Report No. 01

Reading the rooms in Reddit

Forty-seven Reddit rooms, measured the same way: how long people write, who is talking, and what they talk about.

Published
12 November 2026
Data built
Comments 4 September 2026, nine rooms re-measured 29 September; post topics 26 October; harvest hours 12 October
By
Time&Again
License
Free to read and share, CC BY 4.0

Comments, 5 to 28 August 2026. 33,839 comments under 1,940 posts in 47 rooms.

30.5%of comments in the median commerce room use purchase words, 2.4 times the rate in consult

66.0%of comments in the median cohort room are in the first person, the most of the five families

0.82stories for every verdict in the median meta/snark room, the one family where verdicts outnumber stories

4.3%of comments in the median room use one of 22 words for the wider culture, like “nowadays” or “trend”

Posts, 23 March to 22 October 2026. 1,179,387 posts in the same 47 rooms, each given topics by a model.

20.8%of posts in the median cohort room are contested, the most of the five families

46.9%of posts in the median commerce room are practical help, the most of the five families

34topics are among the largest in five rooms or more, and 17 stay in a single room

212questions argued in three threads or more; we count them and show none of their text

Before adding rooms to the ones GOSSIP reads on Reddit, we measured fifty candidates the same way and kept forty-seven, after taking three out on a second read. They run from r/instacart, with 69 thousand members, to r/relationship_advice, with 15.4 million. We counted two things over two windows and keep them apart: how 33,839 comments under 1,940 posts were written, from 5 to 28 August 2026, and what 1,179,387 posts were about, from 23 March to 22 October 2026, as a model read them.

Every number here is a count, a share or a median over a room’s comments or posts. No comment or post is quoted or paraphrased, and the comments and posts themselves aren’t published anywhere.

Five kinds of room

The rooms fall into families by what they’re for, and five families have seven rooms or more: commerce, where people buy, sell and resell; cohort, where they gather by who they are, like parents, servers or people over thirty; place, for cities and countries; meta/snark, where people watch other people or other rooms; and consult, where they ask for advice. Three smaller families hold the other four rooms. Every family number here is the median of its rooms.

