the spring of 2023 and fade through 2025:

"It's not just X, it's Y" moved the other way, edging up through 2025. October 2025 looks odd here too, as in my em dash study. Even so, its ratio this year is inside the random-word range. In the version of this pattern that also accepts a dash, the within-subreddit ratio (2.1×) goes beyond the random-word range; see Method. And I only counted posts that fit a pattern. I didn't check how many matches of the pattern are the construction itself, so if you've spotted the real thing in the wild, you've seen something this count can't confirm.

Which supposed AI tells did not clearly change?

In posts, "hope this helps" stayed about where it was: 0.07% of posts before ChatGPT and 0.06% in 2026. "Great question" is too rare to say much, with 77 posts in 2019 to 2022 and 53 in 2026, and the versions of the comparison disagree (0.6× to 7.6×); the 7.6× in the summer version rests on very few posts. Both are mostly things people say in replies (my assumption), and comments weren't measured.

The transition words didn't rise either. "Moreover", "furthermore", "additionally" and "it's worth noting" all came in at or below their old rates. "In conclusion" showed up about half as often in 2026 (0.5×).

That looks like a real drop, but random words fell that far too. The lowest of the 40 came in at 0.4×. So I'd rather not read anything into it. Here are the transition words year by year:

For scale, the plain word "because" sat at about 23% of posts, before and after. Next to that, every one of these phrases is rare. You could read a thousand posts and meet "in conclusion" once or not at all.

Here's every sign in four versions I ran (ratios.csv has the main and within-subreddit versions; the other columns of the table are in drift_check.csv):

Did headings, bold text and emoji lists get more common?

Headings and bold labels in lists jumped from the second quarter of 2024 and headings kept rising until the first quarter of 2025, partly overlapping with a zero-width-space code vanishing from posts. When the code returned in 2026, headings did not fall. That code may be an editor leftover, but I couldn't confirm it, so I don't count them as signs that rose.

Here's the jump, quarter by quarter:

And here's the zero-width-space code over the same quarters:

The code fell from 5.4% of posts to 0.06% in two quarters. It came back this year with a spike I can't explain, so I'm leaving that as an open question.

I'd also guessed, before looking, that bold text and bold labels would grow more than any of the words. They didn't, at least not clearly. Emoji lists were already on the rise before ChatGPT came out: 0.05% of posts in 2019 and 0.29% in 2022, and the intervals don't overlap, so their ratio says little about AI. Bold text went from 3.45% to 4.27% of posts, but that is inside the range of random words, so I can't tell it from drift. This data can't say whether it's a giveaway.

How many Reddit posts have any of the 20 word, phrase and formatting signs?

Very few. In January to September 2026, 94.6% of Reddit text posts in 100 large subreddits contained none of 20 signs (10 words, 6 phrases and 4 formatting habits; the em dash and the 5 transition words not counted). In January to September 2019 to 2022 it was 95.6%. The em dash alone is in 5.12%.

So whatever these signs mean, you'll only meet them in a small slice of posts. I also don't add them up into a score. Bold labels in lists are also bold text, so one formatting habit can count twice, and I don't report the share of posts with two or more signs.

What this data can't tell you

• One post. I can't tell you whether any single post is AI. I counted how often each sign appears, and that says nothing about who wrote the post you're looking at.
• Removed posts. Posts removed by moderators or deleted by their authors have their text erased, so every number I give covers only posts that stayed up.
• Missing more often. The share of posts whose author account was deleted is 27.3% in January to September 2019, 31.5% in 2022 and 1.67% in 2026, so I think older posts were captured later than this year's; I couldn't measure the delay for posts directly. An estimated quarter (25.0%) of the posts the baseline would hold if it had been captured as in 2026 are missing from the data. If those posts carry signs more often than the rest, every ratio here is pushed up. The em dash would still come out at 4.9× if they used it three times as often.
• 100 subreddits. These are large English-language subreddits picked by their activity in October 2025. That's not all of Reddit and not a random draw, and the panel may favor subreddits that grew recently (I didn't check this).
• One friend-finding subreddit. r/MakeNewFriendsHere went from 3.55% of the baseline sample to 19.2% of the sample in 2026, about a fifth. That shifts every pooled ratio, so I also checked within each subreddit and without it. You'll find both in the table above.
• Cause. A rise after ChatGPT matches it in time but doesn't prove it caused the rise. One possible reason, not measured: people who read a lot of AI text may pick up its words. The em dash rose in steps in mid-2024 and in the spring of 2025, long after ChatGPT arrived.
• The patterns. They also match human uses of the same words (for example a "realm" in a fantasy setting; I did not measure how often) and miss variants such as "not only X but also Y". For "it's not just X, it's Y", I didn't check how many matches of the pattern are the construction itself.
• Formatting. The jump in headings and bold labels fits an editor change, but that isn't confirmed. The zero-width-space code may be an editor leftover, but I couldn't confirm it, headings did not fall when it came back in 2026, and its June 2026 spike is unexplained.
• Random words. The drift band comes from 40 words rarer than the em dash and bold text, so a very common word could drift differently.
• All of Reddit. In the all-Reddit check from the search index, bots are not filtered out and posts of every type are counted, so I use it only to check direction.

Method

Planned before looking at results: I compared text posts (at least 200 characters of body text) in 100 large English-language subreddits, the same panel as my studies of the em dash and of "AI slop" comments. The baseline is January to September of each year from 2019 through 2022 (3,479,134 posts from 94 subreddits). The comparison is January to September 2026 (1,774,381 posts from 95 subreddits). The full monthly series runs January 2019 to September 2026, 93 months and 13,219,288 posts. Removed and deleted posts, posts by accounts that had been deleted, known bots, pinned posts and moderator posts are left out. Posts count only if they were still visible in the data. Words and phrases are counted in the title and body, formatting in the body only.

