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Channel

惠子桜雅苑|东京大阪情报|酒店服务|交流社群💜

@jk6089

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

2,815subscribers

-151 since we began measuring on 7 August 2026

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

Register entry

Telegram ID-1002812667924
TypeChannel
Username@jk6089
CreatedBetween 1 June 2025 and 30 September 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 September 2026
Last confirmed live9 September 2026
Measurements held12
Confirmed unchanged1 time, most recently 9 September 2026
On Telegramt.me/jk6089

Growth

2,8152,9662,890.57 August 2026 — 2,966 subscribers7 August 2026 — 2,965 subscribers10 August 2026 — 2,942 subscribers14 August 2026 — 2,906 subscribers17 August 2026 — 2,905 subscribers20 August 2026 — 2,886 subscribers24 August 2026 — 2,883 subscribers27 August 2026 — 2,869 subscribers30 August 2026 — 2,857 subscribers3 September 2026 — 2,824 subscribers6 September 2026 — 2,824 subscribers9 September 2026 — 2,815 subscribers7 August 20269 September 2026
12 measurements spanning 33 days, net -151. 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”. The vertical axis spans 2,792–2,989 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
9 Sept 2026, 03:192,815-9
6 Sept 2026, 07:222,824no change
3 Sept 2026, 09:562,824-33
30 Aug 2026, 22:252,857-12
27 Aug 2026, 16:552,869-14
24 Aug 2026, 13:042,883-3
20 Aug 2026, 11:332,886-19
17 Aug 2026, 10:232,905-1
14 Aug 2026, 05:372,906-36
10 Aug 2026, 12:252,942-23
7 Aug 2026, 14:142,965-1
7 Aug 2026, 07:172,966first reading

Engagement

10 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
1.24%
avg views ÷ 2,815 subscribers
Avg views / post
34.8
10 posts measured
Reaction rate
7.47%
reactions ÷ views · ER floor
Posts in window
10
of 10 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 7 of 10 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 6 September 2026
Posts held10 (5 September 20266 September 2026)
Views total348
Reactions total23
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken6 Sept 2026, 07:22 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.

Reaction mix

23 reactions across 7 posts, in 3 distinct kinds. The most used accounts for 69.6% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
1669.6%
👍521.7%
🔥28.70%

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 7 of the 10 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 23 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 10 most recent posts we hold, published 5 September 2026 to 6 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.

Recent posts

5 Sept 2026, 12:31 UTC38 views2 reactionsread 6 September 2026
Photo

妹妹舌頭很靈活 會吹舔 可以舔蛋蛋 可以配合殘廢澡 🤤 叫床聲音 👍 出差東京大阪找惠子安排😊

2

5 Sept 2026, 09:10 UTC44 views4 reactionsread 6 September 2026
Photo

遇到自己真的喜歡的,時間真的不要排得太趕😂 如果行程允許,會比較推薦安排 2~3 小時,整個過程不用一直趕時間,慢慢聊天、互動,開開心心享受當下。 感謝我的朋友這次一起安排,整體體驗很舒服,氣氛也很放鬆~ 女孩子互動自然又貼心,服務態度也很好,讓人有種被好好照顧的感覺❤️ 難得遇到喜歡的,就好好安排時間,開心享受整個過程就對了! 😆

2👍1🔥1

5 Sept 2026, 07:57 UTC60 views4 reactionsread 6 September 2026
Photo

靠北🤣 心心念念講了超久,終於給他試到了啦! 我這老朋友之前一直碎念:「有機會一定要試一次 AV 女優!」結果這次真的讓他圓夢,整個人爽到不行哈哈哈😂問他爽不爽?這還需要問嗎?當然爽啊🤣🤣果然專業的就是不一樣,期待這麼久總算沒有白等!

2👍1🔥1

5 Sept 2026, 05:41 UTC40 views3 reactionsread 6 September 2026

✨ 惠子每日心意 ✨ 每天都在努力 → 認真、準時、準點更新 💕 就是希望哥哥們,不管什麼時候來, 都能有滿滿的新選擇。 不論你喜歡 👉 清純、性感、火辣、溫柔 惠子都幫你準備好一整桌大餐🍽 讓你挑到心儀的、最對味的那一位。 哥哥們只需要動動手指, 其餘的交給惠子安排~ 放鬆一下,找到最合適的陪伴 ❤️

2👍1

5 Sept 2026, 04:28 UTC41 views3 reactionsread 6 September 2026
Photo

🫟番號|JP-BB41 🎨条件:166/ 32/ 55kg /K+ 氣質人妻 前凸後翹 大奶姐姐 雪碧肌膚 堅挺雙峰 冰火 奶炮 口爆 觀音坐蓮服務 都配合 本人超溫柔懂按摩哦 💙推荐加Gleezy找惠子 w8433 💚TG约妹联络 @y03512_TM 🌱TG联络惠子 t.me/pvT968 🌐新手流程 t.me/yy03512 👉正妹慾望日記 @kop88hgt 👉Gleezy色色交流群 FB8843 #东京 #大阪 #新宿 #涩谷 #池袋 #上野 #浅草 #难波 #心斋桥 #梅田#日本旅游 #日本出差 #华人交流群

2👍1

5 Sept 2026, 03:56 UTC43 views3 reactionsread 6 September 2026
Photo

🫟番號|JP-BB34 🎨条件:157/ 21/ 40kg /B+ 清純小女友來惹別錯過‼️‼️‼️客人約過說是很讚 嬌小身材很好操控互動黏黏糊糊也很有女朋友的感覺 私密處粉嫩 很會叫窩 💙推荐加Gleezy找惠子 w8433 💚TG约妹联络 @y03512_TM 🌱TG联络惠子 t.me/pvT968 🌐新手流程 t.me/yy03512 👉正妹慾望日記 @kop88hgt 👉Gleezy色色交流群 FB8843 #东京 #大阪 #新宿 #涩谷 #池袋 #上野 #浅草 #难波 #心斋桥 #梅田#日本旅游 #日本出差 #华人交流群

3

Showing the 10 most recent of 10 posts we hold for @jk6089. 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

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

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 7 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 9 September 2026 — this entry's latest reading, not the date you are reading this.

“惠子桜雅苑|东京大阪情报|酒店服务|交流社群💜” (@jk6089), 2,815 subscribers as measured 9 September 2026. Telegram Register, tgregister.com/channel/jk6089.

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.