Figure 2

The forty-seven rooms, as rooms of people

The crowd: one person for 10,000 members, and each added person doubles it

10K members
80K members
1.3M members
20.5M members

The bubble: one line for about 40 characters of the median comment

80 characters
160 characters
320 characters

Commerce 11 rooms

r/declutter

650K members

193-character median

r/poshmark †

94K members

153-character median

r/onebag

806K members

152-character median

r/mercari

102K members

151-character median

r/instacart

69K members

130-character median

r/Frugal

6.4M members

127-character median

r/BuyItForLife

2.8M members

126-character median

r/Depop

188K members

116-character median

r/BuyCanadian

180K members

105-character median

r/starbucks

301K members

94-character median

r/Costco †

881K members

75-character median

Cohort 9 rooms

r/datingoverthirty †

1.1M members

289-character median

r/AskWomen †

5.5M members

239-character median

r/Parenting

8.1M members

193-character median

r/beyondthebump

736K members

174-character median

r/AskMenOver30 †

586K members

155-character median

r/TwoXChromosomes

13.6M members

155-character median

r/childfree

1.5M members

152-character median

r/Serverlife

257K members

151-character median

r/GenX

246K members

114-character median

Place 8 rooms

r/AskAnAmerican

1M members

144-character median

r/AskUK

1.9M members

127-character median

r/AskNYC

423K members

120-character median

r/nyc

921K members

116-character median

r/britishproblems

881K members

106-character median

r/london

1.3M members

102-character median

r/CasualUK

2.6M members

91-character median

r/LosAngeles

725K members

88-character median

Meta/snark 7 rooms

r/OutOfTheLoop

3.4M members

182-character median

r/HobbyDrama

1.4M members

158-character median

r/digitalminimalism

121K members

136-character median

r/blogsnark

195K members

129-character median

r/SubredditDrama

979K members

124-character median

r/BeautyGuruChatter

3M members

116-character median

r/collapse

524K members

116-character median

Consult 8 rooms

r/smallbusiness †

1.9M members

298-character median

r/Marriage

813K members

184-character median

r/MiddleClassFinance

224K members

181-character median

r/relationship_advice

15.4M members

181-character median

r/jobs †

1.8M members

163-character median

r/careerguidance †

4.6M members

159-character median

r/dating_advice

4.6M members

157-character median

r/Advice

1.3M members

145-character median

Confessional 1 room

r/CPTSD †

331K members

324-character median

Practice 1 room

r/tea

951K members

120-character median

Other 2 rooms

r/workingmoms

118K members

243-character median

r/ADHD

2M members

197-character median

Each of the 47 rooms drawn as a room of people, with a crowd for its members and a speech bubble for its median comment. The crowd is a log scale, so no figure stands for one member. Rooms are grouped by family and ordered within each family by median comment length. The measurement holds nothing on who a room's members are, so where a room's name says who it is for, the crowd follows the name: women in r/AskWomen, r/TwoXChromosomes, r/workingmoms and r/beyondthebump, men in r/AskMenOver30, and older people throughout r/GenX and in about a third of r/AskMenOver30 and r/datingoverthirty. Every other room gets one mix, in which about one person in four sits and about one in eight is drawn older. The people are Open Peeps figures by Pablo Stanley (CC0), and most of the older ones are an older head set on another figure's body. † marks the nine rooms read on 90 posts; the other 38 are read on 30. Source: Arctic Shift, posts of 5 to 28 August 2026, members as of 4 September 2026, measured 4 September 2026, nine rooms re-measured 29 September 2026, drawing built 29 September 2026.

In the median commerce room, 30.5% of comments use purchase words, 2.4 times the rate in consult, the next of the five at 12.8%, and the comments run short, 127 characters at the median. The word list counts store names too, so r/Costco’s 30.9% includes every comment that names the store. r/Costco also has the shortest comments of the forty-seven, 75 characters at the median, measured on ninety posts where most rooms had thirty.

Cohort rooms have the most first person of the five, 66.0% of comments, and the most story. In the median cohort room, 21.0% of comments tell something the writer did or something about a person in their life (“I tried”, “my husband”), and the median room’s stories outnumber its verdicts 1.86 to 1.

Meta/snark has the most talk about the wider culture of the five, at 5.3%, and it’s the one family of all eight whose median room has more verdicts than stories, 0.82 stories for every verdict. A verdict here is a comment that passes judgment in words like “I think”, “honestly” or “hot take”.

Consult has the longest comments of the five, 172 characters at the median, and 32.0% of them run past 280. Place rooms have the shortest comments of any family, 111 characters, and the least first person, 50.7%, and we’d guess that’s because people in those rooms ask about the city more than they talk about themselves.

The kind of post follows the same families. In the median commerce room 46.9% of posts are practical help, the most of the five, and 12.4% are about taste, also the most. Cohort rooms have the most contested posts, 20.8%, along with 36.4% that are personal stories. Meta/snark rooms have the fewest practical posts, 10.9%, and the most about events, 17.6%. Consult rooms come to 41.6% practical and 29.6% personal, and place rooms to 31.4% practical and 14.6% personal.

Room size

On the forty-seven, the larger rooms lean toward longer comments (ρ = +0.21), and forty-seven rooms aren’t enough for us to tell that lean apart from chance (p = 0.15). The tie that clears chance is to who’s talking: larger rooms have more different people per hundred comments (ρ = +0.34, p = 0.02). In the smaller rooms a comment is more likely to come from someone who has already commented, which we read as the same people answering each other.

These rooms were picked because people write in them at some length, so they cover a narrower spread than Reddit does, and a narrow spread pulls any correlation toward zero. We haven’t measured a wider set, so we can’t say whether the lean toward longer comments would sharpen or fade.

