Channel
@douban_read
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
35,506subscribers
-1,335 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1001206770471 |
|---|---|
| Type | Channel |
| Username | @douban_read |
| Created | 3 February 2020 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 25 October 2026 |
| Measurements held | 50 |
| Confirmed unchanged | 1 time, most recently 25 October 2026 |
| On Telegram | t.me/douban_read |
Other / unclassifiable — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 9 September 2026 and assigned it the closest of 31 fixed categories, at 41% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 25 Oct 2026, 23:05 | 35,506 | -9 |
| 24 Oct 2026, 20:27 | 35,515 | -16 |
| 23 Oct 2026, 17:39 | 35,531 | -26 |
| 22 Oct 2026, 20:19 | 35,557 | -21 |
| 21 Oct 2026, 15:59 | 35,578 | -27 |
| 20 Oct 2026, 12:28 | 35,605 | -6 |
| 19 Oct 2026, 15:09 | 35,611 | -6 |
| 18 Oct 2026, 18:27 | 35,617 | -14 |
| 17 Oct 2026, 18:56 | 35,631 | -14 |
| 16 Oct 2026, 15:50 | 35,645 | -20 |
| 15 Oct 2026, 19:18 | 35,665 | -22 |
| 14 Oct 2026, 17:59 | 35,687 | -12 |
| 13 Oct 2026, 10:49 | 35,699 | -91 |
| 9 Oct 2026, 05:56 | 35,790 | -61 |
| 4 Oct 2026, 15:39 | 35,851 | -125 |
| 26 Sept 2026, 10:20 | 35,976 | -125 |
| 19 Sept 2026, 09:17 | 36,101 | -44 |
| 16 Sept 2026, 23:00 | 36,145 | -27 |
| 14 Sept 2026, 21:17 | 36,172 | -29 |
| 13 Sept 2026, 09:59 | 36,201 | first reading |
11 posts held, back to 12 March 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 145 pages of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 11 posts for this entry, the most recent from 8 April 2026. An engagement rate over an empty window would be a number about nothing.
603 reactions across 11 posts, in 3 distinct kinds. The most used accounts for 47.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 288 | 47.8% | |
| 👍 | 251 | 41.6% | |
| 🙏 | 64 | 10.6% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 11 of the 11 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 603 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 11 most recent posts we hold, published 12 March 2026 to 8 April 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
我特别引以为傲的一件事是，上大学的时候我因机缘巧合拿到了学校图书馆无人售货小摊的经营权，一经接手我便立马上架了卫生巾，独立包装一元一片。之后我的经营权被图书馆管理没收，在交涉的过程中，我提出要求无论如何都要维持卫生巾的销售，后面几番拉扯对方答应了我的要求。 这件事除了我和身边的朋友，没多少人知道，有一种做无名英雌的爽感。现在我已毕业多年，在小红书上搜发现卫生巾还在卖，他们有好好遵守承诺，我感觉到幸福和满足。 说点自恋的话，这个破烂的世界会因为我的存在而稍微变得好那么一点点，所以我一定一定要坚持活下去，我不能放弃生的希望。 source
❤126👍23🙏11
source
❤27🙏6
source
❤44
source
👍41❤1
每天都得反PUA source
👍61
source
👍17
source
🙏41👍14
source
❤36
笑死哈哈哈哈哈哈哈哈哈哈 source
👍32🙏2
从去年郑智化控诉深圳机场无障碍设施与配套服务的缺位，到近期兰州铁路局针对建议列车售卖卫生巾的女乘客发布失实声明，最终都指向同一个核心议题：公共空间，到底属于谁？ 那些指责郑智化“夸大其词、浪费公共资源”的人，那些激烈反对铁路列车售卖卫生巾的男性，论调背后都藏着同一个心照不宣的预设：公共空间只属于“标准的人”，而这个所谓的“标准”，往往指向的是健全的顺性别异性恋成年男性。 于是，当不符合这套默认“标准”的人——女性、身心障碍者、老人与儿童、性少数群体等等——向公共空间提出合理诉求时，ta们的需求往往被污名化为“巨婴”，好像ta们切实存在的困难诉求，甚至不应该被作为公共议题讨论。郑智化事件的评论区里，对我来说最刺眼的一条留言是：“我们凭什么要让残疾人享受特权！”；而对铁路列车售卖卫生巾大为破防的男性最常挂在嘴边的谬论是：“今天敢要求卖卫生巾，明天是不是就要避孕套，后天是不是还要在高铁上设产房？” 这些看似荒诞的反对声，背后藏着…
❤52👍14🙏4
今年三八节的宣传语 source
👍49❤2
Showing the 11 most recent of 11 posts we hold for @douban_read. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 4 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 11 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 25 October 2026 — this entry's latest reading, not the date you are reading this.
“豆瓣精选” (@douban_read), 35,506 subscribers as measured 25 October 2026. Telegram Register, tgregister.com/channel/douban_read.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.