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Telegram profile photo for Twins Chinese | Анна Кузина

Channel

Twins Chinese | Анна Кузина

@twinschinese

Verified by Telegram

On this record: Topic · Observations · Channels linked by matching posts · Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Posts edited after publishing · Citations · Cite this entry

14,701subscribers

-105 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-1001460122589
TypeChannel
Username@twinschinese
Description🏮чемпион мира по китайскому языку 🏮коуч уровня PCC ICF 🏮мама близнецов-билингвов 🏮 курсы китайского языка А+: Включена Роскомнадзором в перечень персональных страниц: https://knd.gov.ru/license?id=673c6fdd1039886b1dfbde63&registryType=bloggers
CreatedBetween 1 April 2019 and 31 October 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live9 November 2026
Measurements held57
Confirmed unchanged1 time, most recently 9 November 2026
On Telegramt.me/twinschinese

Topic

Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 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 the other topics.

Observations

These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.

Content that also appears on other registered channels

Posts published here appear in near-verbatim form on 1 other registered channel. This entry also sits inside a group of 3 channels linked by matching posts, directly or through other channels in the group; two members of the group may share no posts with each other. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.

Matching posts — open both and compare (3 of the pairs behind the counts below)
Posted firstThenOverlapGap
@twinschinese/6324 · this entry29 Jul 2026, 08:22 UTC@Twins_Chinese_school/129229 Jul 2026, 08:23 UTC1.0072 seconds
@twinschinese/6341 · this entry3 Aug 2026, 06:53 UTC@Twins_Chinese_school/13093 Aug 2026, 07:14 UTC1.0021 minutes
@Twins_Chinese_school/13103 Aug 2026, 14:19 UTC@twinschinese/6342 · this entry3 Aug 2026, 14:19 UTC1.00under a minute
Channels this entry shares post bodies with, where at least one matching post pair has no forward credit between the two channels, or to a common source, recorded in our data
ChannelMatching postsText overlapTypical gapPublished first
@Twins_Chinese_school12 (8/8 hand-verifiable sample passed)1.00under a minute@Twins_Chinese_school (7–5)

Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.

What this cannot establish

MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.

Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. 1 of the 2 matches recorded here fall after that date and carried no header when we read them. The rest predate reliable capture and are not evidence either way.

“Published first” means first in this corpus. We hold 17 comparable posts for this entry, running 28 July 2026 to 7 August 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.

The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.

  • Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
  • 'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
  • shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
  • Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).

Of the group of 3 channels, the one whose matching posts we hold earliest is @twinschinese_life. That channel is not among the channels listed above, so this entry may share no posts with it and this does not say where this entry’s content came from. It is a statement about our reading window, not a claim of authorship.

Recorded under the key clone_mutual, last confirmed 7 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.

Channels linked by matching posts

This channel is in a group of 3 registered channels linked by matching posts, directly or through other channels in the group. This channel may share no posts with some of the channels listed below, and listed channels may share none with each other. Links are found by comparing text fingerprints across every channel on the register.

Which channel, if either, published first is deliberately not shown in this list. “First” could only mean first in our own crawl, which depends on how deep we happened to reach into each channel’s history, not on when the content was actually first posted. Our one hand-check read 45 of the highest-confidence flagged pairs against the live Telegram pages. In 18 of them the live page showed a “Forwarded from” credit that our database had no record of, so reading that pair as an unattributed copy was wrong; the check did not measure who published first. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Near-verbatim matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.