Figure 1

Room size and what is said in it

Median comment length, in characters

Spearman ρ = +0.21 · p = 0.15

0100200300100K1M10Mmembers, log scaler/MiddleClassFinance · 224K members · 181-character median commentr/Depop · 188K members · 116-character median commentr/Frugal · 6.4M members · 127-character median commentr/onebag · 806K members · 152-character median commentr/tea · 951K members · 120-character median commentr/starbucks · 301K members · 94-character median commentr/Marriage · 813K members · 184-character median commentr/OutOfTheLoop · 3.4M members · 182-character median commentr/declutter · 650K members · 193-character median commentr/workingmoms · 118K members · 243-character median commentr/Parenting · 8.1M members · 193-character median commentr/ADHD · 2.0M members · 197-character median commentr/Advice · 1.3M members · 145-character median commentr/dating_advice · 4.6M members · 157-character median commentr/relationship_advice · 15.4M members · 181-character median commentr/TwoXChromosomes · 13.6M members · 155-character median commentr/beyondthebump · 736K members · 174-character median commentr/britishproblems · 881K members · 106-character median commentr/london · 1.3M members · 102-character median commentr/LosAngeles · 725K members · 88-character median commentr/AskAnAmerican · 1.0M members · 144-character median commentr/AskNYC · 423K members · 120-character median commentr/AskUK · 1.9M members · 127-character median commentr/nyc · 921K members · 116-character median commentr/CasualUK · 2.6M members · 91-character median commentr/HobbyDrama · 1.4M members · 158-character median commentr/SubredditDrama · 979K members · 124-character median commentr/blogsnark · 195K members · 129-character median commentr/BeautyGuruChatter · 3.0M members · 116-character median commentr/collapse · 524K members · 116-character median commentr/digitalminimalism · 121K members · 136-character median commentr/mercari · 102K members · 151-character median commentr/BuyCanadian · 180K members · 105-character median commentr/BuyItForLife · 2.8M members · 126-character median commentr/instacart · 69K members · 130-character median commentr/childfree · 1.5M members · 152-character median commentr/Serverlife · 257K members · 151-character median commentr/GenX · 246K members · 114-character median commentr/smallbusiness · 1.9M members · 298-character median commentr/AskMenOver30 · 586K members · 155-character median commentr/careerguidance · 4.6M members · 159-character median commentr/jobs · 1.8M members · 163-character median commentr/CPTSD · 331K members · 324-character median commentr/poshmark · 94K members · 153-character median commentr/Costco · 881K members · 75-character median commentr/datingoverthirty · 1.1M members · 289-character median commentr/AskWomen · 5.5M members · 239-character median comment

