Politics & activism — 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 98% 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 4 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.
No sample pairs were kept for this entry. The counts below were measured, but without the pairs there is nothing here for you to open and check, so treat the relationship as recorded rather than demonstrated.
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
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. None of the 0 matches recorded here fall after that date, so for this entry we cannot say whether any of them carried a credit. The duplication is measured; the absence of attribution is not.
“Published first” means first in this corpus. We hold 81 comparable posts for this entry, running 24 July 2026 to 14 November 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 4 channels, the one whose matching posts we hold earliest is @ETAgenda — which is this entry. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_source, last confirmed 15 November 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 4 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.
65 measurements spanning 99 days, net -675. 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 15,565–16,442 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 65
Measured (UTC)
Subscribers
Change
13 Nov 2026, 10:18
15,666
-32
10 Nov 2026, 18:50
15,698
-10
9 Nov 2026, 07:16
15,708
-2
8 Nov 2026, 00:34
15,710
-6
6 Nov 2026, 23:28
15,716
-6
5 Nov 2026, 23:24
15,722
+2
3 Nov 2026, 19:45
15,720
-3
2 Nov 2026, 22:56
15,723
-7
1 Nov 2026, 21:36
15,730
-10
31 Oct 2026, 21:20
15,740
-4
30 Oct 2026, 20:46
15,744
-11
29 Oct 2026, 23:41
15,755
-15
28 Oct 2026, 21:40
15,770
-10
27 Oct 2026, 21:31
15,780
-2
26 Oct 2026, 20:39
15,782
-8
25 Oct 2026, 19:39
15,790
-10
24 Oct 2026, 21:49
15,800
+1
24 Oct 2026, 00:45
15,799
-7
22 Oct 2026, 23:49
15,806
-5
21 Oct 2026, 18:46
15,811
first reading
Engagement
84 posts held, back to 24 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 80 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
5.47%
avg views ÷ 15,666 subscribers
Avg views / post
857
55 posts measured
Reaction rate
2.63%
reactions ÷ views · ER floor
Posts in window
55
of 84 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 15 November 2026
Posts held
84 (24 July 2026 – 15 November 2026)
Views total
47,113
Reactions total
1,238
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
15 Nov 2026, 19:09 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
≈1,420
Videos
≈3,470
Links
≈9,570
Lifetime counters from Telegram’s own channel header, read 15 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
4h 06m
Average length
3m 10s
Measured directly from 78 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
1,904 reactions across 84 posts, in 26 distinct kinds. The most used accounts for 42.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
💯
809
42.5%
🔥
187
9.82%
😈
142
7.46%
👍
133
6.99%
❤
100
5.25%
🤡
87
4.57%
🤮
75
3.94%
💩
50
2.63%
🖕
50
2.63%
🙏
41
2.15%
🤔
40
2.10%
🤣
38
2.00%
🤬
37
1.94%
👎
24
1.26%
👀
23
1.21%
😡
13
0.683%
🤯
10
0.525%
🥱
9
0.473%
😱
8
0.42%
😢
6
0.315%
6 further kinds
22
1.16%
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 84 of the 84 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 1,904 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 84 most recent posts we hold, published 24 July 2026 to 15 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.
Recent posts
Telegram’s own ‘edited’ marker: 0 of 55 posts held from the last 30 days (0%) 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).
Whitney Webb draws attention to a 2020 simulation exploring how a massive cyber attack could lead to both the cancellation of an election and martial law, questioning why such outcomes were being simulated.
"They basically, through that simulation, established what types of hacks could succeed in achieving the declaration of martial law and the cancellation of a U.S. presidential election."
"So why are they doing t…
"It's very exciting."
Speaking at the World Economic Forum in 2017, Bill Gates "predicted" that a pandemic, "either naturally-caused or intentionally-caused", would cause 10 million excess deaths.
He also expressed excitement about the ability of mRNA technology to accelerate vaccine development once such a pandemic occurred.
Sure enough, everything unfolded exactly in line with his "predictions" just three years …
In light of Bill Gates' recent comments about the "necessity" of a catastrophic AI cyberattack, it's worth remembering that he has a habit of "predicting" massive global crises just before they occur, in exactly the same manner he "warned us about".
I edited this video during the peak of Covid tyranny in early 2021.
Follow @RealWideAwakeMedia for more content like this!
Merch: https://wideawake.clothing
X | YT | …
Bill Gates compares AI to an alien invasion requiring cooperation between the US and China.
He argues that national governments are failing to respond adequately and calls for new global institutions to regulate the technology.
"Some movies talk about, when the aliens are coming, that China and the US cooperate in this unprecedented way to deal with that threat."
"That's exactly the situation we're in."
Follow @R…
When Bill Gates is unable to contain his smirk as he explains why a catastrophic AI cyberattack may be "necessary", while wearing the Agenda 2030 logo on his sweater, you can be absolutely CERTAIN they are planning a massive false flag.
Source
Follow @RealWideAwakeMedia for more content like this!
Merch: https://wideawake.clothing
X | YT | IG | Rumble
"MAHA is a hostile entity."
Shannon Joy describes MAHA as a captured movement that provides cover for Big Pharma and Big Tech while mRNA technology continues to expand. She notes that its corporate-backed summit excluded vaccine-injury families and maintains that the movement was compromised from the outset.
