📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#Nana 课费:#800/p 1500/PP 地址:#拱墅区 联系:@NNXNY520 标签:#御姐 #六九 #陪浴 #混血 🙋上榜工兵:2个6折工兵位 500/p 900/pp 群里发送“工兵“使用200出击分兑换工兵名额,兑换后转发机器人信息给老师报名,报名成功信息以机器人 @sqxzl002_bot 为准。 ⚠️报名后需24小时内及时出具报告给管理 @lgunvuil990_bot

Channel
杭州深情颜选区认证频道
@hangzhou_0002
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Posts recorded as deleted · Citations · Cite this entry
26,700subscribers
+1,834 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
Register entry
| Telegram ID | -1002643301529 |
|---|---|
| Type | Channel |
| Username | @hangzhou_0002 |
| Description | 💕杭州高颜值的小姐姐都在这里上榜 上榜联系: @zgunvuie_99 深情交流群:https://t.me/hangzhou_000 报告频道:https://t.me/Lckgg051400 |
| Created | Between 1 March 2025 and 31 July 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 7 November 2026 |
| Measurements held | 58 |
| Confirmed unchanged | 1 time, most recently 7 November 2026 |
| On Telegram | t.me/hangzhou_0002 |
Topic
Other / unclassifiable — 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 10 September 2026 and assigned it the closest of 31 fixed categories, at 74% 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 the other topics.
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 7 Nov 2026, 10:17 | 26,700 | -8 |
| 6 Nov 2026, 10:59 | 26,708 | +66 |
| 5 Nov 2026, 09:36 | 26,642 | -4,740 |
| 4 Nov 2026, 09:46 | 31,382 | -92 |
| 3 Nov 2026, 09:36 | 31,474 | +32 |
| 2 Nov 2026, 11:40 | 31,442 | +10 |
| 1 Nov 2026, 11:06 | 31,432 | -15 |
| 31 Oct 2026, 11:40 | 31,447 | +30 |
| 29 Oct 2026, 12:07 | 31,417 | -617 |
| 28 Oct 2026, 12:40 | 32,034 | +67 |
| 26 Oct 2026, 13:20 | 31,967 | +48 |
| 25 Oct 2026, 15:35 | 31,919 | +326 |
| 24 Oct 2026, 12:29 | 31,593 | +220 |
| 23 Oct 2026, 14:38 | 31,373 | +1,085 |
| 22 Oct 2026, 15:55 | 30,288 | +4,074 |
| 21 Oct 2026, 10:16 | 26,214 | +95 |
| 20 Oct 2026, 05:58 | 26,119 | +47 |
| 19 Oct 2026, 05:18 | 26,072 | +50 |
| 18 Oct 2026, 08:19 | 26,022 | +76 |
| 17 Oct 2026, 05:56 | 25,946 | first reading |
Engagement
99 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 140 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 2.72%
- avg views ÷ 26,700 subscribers
- Avg views / post
- 727
- 33 posts measured
- Reaction rate
- 0.114%
- reactions ÷ views · ER floor
- Posts in window
- 33
- of 99 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 12 of 33 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 6 November 2026 |
|---|---|
| Posts held | 99 (7 August 2026 – 6 November 2026) |
| Views total | 23,984 |
| Reactions total | 12 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Nov 2026, 10:19 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
- 508
- Videos
- 201
- Links
- 134
Lifetime counters from Telegram’s own channel header, read 7 November 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.
- Video runtime
- 8m 14s
- Average length
- 5s
Measured directly from 92 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
45 reactions across 30 posts, in 2 distinct kinds. The most used accounts for 93.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 42 | 93.3% | |
| 👍 | 3 | 6.67% |
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 38 of the 99 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 45 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 99 most recent posts we hold, published 7 August 2026 to 6 November 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
Telegram’s own ‘edited’ marker: 1 of 33 posts held from the last 30 days (3.03%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#允恩 课费:#900/p 1600/PP 地址:#拱墅区 联系: @yunen2026 标签:#御姐 #舌吻 #大胸 报告1 报告2
❤3
