#青岛市北区 #蝶震皇后 #年龄23 🐻C 体重96 #霸居岛城服务系T0榜首 #🏠10 外15 夜40 #预约 @qingdaoNaigai
❤1

Channel
@QDGKB9090
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
84,062subscribers
+13,116 since we began measuring on 11 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1002242309521 |
|---|---|
| Type | Channel |
| Username | @QDGKB9090 |
| Description | 青岛聊天约课交流总群 https://t.me/qingdaodadui |
| Created | Between 1 June 2024 and 30 September 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 August 2026 |
| Last confirmed live | 17 September 2026 |
| Measurements held | 29 |
| Confirmed unchanged | 1 time, most recently 17 September 2026 |
| On Telegram | t.me/QDGKB9090 |
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 81% 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 |
|---|---|---|
| 17 Sept 2026, 17:41 | 84,062 | +1,113 |
| 15 Sept 2026, 11:58 | 82,949 | +611 |
| 13 Sept 2026, 18:57 | 82,338 | +497 |
| 11 Sept 2026, 21:57 | 81,841 | +577 |
| 9 Sept 2026, 15:01 | 81,264 | +1,301 |
| 6 Sept 2026, 03:59 | 79,963 | +832 |
| 3 Sept 2026, 21:18 | 79,131 | +245 |
| 2 Sept 2026, 15:53 | 78,886 | +311 |
| 1 Sept 2026, 11:55 | 78,575 | +238 |
| 31 Aug 2026, 12:27 | 78,337 | +252 |
| 30 Aug 2026, 14:25 | 78,085 | +237 |
| 29 Aug 2026, 13:53 | 77,848 | +266 |
| 28 Aug 2026, 16:11 | 77,582 | +387 |
| 27 Aug 2026, 19:05 | 77,195 | +325 |
| 26 Aug 2026, 20:12 | 76,870 | +362 |
| 25 Aug 2026, 18:22 | 76,508 | +228 |
| 24 Aug 2026, 16:02 | 76,280 | +1,121 |
| 22 Aug 2026, 23:07 | 75,159 | +453 |
| 21 Aug 2026, 14:16 | 74,706 | +479 |
| 20 Aug 2026, 11:28 | 74,227 | first reading |
50 posts held, back to 4 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 82 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 29 of 36 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 21 September 2026 |
|---|---|
| Posts held | 50 (4 August 2026 – 21 September 2026) |
| Views total | 79,721 |
| Reactions total | 105 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 21 Sept 2026, 23:24 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 21 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Measured directly from 24 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.
150 reactions across 40 posts, in 9 distinct kinds. The most used accounts for 80.7% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 121 | 80.7% | |
| 👍 | 9 | 6.00% | |
| 🤮 | 7 | 4.67% | |
| 👏 | 4 | 2.67% | |
| 🥰 | 4 | 2.67% | |
| 👎 | 2 | 1.33% | |
| 👌 | 1 | 0.667% | |
| 🔥 | 1 | 0.667% | |
| 😡 | 1 | 0.667% |
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 42 of the 50 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 150 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 50 most recent posts we hold, published 4 August 2026 to 21 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.
#青岛市北区 #蝶震皇后 #年龄23 🐻C 体重96 #霸居岛城服务系T0榜首 #🏠10 外15 夜40 #预约 @qingdaoNaigai
❤1
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❤1
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👎2❤1
📍地址: #青岛 城阳区 # 七喜 (个人认证老师) #(不抽烟,无纹身,好停车) # 🏠 (p7 pp13) # 有课室(无定金,见面满意后付) (可上门,可夜 ,先付车费,车费都不愿意付的就不要开口要外出了,自己过来就行) 私信 @qxi1689 双向机器人 @xianjiujiu_bot
❤7
艺名:沁沁 身高:167 体重:98 🐻:c 课费:💖800p一次 💖1400pp120分钟 💖包夜2000 服务: 微m 听话乖乖女 敏感易高潮易,给你不一样的体验 🈚T👄 口爆 鸳鸯浴 胸推 69 毒🐲 舔🥚🥚 特别会提供亲情价值 禁止🚫 醉酒 🈚T 嗑💊 预约联系: @facaila89 双向联系: @yillfubot
❤4
#青岛市北区 #温雅 川妹子 #年龄25 身高166 体重49kg 胸围36c #床上有绝活❤️❤️期待哥哥来调教 #🏠10 夜40 #预约 @qingdao897
❤2
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❤3👍2
#青岛市北区 #涵涵 #年龄24 身高165 体重45kg 胸围36i #巨乳 纯天然 可定制 #🏠10 🚗14 #预约 @qingdao897
青岛 市北区 个人老师:贝贝 p7 pp133 有🏠,可上门,可✌️ 无定金🧧,面付 ,方便停车 性格好,温柔体贴有耐心,身材好邻家小妹 天然🐻手感好 敏感体质易出💦 联系机器人 @beibeisz_bot
❤2
曼曼,青岛极品少妇 🐻超级大,a4腰,微信原相机视频如上☝️ 丰满饱满圆,电动小马达 限时6张起 预约随时联系 @xuanxuanlso
❤3
青岛学生个人兼职 只外出,照片不是本人免费 老师-@chunixi
👍2❤1
#青岛市北区 #甜心 #年龄22 身高168 体重49kg 胸围34c #风骚酥入骨❤️❤️纤腰怀绝技 详情看图4 #营业时间中午11-晚上1点 #🏠10 🚗15 #预约 @qingdao897
❤2
Showing the 12 most recent of 50 posts we hold for @QDGKB9090. 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 3 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.
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 17 September 2026 — this entry's latest reading, not the date you are reading this.
“青岛公开榜” (@QDGKB9090), 84,062 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/QDGKB9090.
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.