Different people per 100 comments

Spearman ρ = +0.34 · p = 0.02

405060708090100K1M10Mmembers, log scaler/MiddleClassFinance · 224K members · 63.2 different people per 100 commentsr/Depop · 188K members · 69.3 different people per 100 commentsr/Frugal · 6.4M members · 66.8 different people per 100 commentsr/onebag · 806K members · 60.2 different people per 100 commentsr/tea · 951K members · 69.4 different people per 100 commentsr/starbucks · 301K members · 62.9 different people per 100 commentsr/Marriage · 813K members · 60.8 different people per 100 commentsr/OutOfTheLoop · 3.4M members · 58.4 different people per 100 commentsr/declutter · 650K members · 48.5 different people per 100 commentsr/workingmoms · 118K members · 60.0 different people per 100 commentsr/Parenting · 8.1M members · 74.5 different people per 100 commentsr/ADHD · 2.0M members · 71.4 different people per 100 commentsr/Advice · 1.3M members · 66.8 different people per 100 commentsr/dating_advice · 4.6M members · 66.6 different people per 100 commentsr/relationship_advice · 15.4M members · 71.3 different people per 100 commentsr/TwoXChromosomes · 13.6M members · 62.4 different people per 100 commentsr/beyondthebump · 736K members · 82.9 different people per 100 commentsr/britishproblems · 881K members · 61.9 different people per 100 commentsr/london · 1.3M members · 65.5 different people per 100 commentsr/LosAngeles · 725K members · 54.8 different people per 100 commentsr/AskAnAmerican · 1.0M members · 69.1 different people per 100 commentsr/AskNYC · 423K members · 58.5 different people per 100 commentsr/AskUK · 1.9M members · 68.4 different people per 100 commentsr/nyc · 921K members · 51.8 different people per 100 commentsr/CasualUK · 2.6M members · 75.8 different people per 100 commentsr/HobbyDrama · 1.4M members · 54.6 different people per 100 commentsr/SubredditDrama · 979K members · 54.8 different people per 100 commentsr/blogsnark · 195K members · 48.8 different people per 100 commentsr/BeautyGuruChatter · 3.0M members · 58.3 different people per 100 commentsr/collapse · 524K members · 59.1 different people per 100 commentsr/digitalminimalism · 121K members · 60.8 different people per 100 commentsr/mercari · 102K members · 64.4 different people per 100 commentsr/BuyCanadian · 180K members · 69.6 different people per 100 commentsr/BuyItForLife · 2.8M members · 81.2 different people per 100 commentsr/instacart · 69K members · 41.1 different people per 100 commentsr/childfree · 1.5M members · 60.2 different people per 100 commentsr/Serverlife · 257K members · 56.2 different people per 100 commentsr/GenX · 246K members · 68.2 different people per 100 commentsr/smallbusiness · 1.9M members · 66.2 different people per 100 commentsr/AskMenOver30 · 586K members · 71.6 different people per 100 commentsr/careerguidance · 4.6M members · 68.1 different people per 100 commentsr/jobs · 1.8M members · 61.4 different people per 100 commentsr/CPTSD · 331K members · 71.5 different people per 100 commentsr/poshmark · 94K members · 44.8 different people per 100 commentsr/Costco · 881K members · 69.6 different people per 100 commentsr/datingoverthirty · 1.1M members · 50.3 different people per 100 commentsr/AskWomen · 5.5M members · 73.8 different people per 100 comments
read on 30 posts read on 90 posts
Each dot is one of the 47 rooms, placed by its membership and by what its comments look like. Larger rooms lean toward longer comments (ρ = +0.21), a lean that 47 rooms cannot separate from chance (p = 0.15). Larger rooms have more different people per 100 comments (ρ = +0.34, p = 0.02). ρ is Spearman's rank correlation, and p comes from 20,000 shuffles of the rooms. Rings mark the nine rooms read on 90 posts; the other 38 are read on 30. Source: Arctic Shift, posts of 5 to 28 August 2026, members as of 4 September 2026, measured 4 September 2026, nine rooms re-measured 29 September 2026, chart built 29 September 2026.

Talk about the culture

To count talk about the wider culture we used a list of 22 words and phrases, among them “nowadays”, “trend”, “algorithm”, “gen z” and “anyone else”, and counted the comments that use at least one. The median room comes in at 4.3%. One room goes past 10%: r/datingoverthirty, one of the nine read on ninety posts, at 12.7%. None of the forty-seven is a room about the culture, and the culture comes up as an aside inside rooms about buying and selling, dating, parenting or a city. The list counts words, so a joke about the algorithm and a complaint about it look the same to it.

The model’s topics give a second view of what the rooms are about. Among each room’s eight largest topics there are 97 different ones, 34 of them among the largest in five rooms or more and 17 in a single room. Mental health and therapy is among the largest in 17 of the 47 rooms, taking anywhere from 3.8% of a room’s posts to half of them, and it turns up in six cohort rooms, six consult rooms, one commerce room, one meta/snark room and three rooms in the smaller families. A post carries up to three topics, so a room’s shares add to more than 100%. The model also grouped the questions that rooms argue over, 212 of them asked in three threads or more under 78 topics, and we count them here without showing any.

How we measured

In each room we counted how long the comments run, how often people write in the first person, how many different people are talking, how much of the talk is about buying things and how often anyone mentions the wider culture.

Every room got thirty posts, three from each of ten equal slices of those three and a half weeks, along with each post’s own comments, up to a hundred per thread. Nine rooms gave back too few comments that way, so we read them again on ninety posts, and each of the nine came back with at least 150 different people commenting. Those nine rest on three times as many posts as the other thirty-eight.

We also had a model read the posts in the same forty-seven rooms, 1,179,387 of them between 23 March and 22 October 2026, and give each post up to three topics from a list of 103 and one kind: practical help, a personal story, something contested, a matter of taste, an event, or talk about the room itself. That read covers posts over seven months, where the comment counts cover three and a half weeks, so we keep the two apart and never set a number from one beside a number from the other.