Growth

14,65514,82114,7386 August 2026 — 14,806 subscribers7 August 2026 — 14,802 subscribers8 August 2026 — 14,796 subscribers9 August 2026 — 14,788 subscribers10 August 2026 — 14,768 subscribers11 August 2026 — 14,758 subscribers12 August 2026 — 14,751 subscribers13 August 2026 — 14,736 subscribers16 August 2026 — 14,739 subscribers17 August 2026 — 14,732 subscribers19 August 2026 — 14,718 subscribers20 August 2026 — 14,713 subscribers22 August 2026 — 14,712 subscribers24 August 2026 — 14,709 subscribers25 August 2026 — 14,706 subscribers26 August 2026 — 14,701 subscribers28 August 2026 — 14,697 subscribers29 August 2026 — 14,695 subscribers30 August 2026 — 14,692 subscribers31 August 2026 — 14,686 subscribers1 September 2026 — 14,682 subscribers2 September 2026 — 14,670 subscribers3 September 2026 — 14,655 subscribers5 September 2026 — 14,672 subscribers9 September 2026 — 14,702 subscribers11 September 2026 — 14,719 subscribers13 September 2026 — 14,714 subscribers15 September 2026 — 14,709 subscribers16 September 2026 — 14,713 subscribers19 September 2026 — 14,709 subscribers26 September 2026 — 14,736 subscribers4 October 2026 — 14,800 subscribers9 October 2026 — 14,821 subscribers13 October 2026 — 14,816 subscribers14 October 2026 — 14,812 subscribers18 October 2026 — 14,806 subscribers19 October 2026 — 14,803 subscribers20 October 2026 — 14,806 subscribers21 October 2026 — 14,801 subscribers22 October 2026 — 14,791 subscribers23 October 2026 — 14,790 subscribers24 October 2026 — 14,787 subscribers25 October 2026 — 14,790 subscribers26 October 2026 — 14,784 subscribers27 October 2026 — 14,786 subscribers28 October 2026 — 14,780 subscribers29 October 2026 — 14,764 subscribers30 October 2026 — 14,748 subscribers31 October 2026 — 14,742 subscribers1 November 2026 — 14,732 subscribers3 November 2026 — 14,735 subscribers4 November 2026 — 14,730 subscribers5 November 2026 — 14,728 subscribers6 November 2026 — 14,721 subscribers7 November 2026 — 14,716 subscribers8 November 2026 — 14,711 subscribers9 November 2026 — 14,701 subscribers14,7016 August 20269 November 2026
57 measurements spanning 95 days, net -105. 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 14,630–14,846 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 57
Measured (UTC)SubscribersChange
9 Nov 2026, 21:0914,701-10
8 Nov 2026, 14:3514,711-5
7 Nov 2026, 04:5114,716-5
6 Nov 2026, 06:5914,721-7
5 Nov 2026, 03:2914,728-2
4 Nov 2026, 01:2114,730-5
3 Nov 2026, 02:0714,735+3
1 Nov 2026, 23:1414,732-10
31 Oct 2026, 20:0814,742-6
30 Oct 2026, 19:5714,748-16
29 Oct 2026, 22:2714,764-16
28 Oct 2026, 22:3914,780-6
27 Oct 2026, 19:0014,786+2
26 Oct 2026, 21:5014,784-6
25 Oct 2026, 21:1014,790+3
24 Oct 2026, 20:2714,787-3
23 Oct 2026, 17:2014,790-1
22 Oct 2026, 16:5914,791-10
21 Oct 2026, 11:4714,801-5
20 Oct 2026, 07:2714,806first reading

Engagement

104 posts held, back to 28 July 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 67 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
9.68%
avg views ÷ 14,701 subscribers
Avg views / post
1,420
30 posts measured
Reaction rate
1.69%
reactions ÷ views · ER floor
Posts in window
30
of 104 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 10 November 2026
Posts held104 (28 July 2026 – 10 November 2026)
Views total42,683
Reactions total721
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken10 Nov 2026, 06:14 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
≈1,760
Videos
≈3,550
Links
≈888

Lifetime counters from Telegram’s own channel header, read 10 November 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
1h 15m
Average length
50s

Measured directly from 91 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

2,743 reactions across 104 posts, in 8 distinct kinds. The most used accounts for 61.8% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤1,69461.8%
🔥56120.5%
👍2559.30%
🥰732.66%
👏652.37%
🤩461.68%
🎉291.06%
😁200.729%

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 104 of the 104 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 2,743 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 104 most recent posts we hold, published 28 July 2026 to 10 November 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.

Telegram Stars

Stars received
2
across the posts below
Posts paid on
2
of 104 we hold a reading for · 2%
Most on one post
1
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @twinschinese. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.

Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.

This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.

Counted over the 104 most recent posts we hold for this entry, published 28 July 2026 to 10 November 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.

