独领风骚数百白天。

Channel
吃瓜 🍉 段子
@neihanA01
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
30,759subscribers
+9,820 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
Register entry
| Telegram ID | -1001340348923 |
|---|---|
| Type | Channel |
| Username | @neihanA01 |
| Description | 吃瓜 段子 投稿联系 @zjl6688 商务联系 @zjl6688 |
| Created | Between 1 March 2018 and 31 August 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 2 October 2026 |
| Measurements held | 34 |
| Confirmed unchanged | 1 time, most recently 2 October 2026 |
| On Telegram | t.me/neihanA01 |
Topic
Adult — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 91% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 2 Oct 2026, 03:22 | 30,759 | +840 |
| 18 Sept 2026, 14:21 | 29,919 | -535 |
| 16 Sept 2026, 08:38 | 30,454 | +1,251 |
| 14 Sept 2026, 16:02 | 29,203 | +1,021 |
| 13 Sept 2026, 02:55 | 28,182 | -655 |
| 11 Sept 2026, 02:58 | 28,837 | -83 |
| 8 Sept 2026, 01:36 | 28,920 | -113 |
| 4 Sept 2026, 21:17 | 29,033 | -59 |
| 3 Sept 2026, 06:29 | 29,092 | -46 |
| 2 Sept 2026, 03:09 | 29,138 | +1,091 |
| 1 Sept 2026, 01:48 | 28,047 | -5 |
| 31 Aug 2026, 02:47 | 28,052 | +1,377 |
| 30 Aug 2026, 03:55 | 26,675 | -52 |
| 29 Aug 2026, 01:15 | 26,727 | -66 |
| 27 Aug 2026, 23:33 | 26,793 | +1,077 |
| 26 Aug 2026, 21:14 | 25,716 | +1,359 |
| 25 Aug 2026, 23:12 | 24,357 | -59 |
| 24 Aug 2026, 19:43 | 24,416 | -65 |
| 23 Aug 2026, 08:12 | 24,481 | -48 |
| 21 Aug 2026, 22:27 | 24,529 | first reading |
Engagement
945 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 253 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 2.47%
- avg views ÷ 30,759 subscribers
- Avg views / post
- 759
- 377 posts measured
- Reaction rate
- 0.144%
- reactions ÷ views · ER floor
- Posts in window
- 377
- of 945 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 27 of 377 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 2 October 2026 |
|---|---|
| Posts held | 945 (6 August 2026 – 2 October 2026) |
| Views total | 286,261 |
| Reactions total | 26 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 2 Oct 2026, 12:42 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
- Photos
- ≈57,700
- Videos
- ≈29,300
- Links
- ≈1,020
Lifetime counters from Telegram’s own channel header, read 2 October 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
- Video runtime
- 4h 23m
- Average length
- 30s
Measured directly from 528 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
47 reactions across 47 posts, in 2 distinct kinds. The most used accounts for 95.7% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 45 | 95.7% | |
| ❤ | 2 | 4.26% |
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 51 of the 945 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 47 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 945 most recent posts we hold, published 6 August 2026 to 2 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
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👍1
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Showing the 12 most recent of 945 posts we hold for @neihanA01. 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
Names
Channels on the register whose handles appear in this channel's posts.
@blzhaoshang · 5,01037 postsN9.COM|官方频道
@N9_com1 · 12,57036 postsN9.COM 福利频道导航
@n9pd888 · 7,45036 postsPG娱乐 92.cc 官方频道
@pgyx8_com · 2,33436 posts🔥西湖娱乐城-xihu.cc-【实力招商】🔥
@xihuzs · 4,92736 posts🔥金贝娱乐场-jinbei.com-【官方招商】🔥
@jbzhaoshang · 4,98034 posts8T娱乐城 官方-频道
@www8tcom · 37,06134 posts新全迅网
@xinquanxunwang · 4,30431 posts🔥辉煌娱乐城-93.cc-超高返水1.6%起🔥官方频道
@hhylc8888 · 1,74922 posts🔥KK娱乐城-18.vip-🔥官方招商
@kkyl5000 · 1,80216 posts78.COM-官方频道@DF78
@tg78com · 19,99213 posts涩涩备用群💋(防丢失)
@SSBYQ · 36012 posts2G官方频道
@GF2Gcom · 63211 posts8G.com
@tg8g · 33,2828 postsSOSO搜搜
@sooo · 452,3767 postsSOSO搜搜🔞
@SosoCnBot · 226,7856 postsSOSO搜搜
@sososo · 585,3674 posts极搜🔍中文搜索@JISO
@jiso · 327,9543 postsSOSO搜搜
@sosoo · 460,5212 postsSOSO搜搜
@souso · 576,8832 postsU99贵宾会-官方频道
@U99GBH · 2,7861 post
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 2 October 2026 — this entry's latest reading, not the date you are reading this.
“吃瓜 🍉 段子” (@neihanA01), 30,759 subscribers as measured 2 October 2026. Telegram Register, tgregister.com/channel/neihanA01.
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.