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#依哆哆 课费:#600/p 1200pp 地址:#西湖区 联系:@ylpfff 标签:#嫩妹 #舌吻 #六九 🙋上榜工兵:2个6折工兵位 400/p 700pp 群里发送“工兵“使用200出击分兑换工兵名额,兑换后转发机器人信息给老师报名,报名成功信息以机器人 @sqxzl002_bot 为准。 ⚠️报名后需24小时内及时出具报告给管理 @lgunvuil990_bot
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#诗雯 课费:#800/p 1500pp 地址:#上城区 联系:@sw1234568 标签:#嫩妹 #舌吻 #六九 🙋上榜工兵:2个6折工兵位 500/p 900pp 群里发送“工兵“使用200出击分兑换工兵名额,兑换后转发机器人信息给老师报名,报名成功信息以机器人 @sqxzl002_bot 为准。 ⚠️报名后需24小时内及时出具报告给管理 @lgunvuil990_bot
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#梦涵 课费:#600/p 地址:#上城区 联系:@XJ27089 标签:#御姐 #无套口 #六九 🙋上榜工兵:2个6折工兵位 400/p 群里发送“工兵“使用200出击分兑换工兵名额,兑换后转发机器人信息给老师报名,报名成功信息以机器人 @sqxzl002_bot 为准。 ⚠️报名后需24小时内及时出具报告给管理 @lgunvuil990_bot
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#雨婷 课费:#800/p 1500/PP 地址:#上城区 联系:@yt58168 标签:#嫩妹 #舌吻 #六九 🙋上榜工兵:2个6折工兵位 500/p 900/pp 群里发送“工兵“使用200出击分兑换工兵名额,兑换后转发机器人信息给老师报名,报名成功信息以机器人 @sqxzl002_bot 为准。 ⚠️报名后需24小时内及时出具报告给管理 @lgunvuil990_bot
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#安娜 课费:#900/p 1600/PP 地址:#西湖区 联系:@annazaixc 标签:#御姐 #舌吻 #六九 #洋马 报告1 报告2 🙋上榜工兵:2个6折工兵位 500/p 1000/pp 群里发送“工兵“使用200出击分兑换工兵名额,兑换后转发机器人信息给老师报名,报名成功信息以机器人 @sqxzl002_bot 为准。 ⚠️报名后需24小时内及时出具报告给管理 @lgunvuil990_bot
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#姐妹花 课费:#1300/PP双飞 地址:#上城区 联系:@Jiemeihuaa 标签: #双飞 #御姐 #69互舔 报告1 报告2 报告3 报告4 报告5 报告6 报告7
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#恋恋 课费:#500/p 900/PP 地址:#拱墅区 联系:@ghvhvjf 标签:#嫩妹 #舌吻 #六九 #BBW 🙋上榜工兵:2个6折工兵位 300/p 500/pp 群里发送“工兵“使用200出击分兑换工兵名额,兑换后转发机器人信息给老师报名,报名成功信息以机器人 @sqxzl002_bot 为准。 ⚠️报名后需24小时内及时出具报告给管理 @lgunvuil990_bot
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#小怡 课费:#800/p 1500/PP 地址:#拱墅区 联系:@xiyi077 标签:#嫩妹 #舌吻 #六九 报告1 🙋上榜工兵:2个6折工兵位 500/p 900/pp 群里发送“工兵“使用200出击分兑换工兵名额,兑换后转发机器人信息给老师报名,报名成功信息以机器人 @sqxzl002_bot 为准。 ⚠️报名后需24小时内及时出具报告给管理 @lgunvuil990_bot
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 艺名:#小野猫 课费:#700/p 1300/PP 地址:#西湖区 联系: @liummm999 频道: @Mummylz_d 标签:#御姐 #毒龙 #混血 报告1 报告2
❤1
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Showing the 12 most recent of 99 posts we hold for @hangzhou_0002. 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.
Posts edited after publishing
@hangzhou_0002 edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
- First edit seen
- 28 September 2026
- Most recent edit
- 25 October 2026
Posts recorded as deleted after publishing
We recorded 8 posts on @hangzhou_0002 as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
- First seen missing
- 14 August 2026
- Most recent seen missing
- 27 October 2026
- Median time before seen missing
- 3 days
- Seen missing in the last 30 days
- 4
Mentions
Named by 1 registered channel — 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.
@Lckgg051400 · 5,76370 posts杭州深情颜选区
@hangzhou_000 · 32,9346 posts蘇冉茶馆1号群
@HZcha1hao · 5,6543 posts杭州蘇冉茶馆
@HZcha6688 · 8,1353 posts杭州蘇冉茶馆1号
@HZcha678 · 1142 posts杭州茶馆 讨论群
@HZchataolun · 6,3002 postshhh
@aahqspa92 · 3,2341 post杭州 拱墅区 迪丽热巴的家
@dnjdooi1 · 5,8511 post短租女友(金丝雀)-全国资源
@DZNYQG · 1,3141 post短租女友之吃肉报告分享
@DZNYQQ · 1351 post短租女友(金丝雀包养)
@HZDQNY · 5,4161 post短租女友(金丝雀包养)
@HZDUNY · 3,3501 post杭州KTV|杭州商k|荤场
@Hzzhwdktv · 6,4421 post悦悦的秘密花园
@ktt1346 · 131 post杭州KTV真空情趣商k|荤场
@KTV1111111111 · 33,5631 post慕斯女仆私影频道
@musinvpu · 1,9571 post兔兔的爱情屋💞
@tu7981 · 1,8911 post欣欣的后花园
@wjtvv52 · 301 post
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 7 November 2026 — this entry's latest reading, not the date you are reading this.
“杭州深情颜选区认证频道” (@hangzhou_0002), 26,700 subscribers as measured 7 November 2026. Telegram Register, tgregister.com/channel/hangzhou_0002.
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.