Recent posts

Telegram’s own ‘edited’ marker: 1 of 30 posts held from the last 30 days (3.33%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

10 Nov 2026, 05:02 UTC133 views13 reactionsread 10 November 2026
Video

Вот где я работаю☺️

👍6❤3🔥3🤩1

9 Nov 2026, 06:33 UTC593 views23 reactionsread 10 November 2026
Video

Эта китайская традиция согревает душу❤️ Сегодня по китайскому календарю Праздник холодной одежды 寒衣节. В этот день в Китае царит особая атмосфера. На улицах можно увидеть людей, которые аккуратно сжигают свертки с бумажными вещами — это целые «зимние коллекции» для тех, кого уже нет с нами. Меня всегда трогает эта глубокая, практичная забота, простирающаяся за пределы нашего мира. Это не про скорбь, а про непрерывн…

❤19👍3🔥1

8 Nov 2026, 14:32 UTC717 views23 reactionsread 10 November 2026
Forwarded from @twinschinese_lifeVideo

На церемонии открытия Тайбэйского кинофестиваля «Золотая лошадь» Посмотрели местное кино. Сама не знаю, чем впечатлилась больше: тем, что спокойно поняла диалект (адаптация прошла успешно😁) или что три часа смотрела историю про проклятья😅 Вы, кстати, верите в сглазы и проклятья? Благо, конец хороший и вывод такой: даже если эта штука существует, если постараться, все можно снять😄

❤18🔥3😁2

6 Nov 2026, 06:32 UTC≈1,390 views37 reactionsread 10 November 2026
Video

Как думаете, о чём Злата и Софья говорят в этом видео? 👀🇨🇳 Делитесь своими догадками в комментариях! ❤️

❤31🥰6

5 Nov 2026, 16:25 UTC≈1,390 views37 reactionsread 10 November 2026
Forwarded from @twinschinese_lifeVideo

ДЗ по каллиграфии😌

🔥32❤5

5 Nov 2026, 06:33 UTC≈1,530 views15 reactionsread 10 November 2026
Video

Все начинается с любви к учителю китайского языка😍 🎥Отрывок из интервью с Вероникой Муравицкой, экспертом по работе с госсектором платформы SkyEng

❤13🥰2

4 Nov 2026, 07:02 UTC≈1,480 views50 reactionsread 10 November 2026
Forwarded from @twinschinese_lifeVideo

4 месяца на Тайване, а новые места все не заканчиваются. Я больше не ищу привычные тропы, я каждый день отправляюсь в новый путь! 📍Водопад Улай

❤38🔥5🤩3👏2🥰2

4 Nov 2026, 06:32 UTC≈1,320 views14 reactionsread 10 November 2026
Video

Угадайте значение иероглифа по ключам😎

❤14

3 Nov 2026, 08:51 UTC≈1,270 views51 reactionsread 10 November 2026
Forwarded from @twinschinese_lifeVideo

Бесплатное молоко детям! По местным правилам, школьники могут еженедельно получать бесплатное молоко по студенческому. Соня к молоку равнодушна, а вот Злата его настолько любит, что пьет за двоих😄

❤35👍13🔥3

3 Nov 2026, 06:41 UTC≈1,370 views22 reactions1 Starread 10 November 2026
Video

Как думаете, о чём это видео? 🇨🇳 Хотите выучить китайский и говорить на нём как чемпион мира? 🏆 Начните с 4 бесплатных уроков по ссылке ниже 👇🏼 https://annakyzina.ru/?utm_source=knopka+tg

❤22

2 Nov 2026, 06:30 UTC≈1,440 views19 reactionsread 10 November 2026
Video

Трудность №1 для тех, кто изучает китайский

❤19

1 Nov 2026, 15:55 UTC≈1,330 views44 reactionsread 10 November 2026
Forwarded from @twinschinese_lifeVideo

Отчетный концерт Тайваньского университета искусств. Студенты показали высший класс👍 Я как всегда в восторге от китайского танца - пластика, гибкость и акробатика в одном флаконе 👏

🔥28❤13👍3

Showing the 12 most recent of 104 posts we hold for @twinschinese. 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.

Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.

Posts edited after publishing

@twinschinese edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.

An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.

First edit seen
25 August 2026
Most recent edit
25 August 2026

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 2 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.

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.

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

“Twins Chinese | Анна Кузина” (@twinschinese), 14,701 subscribers as measured 9 November 2026. Telegram Register, tgregister.com/channel/twinschinese.

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.