家人们我女装好看吗

Channel
小言的后花园
@xiaoyan2026916
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
447subscribers
+103 since we began measuring on 27 October 2026
Risers and fallers across the register · movement among entries of Under 1,000.
Register entry
| Telegram ID | -1004499090020 |
|---|---|
| Type | Channel |
| Username | @xiaoyan2026916 |
| Created | 16 September 2026 — measured — dated from the channel’s first post |
| First recorded | 27 October 2026 |
| Last confirmed live | 4 November 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 4 November 2026 |
| On Telegram | t.me/xiaoyan2026916 |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 4 Nov 2026, 23:26 | 447 | +61 |
| 28 Oct 2026, 07:27 | 386 | +42 |
| 27 Oct 2026, 10:22 | 344 | first reading |
Engagement
15 posts held, back to 16 September 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 1 page of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 39.4%
- avg views ÷ 447 subscribers
- Avg views / post
- 176
- 3 posts measured
- Reaction rate
- 5.38%
- reactions ÷ views · ER floor
- Posts in window
- 3
- of 15 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 1 of 3 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 27 October 2026 |
|---|---|
| Posts held | 15 (16 September 2026 – 27 October 2026) |
| Views total | 528 |
| Reactions total | 14 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 27 Oct 2026, 10:22 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
- Video runtime
- 1m 52s
- Average length
- 22s
Measured directly from 5 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
25 reactions across 7 posts, in 3 distinct kinds. The most used accounts for 52.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🥰 | 13 | 52.0% | |
| ❤ | 11 | 44.0% | |
| 😱 | 1 | 4.00% |
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 9 of the 15 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 25 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 15 most recent posts we hold, published 16 September 2026 to 27 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.
Recent posts
Telegram’s own ‘edited’ marker: 0 of 3 posts held from the last 30 days (0%) 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).
回武汉了宝宝们
家人们想让我回武汉吗
🥰13😱1
武汉学生会2.0 报告榜 『推荐指数:⭐️⭐️⭐️⭐️⭐️(参照末尾星级评判标准)』 【验证人 】:周密 【日 期】:2026.10.7 【美女昵称】:#小言 【所属频道】: https://t.me/whxsh_985/10958 【地 区】:#洪山区 【见面感觉】:9 【身材曲线】:9 【风情指数】:9 【技术含量】:9 【年 龄】:18 【身 高】:160左右 【凶 器】:d 【肤 色】:正常 【人照相似】:人照10分 【服务内容】:口 做 洗澡 舌吻 【服务过程】:妹子是典型的 bbw ,长相还可以,很有肉感,前凸后翘,屁股大,关键很敏感,奶头揉一下哼一下,感觉揉奶头都能高潮,边抽插边揉奶头,就这样,妹子哼哼唧唧个不停,还挡开我揉奶头的手,最后后入中给了她。 缺点:无。 上榜联系 @whlll_bot 群聊 @whxsh_…
❤2
开课咯宝宝们
随后 便是我最爱的秋天
❤1
国庆休两天
Video, posted without a caption
❤3
推荐指数:⭐️⭐️⭐️⭐️⭐️(参照末尾星级评判标准)』 【验证人 】:孙某 【日 期】:2026.9.21 【美女昵称】:#小言 【所属频道】:https://t.me/whxsh_985/10958 【地 区】:#洪山区 【见面感觉】:9 【身材曲线】:10 【风情指数】:10 【技术含量】:10 【年 龄】:18 【身 高】:160左右 【凶 器】:c+ 【肤 色】:正常 【人照相似】:人照10分 【服务内容】:口 做 洗澡 舌吻 【服务过程】:老师颜值很高,人照10分,特别热情!非常温柔,很会聊天。和老师呆在一起,感觉是自己的温顺女友。上楼去找老师,和老师抱在一起,被老师坐在上面很快就出水。换了好几个姿势,每个姿势都感觉到自己的小JJ在踌躇。最后和老师聊聊天走人。强烈推荐兄弟们去试试,老师很好说话人也非常好,18岁非常年轻,物超所值…
❤1
武汉学生会2.0 报告榜 『推荐指数:⭐️⭐️⭐️⭐️⭐️(参照末尾星级评判标准)』 【验证人 】:Travis King 【日 期】:2026.9.20 【美女昵称】:#小言 【所属频道】:https://t.me/whxsh_985/10958 【地 区】:#洪山区 【见面感觉】:8.8 【身材曲线】:9 【风情指数】:9 【技术含量】:9 【年 龄】:18、9 【身 高】:160左右 【凶 器】:c+ 【肤 色】:正常 【人照相似】:人照9分 【服务内容】:口 做 洗澡 舌吻 【服务过程】:老师皮肤不错,嫩是真的嫩。接下来是🐍,老师的舌头湿滑软糯,值得一品。嫩妹三件套,老师口的很用心、无齿感。做的时候感觉老师比较干,但是紧是真的紧。女上抱着做很有感觉差点出💦。换了老师在下面,可能比较干的原因没舍得太深入,时间就差不多聊了聊洗洗走了 …
恨我一辈子 那跟爱我一辈子有什么区别
武汉学生会2.0 报告榜 『推荐指数:⭐️⭐️⭐️⭐️⭐️(参照末尾星级评判标准)』 【验证人 】:周密 【日 期】:2026.9.17 【美女昵称】:#小言 【所属频道】: https://t.me/whxsh_985/10958 【地 区】:#洪山区 【见面感觉】:9.5 【身材曲线】:9 【风情指数】:9 【技术含量】:9 【年 龄】:18 【身 高】:160左右 【凶 器】:c+ 【肤 色】:正常 【人照相似】:人照10分 【服务内容】:口 做 洗澡 舌吻 【服务过程】:进门看见穿着jk小小心动了一下。接下来洗澡,照顾二弟很温柔,微微一硬以示尊敬。老师皮肤不错,嫩是真的嫩。接下来是🐍,老师的舌头湿滑软糯,值得一品。嫩妹三件套,老师口的很用心、无齿感。做的时候感觉老师比较干,但是紧是真的紧。女上抱着做很有感觉差点出💦。换了老师在下面,…
❤1
Showing the 12 most recent of 15 posts we hold for @xiaoyan2026916. 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.
Forward network
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
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.
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 4 November 2026 — this entry's latest reading, not the date you are reading this.
“小言的后花园” (@xiaoyan2026916), 447 subscribers as measured 4 November 2026. Telegram Register, tgregister.com/channel/xiaoyan2026916.
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.