Channel
@ekaterina_nekami
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Cite this entry
506subscribers
+11 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001990661779 |
|---|---|
| Type | Channel |
| Username | @ekaterina_nekami |
| Created | Between 1 October 2023 and 30 April 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 September 2026 |
| Measurements held | 8 |
| Confirmed unchanged | 1 time, most recently 12 September 2026 |
| On Telegram | t.me/ekaterina_nekami |
Literature — 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 21 September 2026 and assigned it the closest of 31 fixed categories, at 99% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Sept 2026, 10:00 | 506 | +5 |
| 3 Sept 2026, 17:17 | 501 | -1 |
| 28 Aug 2026, 06:25 | 502 | -2 |
| 20 Aug 2026, 09:11 | 504 | +4 |
| 13 Aug 2026, 13:06 | 500 | +4 |
| 6 Aug 2026, 18:47 | 496 | no change |
| 6 Aug 2026, 13:30 | 496 | +1 |
| 6 Aug 2026, 05:01 | 495 | first reading |
11 posts held, back to 4 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 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 11 posts for this entry, the most recent from 6 August 2026. An engagement rate over an empty window would be a number about nothing.
172 reactions across 10 posts, in 8 distinct kinds. The most used accounts for 39.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤🔥 | 68 | 39.5% | |
| ❤ | 42 | 24.4% | |
| 🔥 | 30 | 17.4% | |
| 😍 | 20 | 11.6% | |
| 🥰 | 6 | 3.49% | |
| 💘 | 4 | 2.33% | |
| custom 5298620403395074835 | 1 | 0.581% | |
| 🍓 | 1 | 0.581% |
Custom emoji. One row above is a Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The count beside it is Telegram’s.
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 11 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 172 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 11 most recent posts we hold, published 4 August 2026 to 6 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.
📖📖📖 📖📖📖🤩Он много раз играл героя романтических историй и знал все сюжетные шаблоны для пар. Вот только, как оказалось, в реальной жизни эти знания не помогают. Особенно когда ты хочешь, чтобы все было правильно.🤩 // #нецелуйзлодея
❤7
Ой, да ладно, а я думала вам моё эпическое фентези не зашло…
❤7
🔸Рубрика #АльбомМирыЧон 🔸 🔤🔤 🔤🔤🔤🔤🔤 😈😈😈😈😈😈❗ Мама Наён — Мира Чон фанатка дорам и любит собирать вырезки из журналов про любимых звёзд. В честь выхода книги «Не целуй злодея!», Мира решила собрать анкеты всех героев. Нам Джунхён — яркая звезда дорам, прежде прятавшаяся в тени Ли Тэхо. У них сложные отношения, от конкуренции и противостояния, до дружбы. Сам он трудяга много сделавший, чтобы подняться…
❤🔥11💘4😍4
Posted without readable text
Я уже говорила что у моего писательского канала биполярка:) То дорамный ромком то эпичная кровяка. Про что пишем следующую зарисовку? Или текущую продолжаем? ❤️ магический реализм и городское фентези в стиле аниме ( и хочется и колется, писать тяжелое, но с японскими школьниками) 🔥 попытаться любимую нами #криминалAU с Кеем и Эйр превратить в азиатский сериал, типа Х-файлс или девятых врат. 😍 Дарк фентази в стил…
❤🔥7
"Не целуй злодея!", Екатерина Неками Легкий, атмосферный роман о съемках дорамы, закулисье киноиндустрии с детективной линией. Ли Тэхо — популярный актёр, которого зрители привыкли видеть в образах идеальных романтических героев. После длительного вынужденного перерыва он решает вернуться на экраны и доказать, что способен на более сложные роли. Ли Тэхо дают роль Роана — харизматичного и жестокого злодея из культов…
❤6🔥5❤🔥3🥰3
⭐️⭐️Я обещала вам #зарисовканеками ⭐️⭐️В голосование выиграло Дарк фэнтези. У меня, правда больше эпик вышел. Не уверенна в стиле как будто пока тяжеловесно и без достаточного погружения. И названия и имена я бы ещё поменяла. Нооо в целом знакомьтесь #принцессыАрвелла Глава 1. Принцесса в башне (часть 1) Они пришли ночью, Риверстейн никогда не являлся крепостью. Центральный замок был создан, чтобы открывать свои две…
❤🔥11
🔸На какую дораму похож? 🔸 🔤🔤 🔤🔤🔤🔤🔤 😈😈😈😈😈😈❗ Если бы героиня Мун Га Ён из «Истинной красоты» встретила Сон Джэ, мы бы получили «Не целуй злодея!» Из корейских ромкомов я очень люблю «Хватай Сон Джэ и беги» и «Истинную красоту». Наён во многом похожа на героиню «Истинной красоты» — она за своими недостатками не видит красоты. Тэхо же вдохновлен Сон Джэ и его депрессией и одиночеством не смотря на п…
❤🔥25🔥18😍16
🤩🤩🤩Ой, как мне нравится подборка цитат от LB и то что меня как автора форзаца указали тоже)
❤9
А что, если актёр, которого ты терпеть не можешь, вдруг окажется идеальным злодеем? «Не целуй злодея!» Екатерина Неками — новинка для всех, кто любит корейские дорамы, закулисье съёмок и романтику с искрами 🔥 Чон Наён уверена: Ли Тэхо совершенно не подходит на роль её любимого книжного антагониста. Но судьба распоряжается иначе, и теперь им приходится работать вместе. Чем больше времени они проводят рядом, тем сло…
❤🔥10❤5
🦕💧 🦋💧👜🐻🍩 ⌛👜🪶🧸💧💦‼ Я устал играть то хорошего парня, то злодея рядом с тобой. Разреши мне просто быть собой — идиотом, который говорит и делает что-то невпопад и влюбляется во все вещи, связанные с тобой. Дай мне право на ошибки и на прощение за них. 💙 Екатерина Неками 💓 Пу, пуньк, пупуньк #пеннаяэстетика #нецелуйзлодея
❤8🔥7🥰3❤🔥1🍓1custom 52986204033950748351
Showing the 11 most recent of 11 posts we hold for @ekaterina_nekami. 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
Named by 15 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.
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 12 September 2026 — this entry's latest reading, not the date you are reading this.
“nekami пишет| автор ЭКСМО/LikeBook” (@ekaterina_nekami), 506 subscribers as measured 12 September 2026. Telegram Register, tgregister.com/channel/ekaterina_nekami.
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.