胖子 欠抽 ? #打耳光 #舔狗

Channel
吃瓜东南亚头条㊖
@CG887
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
108,401subscribers
-374 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 100,000–316,228.
Register entry
| Telegram ID | -1001969261825 |
|---|---|
| Type | Channel |
| Username | @CG887 |
| Description | ☎️投稿/曝光: @T3333 头条吃瓜: @bg888 交流群: @ty888 发财助手 © 2018-2026 祝商祺 🎉 #斯里兰卡 #柬埔寨 #泰国 #曝光 #吃瓜 |
| Created | Between 1 April 2023 and 31 October 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 7 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/CG887 |
Topic
National news — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 2026 and assigned it the closest of 31 fixed categories, at 39% 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.
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 07:54 | 108,401 | -56 |
| 11 Aug 2026, 08:15 | 108,457 | -46 |
| 10 Aug 2026, 07:06 | 108,503 | -189 |
| 9 Aug 2026, 05:51 | 108,692 | -69 |
| 8 Aug 2026, 02:01 | 108,761 | -14 |
| 7 Aug 2026, 15:45 | 108,775 | no change |
| 7 Aug 2026, 15:34 | 108,775 | first reading |
Engagement
135 posts held, back to 7 August 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 16 pagesof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 4.60%
- avg views ÷ 108,401 subscribers
- Avg views / post
- 4,990
- 135 posts measured
- Reaction rate
- 0.014%
- reactions ÷ views · ER floor
- Posts in window
- 135
- of 135 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 4 of 135 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 12 August 2026 |
|---|---|
| Posts held | 135 (7 August 2026 – 12 August 2026) |
| Views total | 673,102 |
| Reactions total | 3 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 18:28 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
- Photos
- ≈48,800
- Videos
- ≈69,000
- Links
- ≈1,470
Lifetime counters from Telegram’s own channel header, read 12 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
- Video runtime
- 1h 13m
- Average length
- 43s
Measured directly from 102 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
3 reactions across 3 posts, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 3 | 100.0% |
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 4 of the 135 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 3reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 135 most recent posts we hold, published 7 August 2026 to 12 August 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.
Recent posts
#恋老 就爱这个味 ? 吃个舌头
河南,砸车顶救人 #泡水车
𝕏上网友的消息是真厉害,昨天就知道了。
结婚8年育3孩,丈夫发现均非亲生! 结婚证的作用,就是离婚用的! 目前来看它既不能保证你合法的性生活的权利,也不能保证孩子就是你的!
一位中国女子在巴厘岛炫耀被两个意大利男子亲吻的经历!评论区姐妹们馋坏了
的士司机说,我们这些香港的士司机,最讨厌你们这些人(外地人)坐车头了,风景很好看吗,这么喜欢开车我租给你,让你开个够啊,你坐不起的巴拉巴拉,还说你们这些外地人,坐个短途的士还要怀疑司机绕路。乘客就说,上车之前你怎么不说,不喜欢别人坐前面可以提前说,我自己查个导航怎么了,之类的。 大家怎么看?
全是汉奸 跑美国生小孩 !
在百花奖后台,网友近距离拍摄到,演员张子枫瘦到脱相,甚至胸前肋骨都清晰可见 #平胸
狗头萝莉今天发视频说,你可以质疑我的人品,但不能质疑我的健康,我没有性病。
小忙一会 😘 这种彩礼你出多少 ?
民政部公布上半年民政统计数据,结婚对数同比下降7.5%。 8月12日,民政部发布《2026年2季度民政统计数据》,其中多项数据呈现薛微的、那么一丢丢的异常。 一、结婚对数下降7.5% 2025年上半年:结婚353.9万对,离婚133.1万对。 2026年上半年:结婚327.5万对,离婚138.3万对。 半年时间内,结婚少了26.4万对,下降7.5%;离婚多了5.2万对,上升3.9%。 二、养老机构减少1000多家 养老机构从2025年的40002家变为2026年的38489家,一年少了1513家。 老龄化持续加速的背景下,养老机构为何减少? 三、福利彩票销售同比下降6.6%
Showing the 12 most recent of 135 posts we hold for @CG887. 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.
Mentions
Names
Channels on the register whose handles appear in this channel's posts.
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.
Appears in Telegram’s recommendations for other channels
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.
@bg888 · 302,779
Telegram ranks this channel #1 of 71 here — alongside 70 others — read 10 August 2026
@HR8877 · 305,149
Telegram ranks this channel #3 of 76 here — alongside 75 others — read 10 August 2026
@ac666 · 305,586
Telegram ranks this channel #60 of 61 here — alongside 60 others — read 10 August 2026
This channel appears in 3 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 12 August 2026 — this entry's latest reading, not the date you are reading this.
“吃瓜东南亚头条㊖” (@CG887), 108,401 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/CG887.
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.