17 measurements spanning 19 days, net -25. 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 23,126–23,171 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
24 Aug 2026, 22:17
23,131
-8
23 Aug 2026, 05:24
23,139
-1
21 Aug 2026, 20:16
23,140
-4
20 Aug 2026, 17:24
23,144
+4
19 Aug 2026, 18:45
23,140
-8
18 Aug 2026, 16:22
23,148
+1
17 Aug 2026, 16:17
23,147
-6
14 Aug 2026, 20:17
23,153
-7
13 Aug 2026, 12:06
23,160
-1
11 Aug 2026, 13:06
23,161
-5
10 Aug 2026, 12:27
23,166
+2
9 Aug 2026, 09:12
23,164
+2
8 Aug 2026, 08:49
23,162
+5
7 Aug 2026, 10:00
23,157
-2
6 Aug 2026, 12:41
23,159
+3
6 Aug 2026, 02:03
23,156
no change
6 Aug 2026, 00:28
23,156
first reading
Engagement
36 posts held, back to 14 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 40 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
5.78%
avg views ÷ 23,131 subscribers
Avg views / post
1,340
26 posts measured
Reaction rate
1.77%
reactions ÷ views · ER floor
Posts in window
26
of 36 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
Window
Rolling 30 days · latest post in window 24 August 2026
Posts held
36 (14 July 2026 – 24 August 2026)
Views total
34,735
Reactions total
614
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
25 Aug 2026, 08:52 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
≈2,700
Videos
≈347
Links
≈1,450
Lifetime counters from Telegram’s own channel header, read 25 August 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
25s
Average length
13s
Measured directly from 2 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
1,082 reactions across 36 posts, in 10 distinct kinds. The most used accounts for 30.7% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
332
30.7%
👍
315
29.1%
❤
285
26.3%
😁
97
8.96%
🥰
26
2.40%
💯
13
1.20%
😍
7
0.647%
🤔
5
0.462%
🎉
1
0.092%
🤮
1
0.092%
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 36 of the 36 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 1,082reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 36 most recent posts we hold, published 14 July 2026 to 24 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.
Telegram Stars
Stars received
43
across the posts below
Posts paid on
23
of 36 we hold a reading for · 64%
Most on one post
11
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @ProProfiIing. 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 36 most recent posts we hold for this entry, published 14 July 2026 to 24 August 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.
3. Кто оказался особенно хорошим оценщиком?
Из большого числа протестированных характеристик уверенно выделились лишь три.
- Интеллект самого оценщика. Чем выше был интеллект участника, тем точнее он оценивал интеллект других. То есть гипотеза «умные люди лучше распознают умных людей» получила подтверждение.
- Способность распознавать эмоции. С этим параметром связь чуть слабее, но тоже умеренная. Авторы посчитали…
МОЖНО ЛИ ОЦЕНИТЬ ИНТЕЛЛЕКТ ЧЕЛОВЕКА ПО МИНУТНОМУ ВИДЕО?
И КАК ЭТО СДЕЛАТЬ ПРАВИЛЬНО?
В журнале Intelligence вышла статья Christoph Heine и коллег — “The good judge of intelligence”, посвященная тому, можно ди это сделать.
Сама идея исследования достаточно проста и при этом важна для профайлинга и социальной перцепции.
Мы постоянно оцениваем интеллект других людей: на собеседованиях, переговорах, совещаниях, в обуч…
Вечерняя пятничная иллюзия.
Постепенно заканчивается лето, но тем, кому надо, еще можно успеть поносить такие футболки))
Желаю всем отличных выходных, и только приятных и практичных иллюзий)
#мозг, #обманзрения, #восприятие, #классика, #неоднозначность, #иллюзии, #креатив, #профайлинг, #ProProfiling, #Филатов
Также приглашаем вас на классический курс по OSINT
⚡ С 7 по 11 сентября компания Безопасность 360" приглашают на традиционный осенний поток обучения по Комплексной программе "Специалист по OSINT".
Программа состоит из 3 модулей, которые ведут разные преподаватели:
➡️ 7 и 8 сентября - Андрей Масалович (Кибердед) проведет модуль "Методы OSINT для решения задач поиска и анализа информации". Здесь дается обзор возмож…
Друзья, пара важных анонсов на начало сентября!
