音樂視頻:船票 茫茫人海中 你問我找誰 迷中的人啊 別再沉睡 做多少好夢 還是在人堆 你的家在天上 那裡更華貴 資料來源:正見網 https://big5.zhengjian.org/node/296037

Channel
你本該知道
@nbgzd
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
10,566subscribers
-42 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
Register entry
| Telegram ID | -1001404342216 |
|---|---|
| Type | Channel |
| Username | @nbgzd |
| Created | 25 February 2020 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 15 August 2026 |
| Measurements held | 11 |
| Confirmed unchanged | 1 time, most recently 15 August 2026 |
| On Telegram | t.me/nbgzd |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 15 Aug 2026, 00:48 | 10,566 | -3 |
| 13 Aug 2026, 16:18 | 10,569 | +3 |
| 12 Aug 2026, 17:53 | 10,566 | +2 |
| 11 Aug 2026, 20:12 | 10,564 | +1 |
| 10 Aug 2026, 19:38 | 10,563 | -5 |
| 9 Aug 2026, 18:27 | 10,568 | -23 |
| 8 Aug 2026, 21:55 | 10,591 | -10 |
| 8 Aug 2026, 00:15 | 10,601 | -6 |
| 7 Aug 2026, 00:52 | 10,607 | -1 |
| 6 Aug 2026, 12:30 | 10,608 | no change |
| 6 Aug 2026, 12:24 | 10,608 | first reading |
Engagement
257 posts held, back to 6 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 16 pagesof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 0.383%
- avg views ÷ 10,566 subscribers
- Avg views / post
- 40.5
- 250 posts measured
- Reaction rate
- 1.44%
- reactions ÷ views · ER floor
- Posts in window
- 257
- of 257 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 10 of 250 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 13 August 2026 |
|---|---|
| Posts held | 257 (6 August 2026 – 13 August 2026) |
| Views total | 10,120 |
| Reactions total | 7 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 13 Aug 2026, 04:59 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
- 18h 25m
- Average length
- 5m 44s
Measured directly from 193 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
7 reactions across 7 posts, in 3 distinct kinds. The most used accounts for 71.4% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 5 | 71.4% | |
| 👏 | 1 | 14.3% | |
| 😢 | 1 | 14.3% |
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 10 of the 257 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 7reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 257 most recent posts we hold, published 6 August 2026 to 13 August 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
央視12集高調祭江澤民!習近平二十一大前突然出牌 胡錦濤王岐山成風向標,5件舊帳再掀風暴 【紅朝禁聞】
川普雙線佈局!伊朗襲船反噬 驚爆漏油危機|朱鎔基去世引熱議!習背後動手? 【每日頭條】
死後被綁架?朱鎔基討厭習;朱鎔基的政策,習近平全推翻;對法輪功的態度,朱習有本質不同;擅改歌詞犯事,郭德綱吃官司 【新聞看點 李沐陽】
內臟脂肪會讓你的小腹瘦不下!改掉5個簡單的習慣就能減掉 「四維健康」
福建幫為何突然大塌方?泉州、廈門兩大震央連環抓人,溫家寶正拆解習近平權力根基、阻止21大連任,還要追究他⁉️ 「精英論壇」
朱鎔基逝世:他把中國推向世界,為何又親手堵上另一條路? (文昭談古論今)
【 #新聞第一線 】快訊:朱鎔基在北京去世 終年98歲。 完整影片👉🏻 https://newswecantrust.com/5me1nc —————————————————— 點擊訂閱:https://newswecantrust.com/frontline-news 捐款贊助:https://donorbox.org/xwdyx
【 #晚間新聞 】美國破獲史上最大婚姻詐欺案!威州民主黨溫和派候選人驚險勝出!朱鎔基去世北京高度戒備!郭德綱演出改紅歌影射黨魁遭立案調查!高溫威脅奶酪銀行,3億帕瑪森奶酪告急。 完整影片👉🏻 https://newswecantrust.com/rbhk6v —————————————————— 💠觀看更多:https://newswecantrust.com/ntdtv 💠支持我們:https://donation.ntdtv.com/
🔥【 #美國思想領袖 】從鄉村歌手到「公民倡議者」:John Rich 談音樂、信仰、言論自由與 完整影片👉🏻 https://newswecantrust.com/nvmals —————————————————— 💠觀看更多:https://newswecantrust.com/DJYsubscribe 💠支持我們:https://donate.epochtimes.com/
【 #圓桌騎士 】敏感時刻朱鎔基訃告發布!反習派借喪發力?北戴河會前上演「借題發揮」權謀劇。 立即收看👉🏻 https://newswecantrust.com/9789hh ———————————————— 歡迎訂閱:https://newswecantrust.com/RoundTableKnights9
【禁聞】朱鎔基去世 網友翻出舊視頻打臉中共官方
Showing the 12 most recent of 257 posts we hold for @nbgzd. 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.
Citation-graph rank
Citation-graph rank — 280,582 of 1,345,403entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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 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.
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 15 August 2026 — this entry's latest reading, not the date you are reading this.
“你本該知道” (@nbgzd), 10,566 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/nbgzd.
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.