☕️ SPILL THE TEA ☕️ Урок про плітки, gossip та поширення інформації — warm-up, тематична лексика, відео, робота з текстом і багато speaking. 💬 B2 💙 Що всередині? 🩷 Warm-up: вступне обговорення та інтерактив ☕️ 🩷 Are you a Gossip Queen/King?: тест на схильність до пліток 👑 🩷 Survival Vocabulary: картки з лексикою, сортування та вправи на підстановку 📚 🩷 Video: відео "Why We Love to Gossip" + Lead-in, True/False, Mat…

Channel
Dianelle | tutor | 🇺🇸 🇸🇰
@dianelle_tutor
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
1,802subscribers
-10 since we began measuring on 11 September 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
Register entry
| Telegram ID | -1003089726459 |
|---|---|
| Type | Channel |
| Username | @dianelle_tutor |
| Created | Between 1 August 2025 and 31 October 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 September 2026 |
| Last confirmed live | 16 September 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 16 September 2026 |
| On Telegram | t.me/dianelle_tutor |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 16 Sept 2026, 21:40 | 1,802 | -8 |
| 13 Sept 2026, 01:00 | 1,810 | -2 |
| 11 Sept 2026, 20:22 | 1,812 | no change |
| 11 Sept 2026, 19:47 | 1,812 | first reading |
Engagement
20 posts held, back to 15 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.
- ERR · 30 days
- 26.3%
- avg views ÷ 1,802 subscribers
- Avg views / post
- 475
- 7 posts measured
- Reaction rate
- 1.76%
- reactions ÷ views · ER floor
- Posts in window
- 7
- of 20 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 5 of 7 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 3 September 2026 |
|---|---|
| Posts held | 20 (15 August 2026 – 3 September 2026) |
| Views total | 3,322 |
| Reactions total | 48 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 11 Sept 2026, 20:22 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.
Reaction mix
94 reactions across 13 posts, in 4 distinct kinds. The most used accounts for 58.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 55 | 58.5% | |
| custom 5256053622474553432 | 21 | 22.3% | |
| 🥰 | 13 | 13.8% | |
| 🔥 | 5 | 5.32% |
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 13 of the 20 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 94 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 15 August 2026 to 3 September 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
всім дякуємо за розсилку ❤️
❤1
guys! о 14 матеріали будуть у вас 🫰🏻
🤩 А ось і огляд на мій урок із безкоштовної розсилки! SPILL THE TEA ☕️ Level: B1–B2 | Individual lesson Що всередині? 🟣Warm-up 🟣Gossip Queen / King? 🟣Survival Vocabulary 🟣Use the Words 🟣Watching 🟣True or False 🟣Matching 🟣Speaking 🟣Homework урок допоможе поговорити про плітки та gossip, розширити словниковий запас на тему, попрактикувати listening & speaking та обговорити, чи завжди варто ділитися чужими секретами…
🥰6❤4🔥2
певний відсоток охочих не натискає done після того як підписались на авторів будьте уважні, тисніть DONE 🐰
custom 52560536224745534323❤2🥰1
Повноцінний осінній урок, повністю побудований за принципами лексичного підходу від @teacher’s_space Замість сухого заучування слів студент спостерігає за мовою в контексті, аналізує ідіоматичні вирази, відпрацьовує їх до автоматизму та виходить у вільний сторітелінг. Боже, ви тільки подивіться на це наповнення🐱... і цей урок ви отримуєте БЕЗКОШТОВНО?!🥹 для того щоб отримати виконайте умови тут 🤩
🥰4❤2custom 52560536224745534321
БЕЗКОШТОВНА РОЗСИЛКА | LEXICAL BOOST 🌟 6 міро-бордів та 1 презентація 🤩 🌟Minecraft (At Home) pre-A1/ A1 🌟Things teenagers are obsessed with A2 🌟News&Media A2/B1 🌟English you actually hear in movies A2+/B1 🌟Turn over a new leaf B1/B2 🌟Gossips B1+/B2 🌟Why do we cringe at our old selves? B2 як отримати ці матеріали? 🌟потрібно лишити реакцію на пост 🌟перейти в бот і виконати легкі умови 🌟в кінці натиснути кнопку "done
custom 525605362247455343213❤8🔥1
⚡️ TOXIC PRODUCTIVITY 🧠 Урок про токсичну продуктивність, вигорання й здорове ставлення до відпочинку — з відео BBC, лексикою та глибокою дискусією. 🌿 B2 💚 Що всередині? 🟢 Warm-up: асоціації та mind-map 🧩 🟢 Vocabulary: 13 слів + matching, symptoms & interventions 📚 🟢 Fill in the gaps: 12 речень із новою лексикою ✍️ 🟢 Watching: відео BBC + True/False 🎥 🟢 Discussion: 8 питань про вигорання та відпочинок 🗣 🟢 Homework:…
☕️ THE WORLD'S FAVORITE DRUG (Caffeine) 🧠 Урок про кофеїн — читання наукового тексту, robота з фактами, критичне мислення та лексика на тему звичок і залежностей. 📖 B2 💙 Що всередині? ⚪️ Warm-up про типи залежностей (technology, food, caffeine) 🎮 ⚪️ Pre-reading: вміст кофеїну в продуктах + прогноз 📊 ⚪️ Reading "The World's Favorite Drug" про вплив кофеїну 🔬 ⚪️ Comprehension: multiple choice, matching 🧩 ⚪️ Reading s…
❤2
🎶 ORDINARY — Alex Warren 🕊 Урок на основі пісні "Ordinary" — поетична лексика про кохання, релігійні метафори та глибокий emotional vocabulary. 💗 B2-C1 🎀 Що всередині? ❤️ Warm-up з відео + фото-стимули про глибоке кохання 🎬 ❤️ Vocabulary: holy water, sanctuary, altar, heavenly та ін. 📚 ❤️ Reading/listening stories "You Made Life Sacred" та "The Way You Changed Me" 🎵 ❤️ Rephrase task — переказ рядків пісні своїми сл…
❤2
💘 LOVE IDIOMS & EXPRESSIONS 💕 Урок про англійські ідіоми та вирази про кохання — лексика, читання коротких історій і жвава дискусія про стосунки. 🌹 B2-С1 ❤️ Що всередині? ❤️ Warm-up з відео "The look of love in film" + фото-стимули 🎬 ❤️ Vocabulary: head over heels, pop the question, tie the knot та ін. 💐 ❤️ Reading "When the Stars Aligned" та "The Rhythm of Love" з fill in the gaps 📖 ❤️ Rephrase task — переказ рече…
💕 SUMMER LOVE & FRIENDSHIP ☀️ Урок про літню романтику, дружбу та зраду — з захопливою історією про кохання, зраду й підтримку справжніх друзів. 💔 B2 ❤️ Що всередині? 🩷 Warm-up про літні звички та якості справжнього друга 🌊 🩷 Vocabulary: intrigue, smitten, chemistry, betrayal та ін. 📚 🩷 Reading "The Summer Intrigue" — історія про Софі, Емму, Лізу й Марка 📖 🩷 Post-reading: True/False, comprehension, vocabulary in co…
Showing the 12 most recent of 20 posts we hold for @dianelle_tutor. 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
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.
Mentions
Named by 3 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.
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 16 September 2026 — this entry's latest reading, not the date you are reading this.
“Dianelle | tutor | 🇺🇸 🇸🇰” (@dianelle_tutor), 1,802 subscribers as measured 16 September 2026. Telegram Register, tgregister.com/channel/dianelle_tutor.
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.