⚡10-11 сентября компания «Безопасность 360» приглашает вас на специализированный тренинг для сотрудников финансовых организаций "Противодействие социальной инженерии в финансовом секторе".
Наиболее предпочтительный формат - очное обучение, но возможно и онлайн-участие.
✅ Цель обучения - дать сотрудникам КФО, работающими непосредственно с клиентами, знания и техники п…
Лидерство и иллюзия справедливости
Одной из наименее освещаемой стороной профайлинга являются технологии определения глубинных убеждений и установок, во многом определяющих нашу социальную жизнь и успех. Сегодня предлагаю поисследовать наши установки на амбициозность, успех и лидерство.
«Почему меня не повышают? Ведь все знают, что я хорошо работаю!». Фактически это перефразированные слова Николая Ростова из романа…
Причём этот результат сохранялся и тогда, когда авторы пересчитывали модели с поправкой на надёжность измерений, и когда анализировали только американские и европейские выборки отдельно. То есть вывод оказался устойчивым к разным способам анализа.
Почему мы так переоцениваем стабильность личности?
Авторы обсуждают несколько причин.
Во-первых, в культуре широко распространена идея постоянного «истинного Я» — некой …
ЛИЧНОСТЬ МЕНЯЕТСЯ СИЛЬНЕЕ, ЧЕМ МЫ ДУМАЕМ
Напрягу вас еще одним лонгридом)
В Communications Psychology две недели назад вышла интересная статья Amanda J. Wright и коллег — “Личностные качества менее стабильные, чем мы думаем”. Авторы проверили, насколько представления людей о стабильности личности совпадают с реальными многолетними данными.
Главный вывод: мы систематически переоцениваем стабильность личностных черт.…
🔹Замечали, что попытки договориться с человеком или замотивировать его иногда бьют мимо цели?
Один ждет четких пошаговых регламентов и теряется в неопределенности.
Второго вдохновляют масштабные перспективы, а третьего включает в работу только риск потерять стабильность.
Кто-то опирается исключительно на свое мнение, а кому-то жизненно необходимо внешнее подтверждение.
Причина не в характере «не сошлись», а в мета…
ИИИ традиционная пятничная иллюзия.
Посмотрите на синие точки. И, если вы будете спокойно дышать, то при вдохе они будут двигаться в одну сторону, а при выдохе – в другую.
Всем здорового дыхания и отличных первых впечатлений на выходных
#мозг, #обманзрения, #восприятие, #классика, #неоднозначность, #иллюзии, #креатив, #профайлинг, #ProProfiling, #Филатов
4. Компетентность и теплота: что важнее?
Средняя сила связи у них оказалась почти одинаковой:
— компетентность — ρ = 0,38–0,47;
— теплота — ρ = 0,41–0,47.
Исследование не показывает, что компетентность существенно важнее «теплоты». То есть можно быть компетентным, но холодным… или некомпетентным и хорошим парнем… И не особо ясно, что из этого более значимо. В цифрах - эффект выражен сопоставимо.
Разница в другом:…
ЭФФЕКТ ПЕРВОГО ВПЕЧАТЛЕНИЯ НА РАБОТЕ: что показывает крупный метаанализ?
В Personnel Psychology пару недель назад вышел крупный метаанализ о влиянии первого впечатления на эффективность в рабочей среде. Прекрасное исследование: тем, кто занимается рекрутментом и оценкой персонала рекомендую почитать оригинал.
Авторы объединили 204 независимые выборки из 145 исследований и изучили четыре вопроса:
1. из чего складыва…
👍13🔥8❤1🤔1🥰1
Signed Aleksei Filatov
Showing the 12 most recent of 36 posts we hold for @ProProfiIing. 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.
Citation-graph rank
Citation-graph rank — 304,028 of 1,602,822entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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 4 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.
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.
Appears in Telegram’s recommendations for other channels
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.
Психология @PsyEducation · 59,550 Telegram ranks this channel #17 of 95 here — alongside 94 others — read 22 August 2026
Татьяна Черниговская @tvchernigovskya · 110,567 Telegram ranks this channel #64 of 90 here — alongside 89 others — read 20 August 2026
This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 24 August 2026 — this
entry's latest reading, not the date you are reading this.
“Профайлинг, нейротехнологии и детекции лжи” (@ProProfiIing), 23,131 subscribers as measured 24 August 2026. Telegram Register, tgregister.com/channel/ProProfiIing.
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.