For the model’s read we checked one post in ten a second time with the room hidden, 448,861 posts from these rooms over the two years to 22 October 2026. The two readings gave the same first topic for 65.5% and shared at least one topic for 89.4%, and agreement on the kind of post came to a kappa of 0.63, so we treat the topics as a coarse sort.

Method and limitsShow

A first pass we threw out

The first time through, we took the newest comments in each room, and those weren’t the comments under the posts we’d sampled, so that pass was reading two crowds as if they were one, and we’ve thrown out its room-by-room numbers. One of the things it told us was that a room’s size had nothing to do with how long its comments run. Every number above comes from the corrected read.

What the list leaves out

The forty-seven are a shortlist we chose from the first pass’s numbers, partly by reading each room and partly to fill out the families, and we took three out when the corrected numbers came in, so a different read would give a different list. Two of the rooms are about health; they’re counted and drawn with the rest, and we haven’t written about either one. The busiest threads were cut at a hundred comments, 148 threads across 40 rooms, so we’ve left out anything about how busy a room is, since those numbers would all be floors.

The post read

Of the 1,207,113 posts we checked, 404,603 (33.5%) were gone from Reddit at the archive’s second look; they stay in every count, apart from the 27,719 whose topic was unclear. The topics came from claude-haiku-5-5, a smaller model than the one that grouped the argued questions. Where a figure would rest on fewer than 20 accounts, or an argued question on fewer than three threads, we left it out, which removed 36 rows.

The time of day

We’ve also left out the time of day. The harvest GOSSIP runs every day reads each of its 59 Reddit rooms once a day and takes the comments under threads less than two days old, and the hours on those comments sit close to the hour it ran. Before 21 September it took half of its comments in the 9am hour, New York time, and 30.4% of them were written in the 8am hour. Posts in the same rooms spread across the day. The forty-seven rooms in this report were read from an archive across ten slices of three and a half weeks, and we haven’t measured their hours.

Figure 3

The hours on the harvest's comments

Taken before 21 September

19,253 comments · 38,132 posts

most taken, 9amcomments, by the hour written12am · 1.6% of comments written1am · 1.4% of comments written2am · 1.3% of comments written3am · 1.4% of comments written4am · 1.3% of comments written5am · 1.5% of comments written6am · 3.6% of comments written7am · 11.5% of comments written8am · 30.4% of comments written9am · 9.1% of comments written10am · 5.3% of comments written11am · 3.7% of comments written12pm · 2.6% of comments written1pm · 1.5% of comments written2pm · 2.6% of comments written3pm · 1.9% of comments written4pm · 2.4% of comments written5pm · 3.6% of comments written6pm · 2.3% of comments written7pm · 2.6% of comments written8pm · 2.5% of comments written9pm · 2.5% of comments written10pm · 2.0% of comments written11pm · 1.3% of comments writtencomments, by the hour the harvest took them1am · 1.8% of comments taken2am · 0.3% of comments taken8am · 5.7% of comments taken9am · 51.5% of comments taken10am · 2.2% of comments taken11am · 12.8% of comments taken12pm · 6.0% of comments taken1pm · 0.9% of comments taken2pm · 1.2% of comments taken3pm · 3.5% of comments taken6pm · 2.2% of comments taken7pm · 1.1% of comments taken9pm · 5.4% of comments taken10pm · 1.3% of comments taken11pm · 4.1% of comments takenposts in the same rooms, by the hour written12am · 3.7% of posts written1am · 3.1% of posts written2am · 2.9% of posts written3am · 3.1% of posts written4am · 2.8% of posts written5am · 3.5% of posts written6am · 3.9% of posts written7am · 5.0% of posts written8am · 5.6% of posts written9am · 5.2% of posts written10am · 5.2% of posts written11am · 5.1% of posts written12pm · 4.8% of posts written1pm · 4.7% of posts written2pm · 4.5% of posts written3pm · 4.4% of posts written4pm · 4.2% of posts written5pm · 4.0% of posts written6pm · 4.2% of posts written7pm · 4.2% of posts written8pm · 4.1% of posts written9pm · 4.1% of posts written10pm · 3.9% of posts written11pm · 3.8% of posts written12am3am6am9am12pm3pm6pm9pm

