What kind of books are you looking for? Comment below ⬇️
❤1

Channel
@EnglishNovels_Classic
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
22,726subscribers
+556 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1001252170216 |
|---|---|
| Type | Channel |
| Username | @EnglishNovels_Classic |
| 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 | 19 September 2026 |
| Measurements held | 33 |
| Confirmed unchanged | 1 time, most recently 19 September 2026 |
| On Telegram | t.me/EnglishNovels_Classic |
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 10 September 2026 and assigned it the closest of 31 fixed categories, at 65% 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 |
|---|---|---|
| 19 Sept 2026, 15:37 | 22,726 | +56 |
| 17 Sept 2026, 04:15 | 22,670 | +15 |
| 15 Sept 2026, 02:17 | 22,655 | +21 |
| 13 Sept 2026, 09:00 | 22,634 | +16 |
| 11 Sept 2026, 08:56 | 22,618 | +56 |
| 8 Sept 2026, 17:36 | 22,562 | +69 |
| 5 Sept 2026, 07:54 | 22,493 | +60 |
| 3 Sept 2026, 10:57 | 22,433 | +10 |
| 2 Sept 2026, 05:04 | 22,423 | +8 |
| 1 Sept 2026, 01:36 | 22,415 | +9 |
| 31 Aug 2026, 01:18 | 22,406 | +35 |
| 29 Aug 2026, 22:14 | 22,371 | +18 |
| 28 Aug 2026, 19:14 | 22,353 | -6 |
| 27 Aug 2026, 21:36 | 22,359 | +5 |
| 26 Aug 2026, 20:58 | 22,354 | +15 |
| 25 Aug 2026, 22:45 | 22,339 | +8 |
| 25 Aug 2026, 01:25 | 22,331 | -3 |
| 23 Aug 2026, 14:44 | 22,334 | +21 |
| 21 Aug 2026, 22:09 | 22,313 | +22 |
| 20 Aug 2026, 19:17 | 22,291 | first reading |
41 posts held, back to 5 October 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 56 pages 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 27 August 2026 |
|---|---|
| Posts held | 41 (5 October 2025 – 27 August 2026) |
| Views total | 2,410 |
| Reactions total | 1 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 3 Sept 2026, 12:14 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.
Measured directly from 10 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.
321 reactions across 38 posts, in 11 distinct kinds. The most used accounts for 81.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 260 | 81.0% | |
| 👍 | 30 | 9.35% | |
| 🔥 | 12 | 3.74% | |
| 👏 | 7 | 2.18% | |
| 🥰 | 4 | 1.25% | |
| 👎 | 2 | 0.623% | |
| 💯 | 2 | 0.623% | |
| 🍓 | 1 | 0.312% | |
| 👌 | 1 | 0.312% | |
| 💋 | 1 | 0.312% | |
| 😭 | 1 | 0.312% |
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 38 of the 41 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 321 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 41 most recent posts we hold, published 5 October 2025 to 27 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.
What kind of books are you looking for? Comment below ⬇️
❤1
💰🧠 The Psychology of Selling 🧠💰 📈 “Top salespeople think differently — and that’s what makes them successful.” 💡 🎯 Master the art of persuasion 🗣️ Build trust & confidence 💪 Overcome objections 🚀 Close more deals with ease 📖 Brian Tracy’s bestseller reveals the mindset and methods behind every great salesperson. It’s not just about selling products — it’s about understanding people and helping them get what they t…
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🐺 OQ BO'RILAR 🇺🇿 Bu oddiy qo'shiq emas... Bu O'zbekiston futbolining ruhi! ⚽️🔥 🎥 Videoni ko'rish uchun bosing: 👉 https://www.youtube.com/watch?v=p5KThyGlDpM https://www.youtube.com/watch?v=p5KThyGlDpM https://www.youtube.com/watch?v=p5KThyGlDpM Fikringizni kutamiz! 🇺🇿🐺
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Just like other self-help books, this one also shares tips and tricks for becoming a mentally strong person, interwoven with life stories of famous and ordinary people, both past and present. What I really enjoyed most were life stories—the ups and downs, the successes and failures. The author skillfully portrays the stories of the patients and then provides explanations, followed by helpful and not-so-helpful tips f…
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🧠 Book Recommendation Behave: The Biology of Humans at Our Best and Worst by Robert Sapolsky 📘🧬 Why do we do what we do? Is it brain chemistry, upbringing, or society? 🤔 This groundbreaking book explores human behavior from neurons to nations. 🧪🌍 🔍 Dive into: 🧬 Neuroscience & hormones 🕰 Evolutionary history 💣 Violence & compassion 👥 Morality & decision-making Whether you're curious about the roots of kindness or t…
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💔 Book Recommendation Ugly Love by Colleen Hoover ✈️🌉 Get ready for a story that will break your heart... and then heal it. 💙 When Tate meets Miles, it's not love at first sight. It's an arrangement. No promises. No emotions. Just passion.🔥 But what happens when hearts get involved? 😢💏 📚 Why you should read it: 💥 Emotional rollercoaster 💬 Deep, raw storytelling 👩❤️👨 Intense chemistry 😢 A love story you’ll never f…
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Nega yangi so‘zlar 1 soatdan keyin esdan chiqadi? (Miya siri) TOMOSHA QILISH: FULL VIDEO (iltimos bizni qo'llab quvvatlang)
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😴📖 The Art of Laziness ✨ What if laziness isn’t always bad? 🤔 This book shows how slowing down, resting, and choosing the easy but smart way can actually make life more productive and enjoyable. 🌿 💡 Key ideas: ✔️ Laziness = efficiency when done right ✔️ Rest is not wasted time, it’s recharging 🔋 ✔️ Sometimes doing less achieves more 🎯 A light, witty, and refreshing read that makes you rethink the pressure of “alway…
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Foydalanib ko'ring!
🏓 Endi ingliz tilini o‘rganish yanada oson 😎 📩 So‘z yuboring va darhol oling: ⏺ Tarjima ⏺ Sinonim ⏺ Antonim ⏺ Talaffuz ⏺ Writing Checker 🎩 Har bir so‘z — yangi imkoniyat! Bugun boshlang, ertaga bemalol gapiring 💬✨ 🎉 Sinab ko‘rishga tayyormisiz? 🤖 @wordyuzbot 🤖 @wordyuzbot 🤖 @wordyuzbot
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🍿 Botga yangi film joylandi! 🎞 Film haqida: 🎥: The Shadow's Edge {2025} New Movie Update 🔔 💬 Summaries: Macau Police brings the tracking expert police officer out of retirement to help catch a dangerous group of professional thieves. @movies_XXI 🔢 Yuklash kodi: 1 ‼️ Bot manzili: @EnglishMovieskinobot ❗ Diqqat quyidagi tugmani bosish orqali filmni olasiz.
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Psycho-Cybernetics by Maxwell Maltz explains that the mind functions like a self-regulating system, or servo-mechanism, and that a person's self-image is the key to achieving success and happiness. By using techniques to develop a positive self-image and reprogram negative thought patterns, individuals can guide their minds toward positive goals and desired outcomes in life. The book combines psychological principles…
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Showing the 12 most recent of 41 posts we hold for @EnglishNovels_Classic. 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.
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 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.
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.
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 20 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 16 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 19 September 2026 — this entry's latest reading, not the date you are reading this.
“English Novels” (@EnglishNovels_Classic), 22,726 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/EnglishNovels_Classic.
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.