Channel
@DNY9988AA
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
60,580subscribers
+0 since we began measuring on 24 October 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1001569349914 |
|---|---|
| Type | Channel |
| Username | @DNY9988AA |
| First recorded | 24 October 2026 |
| Last confirmed live | 24 October 2026 |
| Measurements held | 2 |
| Confirmed unchanged | 1 time, most recently 24 October 2026 |
| On Telegram | t.me/DNY9988AA |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 24 Oct 2026, 07:27 | 60,580 | no change |
| 24 Oct 2026, 07:22 | 60,580 | first reading |
14 posts held, back to 23 October 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 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.
| Window | Rolling 30 days · latest post in window 24 October 2026 |
|---|---|
| Posts held | 14 (23 October 2026 – 24 October 2026) |
| Views total | 84,285 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 24 Oct 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.
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.
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网络诈骗过去一年导致菲律宾损失达19亿美元 全球反诈骗联盟（GASA）发布《2026年诈骗状况报告》显示，过去一年网络诈骗（情感陷阱、诱导投资、兼职招聘等）致菲律宾损失达19亿美元。 民调显示，84%的菲律宾成年人曾遇诈骗尝试，34%的受害者遭受经济损失，人均损失164美元。GASA合作方指出，虽然大型诈骗园区屡遭打击，但犯罪网络正向乡村化、小型化演变。购物诈骗最常见（24%），其次为投资与招聘诈骗（各占22%）；88%的诈骗发生在即时通讯平台，其中Facebook（58%）与Gmail（33%）沦为重灾区。 😀😀😀😀😀9G.COM 全球布局 实力保障 😀😀😀😀😀7T.COM全球最稳的USDT娱乐城 🔤🔤🔤🔤🔤🔤🔤🔤反水1.3%起大额无忧 🤩🤩🤩🤩 🤩🤩🤩🤩🤩🤩🤩🤩 香港六合彩特码49倍｜真人返水1%｜电子/棋牌/捕鱼返水1.5%｜十三水/德州 无抽佣｜百家乐真人视讯台红30万U 😀 🔤🔤🔤🔤🔤🔤 ⚡️ 【 U…
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Showing the 12 most recent of 14 posts we hold for @DNY9988AA. 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 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.
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 24 October 2026 — this entry's latest reading, not the date you are reading this.
“东南亚大事件/柬埔寨新闻/缅甸头条” (@DNY9988AA), 60,580 subscribers as measured 24 October 2026. Telegram Register, tgregister.com/channel/DNY9988AA.
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.