Taken from 21 September

5,578 comments · 25,726 posts

most taken, 11amcomments, by the hour written12am · 2.1% of comments written1am · 1.6% of comments written2am · 1.3% of comments written3am · 1.4% of comments written4am · 1.3% of comments written5am · 1.7% of comments written6am · 2.3% of comments written7am · 4.6% of comments written8am · 8.2% of comments written9am · 9.7% of comments written10am · 10.5% of comments written11am · 9.3% of comments written12pm · 8.6% of comments written1pm · 5.8% of comments written2pm · 6.0% of comments written3pm · 5.5% of comments written4pm · 4.9% of comments written5pm · 3.7% of comments written6pm · 2.3% of comments written7pm · 1.6% of comments written8pm · 1.3% of comments written9pm · 1.8% of comments written10pm · 2.6% of comments written11pm · 1.8% of comments writtencomments, by the hour the harvest took them4am · 0.1% of comments taken6am · 0.0% of comments taken7am · 0.1% of comments taken8am · 0.2% of comments taken9am · 0.1% of comments taken10am · 4.3% of comments taken11am · 37.1% of comments taken12pm · 10.9% of comments taken1pm · 16.9% of comments taken2pm · 10.0% of comments taken3pm · 6.1% of comments taken4pm · 5.4% of comments taken5pm · 0.9% of comments taken6pm · 5.4% of comments taken7pm · 2.7% of comments takenposts in the same rooms, by the hour written12am · 3.4% of posts written1am · 3.2% of posts written2am · 2.7% of posts written3am · 2.7% of posts written4am · 2.8% of posts written5am · 3.2% of posts written6am · 3.7% of posts written7am · 4.8% of posts written8am · 5.5% of posts written9am · 6.0% of posts written10am · 5.5% of posts written11am · 5.8% of posts written12pm · 5.6% of posts written1pm · 5.0% of posts written2pm · 4.7% of posts written3pm · 4.5% of posts written4pm · 4.4% of posts written5pm · 4.1% of posts written6pm · 3.9% of posts written7pm · 3.8% of posts written8pm · 3.7% of posts written9pm · 4.0% of posts written10pm · 3.7% of posts written11pm · 3.4% of posts written12am3am6am9am12pm3pm6pm9pm
hour written hour the harvest took it
Each dot is an hour of the day in New York, and its area is the share of a row's comments or posts that fall in that hour. The harvest GOSSIP runs every day took 24,831 comments from the 59 Reddit rooms it reads, each from a thread less than two days old. Before 21 September it took 51.5% of them in the 9am hour, and 30.4% were written in the 8am hour. Of those taken before 21 September, 52.3% were written less than two hours before the harvest took them. From 21 September the harvest's busiest hour was 11am, with 37.1% of the comments it took, and no hour holds more than 10.5% of the comments written. Posts in the same rooms spread across the day in both periods, at no more than 6% in any hour. The dashed line marks the hour in which the harvest took the most comments. Source: GOSSIP's daily Reddit harvest, comments and posts published 28 July to 11 October 2026, counted 12 October 2026, chart built 12 October 2026.

If there’s a room on Reddit you read, which one is it, and do you comment there or only read? The forty-seven are the rooms we measured, and we’d like to know how close they come to the rooms people spend their time in.

Send us a note

Sources

  • Reddit posts and comments: the Arctic Shift archive. Posts of 5 to 28 August 2026 and their comments, members as of 4 September 2026, measured 4 September 2026; nine rooms re-measured 29 September 2026.
  • Post topics and kinds: 1,179,387 posts from the same 47 rooms, 23 March to 22 October 2026, read by claude-haiku-5-5; built 26 October 2026.
  • Hours on comments: GOSSIP’s daily Reddit harvest, comments and posts published 28 July to 11 October 2026, counted 12 October 2026.
  • Figure 2’s people: Open Peeps by Pablo Stanley, CC0.

How to cite

Time&Again. Reading the rooms in Reddit. Report No. 01, 12 November 2026. CC BY 4.0.