23 Sept 2021, 05:21 UTC≈19,900 viewsread 6 August 2026
置顶 中国农民丰收节 1.许昕比赛结束就失忆 2.男子为换新手机骗妻子手机被抢 3.女子婚前个人房产离婚5年仍遭公婆霸占 更多: https://telegra.ph/微博当前热搜-09-23-53
Channel
微博热搜
@weibo_hot
On this record: Growth · Engagement · Posts · Citations · Cite this entry
445subscribers
-1 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
Register entry
| Telegram ID | -1001366403906 |
|---|---|
| Type | Channel |
| Username | @weibo_hot |
| Created | Between 1 March 2018 and 31 July 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 14 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 14 August 2026 |
| On Telegram | t.me/weibo_hot |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 14 Aug 2026, 17:46 | 445 | -1 |
| 6 Aug 2026, 19:16 | 446 | no change |
| 6 Aug 2026, 13:40 | 446 | first reading |
Engagement
20 posts held, back to 22 September 2021 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof 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 20 posts for this entry, the most recent from 23 September 2021. An engagement rate over an empty window would be a number about nothing.
Recent posts
23 Sept 2021, 04:21 UTC≈18,600 viewsread 6 August 2026
置顶 中国农民丰收节 1.女子婚前个人房产离婚5年仍遭公婆霸占 2.男子偶遇3名小孩江边捞鱼怒吼叫回 3.许昕比赛结束就失忆 更多: https://telegra.ph/微博当前热搜-09-23-52
23 Sept 2021, 03:21 UTC≈13,000 viewsread 6 August 2026
置顶 让农民生活芝麻开花节节高 1.十一期间北京环球影城人均消费或超3000元 2.男子偶遇3名小孩江边捞鱼怒吼叫回 3.江南布衣回应童装印不当图案 更多: https://telegra.ph/微博当前热搜-09-23-51
23 Sept 2021, 02:21 UTC≈9,120 viewsread 6 August 2026
置顶 中国农民丰收节 1.十一期间北京环球影城人均消费或超3000元 2.司机避让闯红灯老人摔倒身亡担主责 3.厦门疫情 更多: https://telegra.ph/微博当前热搜-09-23-50
23 Sept 2021, 01:21 UTC≈5,980 viewsread 6 August 2026
置顶 中国农民丰收节 1.十一期间北京环球影城人均消费或超3000元 2.杨振宁百岁生日 3.滨州交警通报查酒驾存在漏洞 更多: https://telegra.ph/微博当前热搜-09-23-49
23 Sept 2021, 00:21 UTC≈4,190 viewsread 6 August 2026
置顶 第七十六届联合国大会一般性辩论 1.杨振宁百岁生日 2.为什么一定要分清五谷 3.东莞报告1例H5N6病例 更多: https://telegra.ph/微博当前热搜-09-23-48
22 Sept 2021, 23:21 UTC≈3,070 viewsread 6 August 2026
置顶 让农民生活芝麻开花节节高 1.为什么一定要分清五谷 2.你知道今天是国际熊猫节吗 3.美国新冠零号病人可能2019年4月出现 更多: https://telegra.ph/微博当前热搜-09-22-68
22 Sept 2021, 22:21 UTC≈2,370 viewsread 6 August 2026
置顶 文明交通绿色出行 1.为什么一定要分清五谷 2.美国新冠零号病人可能2019年4月出现 3.陈梦晋级全运会女单十六强 更多: https://telegra.ph/微博当前热搜-09-22-67
22 Sept 2021, 21:21 UTC≈1,880 viewsread 6 August 2026
置顶 让农民生活芝麻开花节节高 1.为什么一定要分清五谷 2.美国新冠零号病人可能2019年4月出现 3.广场舞老人铁锤砸锁霸占足球场 更多: https://telegra.ph/微博当前热搜-09-22-66
22 Sept 2021, 20:21 UTC≈1,550 viewsread 6 August 2026
置顶 让农民生活芝麻开花节节高 1.为什么一定要分清五谷 2.美国新冠零号病人可能2019年4月出现 3.广场舞老人铁锤砸锁霸占足球场 更多: https://telegra.ph/微博当前热搜-09-22-65
22 Sept 2021, 19:21 UTC≈1,280 viewsread 6 August 2026
置顶 让农民生活芝麻开花节节高 1.为什么一定要分清五谷 2.美国新冠零号病人可能2019年4月出现 3.东莞报告1例H5N6病例 更多: https://telegra.ph/微博当前热搜-09-22-64
22 Sept 2021, 18:21 UTC≈1,100 viewsread 6 August 2026
置顶 第七十六届联合国大会一般性辩论 1.为什么一定要分清五谷 2.美国新冠零号病人可能2019年4月出现 3.东莞报告1例H5N6病例 更多: https://telegra.ph/微博当前热搜-09-22-63
Showing the 12 most recent of 20 posts we hold for @weibo_hot. 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.
Citation-graph rank
Citation-graph rank — 1,278,189 of 1,350,102entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Mentions
Named by 2 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.
Cite this entry
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 14 August 2026 — this entry's latest reading, not the date you are reading this.
“微博热搜” (@weibo_hot), 445 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/weibo_hot.
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.