Source
Follow @RealWideAwakeMedia for more content like this!
Merch: https://wideawake.clothing
X | YT | …
Neil Oliver notes that Western governments are attempting to revive the patriotic fervour of the World Wars to mobilise populations for another conflict. He argues that decades of declining public trust have rendered these appeals ineffective, as citizens increasingly recognise political manipulation.
"You can't play a conjuring trick on people who know that they are watching a stage magician."
Source
Follow @Real…
Journalist Alex Newman warns that global elites are preparing to orchestrate a catastrophic "cyber pandemic".
Drawing parallels with pre-Covid pandemic exercises, he argues that the resulting crisis will provide a convenient pretext to justify war, financial asset seizures and mandatory digital ID, eliminating online anonymity.
"They've been plotting on this for a very long time."
"It would be very easy to take so…
Political commentator Ralph Schoellhammer argues that Western leaders insult their own populations through their rhetoric on migration, only to demand patriotism when preparing for war. He predicts widespread resistance to conscription, warning that governments cannot restore national loyalty on demand.
"We were told that patriotism is bad, nationalism is bad, loyalty is bad. And now they all of a sudden want that y…
"It absolutely was a psyop."
Shannon Joy argues that MAHA was a coordinated psychological operation designed to divert attention from Covid vaccine mandates and medical freedom towards food policy. She maintains that the movement has enabled the expansion of mRNA technology while chemical and technology companies gain additional liability protections.
Source
Follow @RealWideAwakeMedia for more content like this!
…
Neil Oliver argues that Western governments have abandoned their obligations to citizens, allowing living standards and public trust to deteriorate. He maintains that the resulting unrest is being deliberately provoked to justify crackdowns and divert populations towards war.
"It's becoming more and more apparent that the governing class have abandoned any notion of a social contract, largely because they simply dis…
"Absolutely nothing has been done about mRNA. In fact, it's been expanded."
Dr. Mary Talley Bowden argues that RFK Jr. rose to prominence by challenging the Covid response, but that MAHA shifted its focus from vaccines to food following Calley Means' arrival. She warns that mRNA vaccines are expanding despite unresolved concerns over safety, accountability and informed consent.
"We now have [mRNA] in flu shots. We …
💯22😢3👍1🤬1😈1🙏1
Signed Travis Bickle
Showing the 12 most recent of 84 posts we hold for @ETAgenda. 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.
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 1 registered channel — 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.
Wide Awake Media - Official Channel @realwideawakemedia · 36,382 Telegram ranks this channel #3 of 84 here — alongside 83 others — read 1 September 2026
Covid Truth Network @covidtruthnet · 57,288 Telegram ranks this channel #3 of 83 here — alongside 82 others — read 22 August 2026
Mikki Willis Official @OfficialPlandemic · 32,513 Telegram ranks this channel #5 of 85 here — alongside 84 others — read 4 September 2026
Dr. Simon @goddek · 38,187 Telegram ranks this channel #8 of 87 here — alongside 86 others — read 30 August 2026
Steve Kirsch Channel @stkirsch · 18,787 Telegram ranks this channel #10 of 88 here — alongside 87 others — read 3 October 2026
Zeee Media 🎙 @zeeemedia · 53,459 Telegram ranks this channel #13 of 84 here — alongside 83 others — read 23 August 2026
Dr David Martin @DrDavidMartin · 49,721 Telegram ranks this channel #30 of 82 here — alongside 81 others — read 25 August 2026
World Council for Health @wch_org · 33,899 Telegram ranks this channel #39 of 87 here — alongside 86 others — read 2 September 2026
Dr Mike Yeadon solo channel @DrMikeYeadonsolochannel · 25,916 Telegram ranks this channel #40 of 89 here — alongside 88 others — read 13 September 2026
COVID-19 Up @COVID19Up · 25,430 Telegram ranks this channel #42 of 79 here — alongside 78 others — read 13 September 2026
Reiner Fuellmich 🇺🇸/🇬🇧/🇦🇺 @ReinerFuellmichEnglish · 83,207 Telegram ranks this channel #47 of 91 here — alongside 90 others — read 18 August 2026
Robin Monotti @robinmg · 66,827 Telegram ranks this channel #48 of 90 here — alongside 89 others — read 20 August 2026
Dr. Lynn Fynn’s Science Enlightenment Channel & Stuff @DrLynnFynn · 23,843 Telegram ranks this channel #53 of 84 here — alongside 83 others — read 16 September 2026
Dr. Bryan Ardis @BryanArdis · 36,466 Telegram ranks this channel #55 of 73 here — alongside 72 others — read 1 September 2026
Children’s Health Defense @childrenshd · 52,271 Telegram ranks this channel #58 of 91 here — alongside 90 others — read 24 August 2026
Christine Anderson, MdEP (AfD) @christineanderson · 34,916 Telegram ranks this channel #72 of 86 here — alongside 85 others — read 2 September 2026
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.
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 13 November 2026 — this
entry's latest reading, not the date you are reading this.
“Expose The Agenda” (@ETAgenda), 15,666 subscribers as measured 13 November 2026. Telegram Register, tgregister.com/channel/ETAgenda.
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.
