#李佳怡 #茂名母狗 茂名母狗 李佳怡 露脸性爱视频被前男友曝光 清纯人设彻底崩塌 看视频🌏传送门 关注吃瓜频道➡️ @v123 投稿:@ab123
❤4

Channel
@dashijian09
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
85,977subscribers
+8,709 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1002416059183 |
|---|---|
| Type | Channel |
| Usernames | @ab333 @dashijian09 |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 8 September 2026 |
| Measurements held | 53 |
| Confirmed unchanged | 1 time, most recently 8 September 2026 |
| On Telegram | t.me/dashijian09 |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Sept 2026, 19:56 | 85,977 | -7 |
| 7 Sept 2026, 09:40 | 85,984 | +29 |
| 5 Sept 2026, 16:01 | 85,955 | +37 |
| 4 Sept 2026, 16:19 | 85,918 | -72 |
| 3 Sept 2026, 15:19 | 85,990 | -7 |
| 3 Sept 2026, 01:42 | 85,997 | +210 |
| 2 Sept 2026, 11:54 | 85,787 | +178 |
| 2 Sept 2026, 01:42 | 85,609 | +509 |
| 1 Sept 2026, 11:12 | 85,100 | -21 |
| 1 Sept 2026, 01:35 | 85,121 | +51 |
| 31 Aug 2026, 08:53 | 85,070 | -9 |
| 31 Aug 2026, 00:58 | 85,079 | +6 |
| 30 Aug 2026, 08:28 | 85,073 | +41 |
| 29 Aug 2026, 23:12 | 85,032 | -72 |
| 29 Aug 2026, 08:17 | 85,104 | -4 |
| 29 Aug 2026, 01:27 | 85,108 | +26 |
| 28 Aug 2026, 11:25 | 85,082 | +61 |
| 28 Aug 2026, 01:24 | 85,021 | +56 |
| 27 Aug 2026, 12:55 | 84,965 | +25 |
| 27 Aug 2026, 03:55 | 84,940 | first reading |
646 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 68 pages of Telegram’s post history, 20 posts per page.
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 447 of 594 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 8 September 2026 |
|---|---|
| Posts held | 646 (7 August 2026 – 8 September 2026) |
| Views total | 1,554,722 |
| Reactions total | 1,238 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Sept 2026, 18:06 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.
Measured directly from 437 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.
1,347 reactions across 446 posts, in 10 distinct kinds. The most used accounts for 48.6% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 654 | 48.6% | |
| ❤ | 366 | 27.2% | |
| 🤣 | 85 | 6.31% | |
| 💩 | 76 | 5.64% | |
| 🤡 | 61 | 4.53% | |
| 🤮 | 45 | 3.34% | |
| 😱 | 33 | 2.45% | |
| 😭 | 13 | 0.965% | |
| 🙏 | 11 | 0.817% | |
| 🐳 | 3 | 0.223% |
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 485 of the 646 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 1,347 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 646 most recent posts we hold, published 7 August 2026 to 8 September 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.
#李佳怡 #茂名母狗 茂名母狗 李佳怡 露脸性爱视频被前男友曝光 清纯人设彻底崩塌 看视频🌏传送门 关注吃瓜频道➡️ @v123 投稿:@ab123
❤4
你老婆叫我来做裤子,做这个需要脱裤子吗? 关注吃瓜频道➡️ @v123 投稿:@ab123
❤1🤡1😭1
#军方在内比都为NCA少数民族领导人设宴 现场有女性表演 内比都消息:一段视频显示,军方委员会近日在内比都为已签署《全国停火协议》(NCA)的部分少数民族武装领导人举办晚宴,席间有年轻女性进行舞蹈表演。该视频正在网络上引发广泛关注和传播。 关注吃瓜频道➡️ @v123 投稿:@ab123
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这一招学名叫啥?是什么技术? 关注吃瓜频道➡️ @v123 投稿:@ab123
这种是好空姐吗 关注吃瓜频道➡️ @v123 投稿:@ab123
我的评价是gpt不如豆包 建议所有打工人都学一学这种工作态度,来了活就接着,管他妈什么活。 关注吃瓜频道➡️ @v123 投稿:@ab123
#小姐姐吐槽 #巨婴 翻译:“我也嫁给了个巨婴。只是我没想到婚后情况会糟糕成这样,我现在每一天都对他充满了怨恨。我做饭、打扫、拖地、洗衣服,而他在空闲时间所做的这一切,就是跑出去骑单车,根本不帮忙做家务。 有了孩子后情况变得更恶劣。我好几次抓到他用我幼童女儿的浴巾去擦她刚尿尿/拉完屎的屁股,导致那条浴巾散发着尿骚味还沾着便便,而到了晚上洗完澡,他竟然还用同一条浴巾去擦孩子的身体。 我跟他说这太恶心了,他居然理直气堡地说他不用湿纸巾,因为‘不环保’。你能想象我的痛苦和绝望吗? 有时候,我甚至能在卫生间的电灯开关上看到粪便痕迹,因为他每次擦屁股只舍得用一点点卫生纸,理由同样是‘不环保’等等。 他还会直接把沾着便便脏污的毛巾丢进洗衣机,连预先手洗冲掉都不做,我每天跟在他屁股后面打扫收拾,简直心力交瘁…… 你可以看得出来,他这个人非常抠门,根本舍不得花钱请保洁来打扫。 与此相反的是,他反而说是我患有强迫症,但事实上明明是他…
💩5🤮5🤡2❤1
9月7 陕西西安,来自对抗路的魔法战士 #吐口水 关注吃瓜频道➡️ @v123 投稿:@ab123
骑车请看路 关注吃瓜频道➡️ @v123 投稿:@ab123
半夜你老婆发来信息 老公我爱你 #出轨 #内涵 关注吃瓜频道➡️ @v123 投稿:@ab123
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什么鬼东西? #反差 关注吃瓜频道➡️ @v123 投稿:@ab123
开口彩礼减一半 !什么口音 关注吃瓜频道➡️ @v123 投稿:@ab123
Showing the 12 most recent of 646 posts we hold for @dashijian09. 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.
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.
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.
@dashijian09 named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
We ourselves saw each of these resolve to a real page at some point before it went vacant — a genuine, evidenced change, not an inference from absence.
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 8 September 2026 — this entry's latest reading, not the date you are reading this.
“东南亚吃瓜@v123” (@dashijian09), 85,977 subscribers as measured 8 September 2026. Telegram Register, tgregister.com/channel/dashijian09.
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.