The 26 signs were fixed in advance: 10 words, 6 phrases, 5 formatting habits and 5 transition words. Several of the 10 words come from research published in 2024 on excess vocabulary in text written with language models (Kobak and others; Juzek and Ward) and from Wikipedia's guide to signs of AI writing; the rest are words people commonly name as tells. The phrases, formatting habits and transition words are ones people commonly call tells, several of them also in Wikipedia's guide. Words and phrases are matched case-insensitively as whole words; the em dash and formatting habits are matched as characters and line patterns. The pattern "in today's fast-paced/digital/world" means "in today's fast-paced", "in today's digital" or "in today's world". The "it's not just X, it's Y" pattern also accepts "it is", "this is" and "that's", "only", "merely" or "about" in place of "just", "but" after the break, and "isn't just X, it's Y"; the break is a comma, semicolon or colon. For each sign I counted the share of posts with it at least once, with a 95% Wilson interval, and divided the share in 2026 by the share in the baseline. Intervals for that ratio come from a bootstrap that resamples whole subreddits.

The plan also fixed control words ("because", "yesterday", "friend"). Their spread was to serve as the noise band: 0.9× to 1.7×. It also fixed alternative versions: within each subreddit, without the subreddit with the most posts that contain a sign, with a baseline of 2022 alone, with long posts only, and on all of Reddit through the search index. On all of Reddit, the control "because" came out at 1.3× and "delve" at 1.3×. Through the search index, bots included, several words rose more than the control: "underscores" (5.5×), "meticulous" (3.4×) and "it's not just" (3.1×). I don't use that to call them rising, because bots are not filtered out and posts of every type are counted.

Found while analysing: the date cutoff in the plan had a typo (it pointed to 2025); the queries use October 1, 2026, as the plan's words said. The control "friend" moved 1.7× in the main comparison but 1.0× within each subreddit. The main move comes from one subreddit, r/MakeNewFriendsHere, which took a bigger share of the sample in 2026, so the control "friend" had swelled and the planned band was too wide. That is the first reason for the change. The second: after a separate review of the method, I replaced the three controls with the drift of 40 random words picked before I looked at their ratios, which gives a wider band than the planned one. I set that rule after seeing the first results, and it was not in the plan. By the planned band, "in conclusion" would have counted as a real fall in the main comparison. By the narrow within-subreddit band I added during the analysis, 7 other signs would have counted as rising and 5 as falling. The middle 95% of those words runs from 0.5× to 2.1× in the main comparison, 0.6× to 1.7× within subreddits, and 0.3× to 2.5× comparing July to September of 2023 and of 2026 (554,836 and 557,171 posts). A sign counts as rising only if the low end of its interval is above the top of that range. I dropped the share of posts with two or more signs. One planned cut by subreddit did not confirm my guess about advice subreddits, so I'm not publishing it.

Added after I saw the results: the comparison of July to September 2023 and 2026, the scenarios where the missing baseline posts carry a sign more often (twice and three times as often), the year-by-year view, the version without r/MakeNewFriendsHere, the random words, and a version of "it's not just X, it's Y" that also accepts a dash (2.2×). Within each subreddit that dash version goes beyond the matching range of random words. I did not find out why, and it counts the dash too.

Here's how far the random words moved:

Checked against the em dash study: 5.49% here and 5.48% there for the first quarter of 2026. The small gap in post counts (about three thousand posts) comes from the extra filters here: known bots, moderator posts and deleted text.

Conflict of interest: KissMyPost makes Reddit research tools. Funding: none.

Corrections: contact me; each fix gets a dated note on this page.

Questions people ask (FAQ)

The data

These CSV files hold the numbers behind the charts and the table: the ratio for each sign in all versions of the comparison, the 40 random words, the monthly, quarterly and yearly series, and the ratios by post length. Shares in ratios.csv, drift_check.csv and by_length.csv are percents; in the monthly, quarterly and yearly files they are fractions of posts. Ratios are the rate in 2026 over the rate in the baseline. None of the files contain post text or usernames. License: CC BY 4.0; credit KissMyPost, link to this page.

• CSVRatio for every signShare of posts with each sign in 2019-2022 and 2026, the ratio with its 95% interval, and the same ratio within each subreddit.
• CSVSigns against random-word driftFor each sign, the ratio in four versions of the comparison and whether it sits above the random-word range in each.
• CSVRandom wordsThe 40 random words used to measure drift, with their ratios in the main comparison and within subreddits.
• CSVEm dash by monthMonthly share of text posts with an em dash in the body, January 2019 to September 2026, with the number of posts.
• CSV"Delve" by monthMonthly share of text posts with "delve", with the number of posts.
• CSV"Tapestry" by monthMonthly share of text posts with "tapestry", with the number of posts.
• CSV"It's not just X, it's Y" by monthMonthly share of text posts matching the pattern, with the number of posts.
• CSV"Delve" by year against 2019-2022Ratio for each year from 2023 to 2026, January to September, with 95% intervals. The other yvb files cover the em dash, "tapestry", "testament to", the pattern, "let's dive in" and "in conclusion".
• CSVTransition words by yearYearly share of text posts, January to September, with 95% intervals; the y files cover three of the five transition words, the reply phrase, the control word and emoji lists.
• CSVZero-width-space code by quarterQuarterly share of text posts with the zero-width-space code, which may be an editor leftover; q_f2 and q_f4 cover headings and bold labels in lists.
• CSVRatios by post lengthRatio for every sign in short, medium and long posts (by characters of body text), with 95% intervals.
• PNGMain chart (image)Ratio for every sign with the random-word range, as an image.

Zenodo DOI: https://doi.org/10.5281/zenodo.24139687