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Telegram profile photo for 深圳龙华兔兔频道

Channel

深圳龙华兔兔频道

@nsnnfjdj

On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry

1,203subscribers

+1,147 since we began measuring on 5 September 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1004423142031
TypeChannel
Username@nsnnfjdj
CreatedBetween 1 June 2026 and 31 August 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded5 September 2026
Last confirmed live17 September 2026
Measurements held3
Confirmed unchanged1 time, most recently 17 September 2026
On Telegramt.me/nsnnfjdj

Growth

561,203629.55 September 2026 — 56 subscribers8 September 2026 — 574 subscribers17 September 2026 — 1,203 subscribers5 September 202617 September 2026
3 measurements spanning 11 days, net +1,147. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
17 Sept 2026, 02:551,203+629
8 Sept 2026, 04:38574+518
5 Sept 2026, 15:2356first reading

Engagement

7 posts held, back to 5 September 2026the 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
4.99%
avg views ÷ 1,203 subscribers
Avg views / post
60.0
6 posts measured
Reaction rate
this channel exposes no reaction counts
Posts in window
7
of 7 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.

What these figures were computed from
WindowRolling 30 days · latest post in window 5 September 2026
Posts held7 (5 September 20265 September 2026)
Views total360
Reactions total
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken5 Sept 2026, 15:23 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
21s
Average length
11s

Measured directly from 2 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.

Recent posts

5 Sept 2026, 14:21 UTC63 viewsread 5 September 2026
Photo

✅花名:#兔兔 😘水费:#1000p 📍地区:#龙华 🧑‍🩰身高:162 罩杯:B 💎标签:#嫩妹 #舌吻 #六九 ✈️电报:@kdjndbs ☎️双向:@tutu7937_bot 深圳赏颜阁: @szsyg1 上榜机器人: @hajimi11_bot 车评机器人: @qwglbot 写车评获取出击码参与抽奖

5 Sept 2026, 14:20 UTC56 viewsread 5 September 2026
Photo

🎉 兔兔半价卷福利 🎁 奖品清单 • 单p半价卷 × 1 🛡️ 参与条件 • 加入 深圳赏颜阁 • 加入 深圳赏颜阁(备用) • 加入 深圳赏颜阁(轮播) • 加入 深圳赏颜阁公开榜 • 加入 赏颜阁【甄选榜】 • 加入 深圳龙华兔兔频道 • 必须设置用户名 🔮 祝福加持 • 祝福消耗:50肾结石 • 祝福上限:无限次 • 祝福一次增加一个抽奖号,提高中奖率 • 幸运值祝福:支持 幸运值说明 ⌛ 开奖时间 • 2026-09-09 15:18:00 📜 抽奖说明 优惠卷有效期(7天) 半价卷有效期(3天) 白嫖卷有效期(3天) (半价卷,白嫖卷需出报告) 禁止转让私下交易买卖发现永久拉黑。 炸号作废,禁止乱水!!! 中奖兑换 @kdjndbs ✨ 愿好运伴随每一位参与者,祝大家好运!

5 Sept 2026, 14:10 UTC64 viewsread 5 September 2026
Photo

🎰 兔兔5折劵工兵活动 📮 参与条件: 🎫 加入-深圳龙华兔兔频道 🎫 加入-花荟交流群 🎫 加入-深圳花荟资源录 🎫 加入-深圳花荟群 └ 🎟️ 发言数大于 10 条 🎁 奖品内容: 💰️ 单p5折工兵劵 × 1 ⚠️ 抽奖说明: 注:1.有效期5天,过期无效,花荟群回收! 2.工兵专属补贴金活动,凭中奖记录联系老师兑奖,不可折现,属约课抵现! 3..炸号中奖作废回收! 4.中奖出击后需要提供真实报告给与群里发布,供后面群友出击参考! 中奖联系:https://t.me/a888888n/5107 📅 开奖日期:(北京时间) 2026年09月08日 22时10分00秒

5 Sept 2026, 14:08 UTC49 viewsread 5 September 2026
Photo

兔兔 @kdjndbs 地区课费: » #龙华 #民治 » 1000P 1800PP » 18 162cm 47kg 标签: #1000以内 #不限次 #制服 #包夜 #包时 #手势验证 #按摩 #新人 查看: » 看报告 » 写报告 » 榜单 » 群组 » 防失联

5 Sept 2026, 13:53 UTC73 viewsread 5 September 2026
Forwarded from @SZxiuchechuji_zyPhoto

艺名:#兔兔 坐标:#龙华 私聊:@kdjndbs 双向:@tutu7937_bot 频道:https://t.me/nsnnfjdj 消费:1000p 1800pp 人照:人照已验证💯

Showing the 7 most recent of 7 posts we hold for @nsnnfjdj. 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 5 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.

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 17 September 2026 — this entry's latest reading, not the date you are reading this.

“深圳龙华兔兔频道” (@nsnnfjdj), 1,203 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/nsnnfjdj.

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.