#福利姬 #女神 #丝袜 #足控 极品高颜值大学生自拍美腿高跟鞋超性感身材各种姿势大尺度私拍丝袜足控福利
❤2👍2🔥2

Channel
@KMOOKMOA
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
47,375subscribers
+357 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1002544694773 |
|---|---|
| Type | Channel |
| Username | @KMOOKMOA |
| Created | Between 1 March 2025 and 31 July 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 1 October 2026 |
| Measurements held | 34 |
| Confirmed unchanged | 1 time, most recently 1 October 2026 |
| On Telegram | t.me/KMOOKMOA |
Adult — 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 98% 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 |
|---|---|---|
| 1 Oct 2026, 07:37 | 47,375 | +93 |
| 18 Sept 2026, 05:57 | 47,282 | +46 |
| 16 Sept 2026, 02:58 | 47,236 | -3 |
| 14 Sept 2026, 11:18 | 47,239 | +9 |
| 12 Sept 2026, 18:01 | 47,230 | -11 |
| 10 Sept 2026, 18:19 | 47,241 | +27 |
| 7 Sept 2026, 17:37 | 47,214 | +29 |
| 4 Sept 2026, 15:41 | 47,185 | +12 |
| 3 Sept 2026, 01:44 | 47,173 | +16 |
| 2 Sept 2026, 01:43 | 47,157 | +3 |
| 1 Sept 2026, 03:07 | 47,154 | +3 |
| 31 Aug 2026, 02:29 | 47,151 | +1 |
| 30 Aug 2026, 04:38 | 47,150 | +12 |
| 29 Aug 2026, 07:08 | 47,138 | +14 |
| 28 Aug 2026, 08:25 | 47,124 | -5 |
| 27 Aug 2026, 10:59 | 47,129 | +20 |
| 26 Aug 2026, 08:58 | 47,109 | +14 |
| 24 Aug 2026, 07:58 | 47,095 | +4 |
| 22 Aug 2026, 18:34 | 47,091 | +10 |
| 21 Aug 2026, 07:22 | 47,081 | first reading |
258 posts held, back to 6 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 142 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 109 of 113 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 2 October 2026 |
|---|---|
| Posts held | 258 (6 August 2026 – 2 October 2026) |
| Views total | 364,450 |
| Reactions total | 1,037 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 3 Oct 2026, 06:43 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.
Lifetime counters from Telegram’s own channel header, read 3 October 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.
Measured directly from 239 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.
2,033 reactions across 247 posts, in 8 distinct kinds. The most used accounts for 53.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 1,080 | 53.1% | |
| 👍 | 505 | 24.8% | |
| 🔥 | 224 | 11.0% | |
| 🤩 | 218 | 10.7% | |
| 👎 | 3 | 0.148% | |
| 👏 | 1 | 0.049% | |
| 😍 | 1 | 0.049% | |
| 🙏 | 1 | 0.049% |
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 247 of the 258 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 2,033 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 258 most recent posts we hold, published 6 August 2026 to 2 October 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.
#福利姬 #女神 #丝袜 #足控 极品高颜值大学生自拍美腿高跟鞋超性感身材各种姿势大尺度私拍丝袜足控福利
❤2👍2🔥2
#反差 #露脸 #少女 #大学生 高颜值眼镜反差少女极品完美炮架大尺度露脸自拍上位乘骑翻白眼简直绝了
❤4🔥2👍1
#大学生 #素人 #露脸 #少女 极品高颜值少女被调教无套调教露脸口交都快被灌满了人前清纯在床上的样子真反差
❤2🔥2👍1
#反差 #眼镜 #少女 #大学生 极品反差眼镜少女清纯颜值露脸对镜自拍福利掰开露出大尺度自慰特写
❤4🔥2👍1
#素人 #黑丝 #少女 #大学生 颜值极品少女穿着黑丝露脸特写视角干到冒白浆拉丝灌满再流出来情趣装来回切换玩的真花
❤2👍2🔥1
#短发 #白虎 #少女 #自拍 反差短发舞蹈少女福利姬露脸自拍展示苗条身材分开双腿自慰抽插无毛白虎
❤3🔥3👍1
#抖音 #网红 #少女 #自拍 抖音反差网红萝莉自拍出租屋里各种姿势自慰抽插性感丝袜美腿足控福利
❤4🔥2👍1
#快手 #清纯 #少女 #高中生 高颜值清纯网红萝莉少女极品苗条身材酒店素颜露脸大尺度调教私拍特写视角
❤4👍2🔥2
#丰满 #性感 #御姐 #网红 高颜值肥臀巨乳御姐超有肉感的性感大肥腿加上肉感十足的巨臀诱惑力是真的完全拉满
❤9🔥3👍2
#网红 #少女 #丝袜 #足控 高颜值极品少女露出大尺度自拍模拟足交白丝袜诱惑自拍身材完美炮架
❤4👍2🔥2
#极品 #反差 #白虎 #精神小妹 高颜值网红少女大学生宿舍自拍各种情趣内衣大尺度展示超反差身材性感极品
❤6👍3🔥2
#反差 #御姐 #女神 #大学生 高颜值少女身材是真的极品自慰私拍反差露出展示完美白虎撸点十足
❤5🔥2👍1
Showing the 12 most recent of 258 posts we hold for @KMOOKMOA. 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.
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.
@KMOOKMOA 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 1 October 2026 — this entry's latest reading, not the date you are reading this.
“学生少女高中生精神小妹” (@KMOOKMOA), 47,375 subscribers as measured 1 October 2026. Telegram Register, tgregister.com/channel/KMOOKMOA.
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.