Channel
@hyxw2
On this record: Topic · Observations · Also posting the same content · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
75,053subscribers
+50,527 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1002180961034 |
|---|---|
| Type | Channel |
| Username | @hyxw2 |
| Description | 菲律宾新闻大事件 每天分享最新热门资讯新闻 本频道只负责相关发布信息，真假自行甄别 《删帖悬赏付费》真假自分辨 曝光投稿商务合作 @feihang |
| Created | Between 1 June 2024 and 30 September 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 17 September 2026 |
| Measurements held | 32 |
| Confirmed unchanged | 1 time, most recently 17 September 2026 |
| On Telegram | t.me/hyxw2 |
National news — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 2026 and assigned it the closest of 31 fixed categories, at 77% 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.
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.
Posts published here appear word for word on 1 other registered channel. 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.
| Posted first | Then | Overlap | Gap |
|---|---|---|---|
| @dnysj/897967 Aug 2026, 14:45 UTC | @hyxw2/62563 · this entry7 Aug 2026, 14:45 UTC | 1.00 | under a minute |
| @dnysj/898017 Aug 2026, 15:07 UTC | @hyxw2/62568 · this entry7 Aug 2026, 15:07 UTC | 1.00 | under a minute |
| @dnysj/898027 Aug 2026, 15:15 UTC | @hyxw2/62569 · this entry7 Aug 2026, 15:15 UTC | 1.00 | under a minute |
| @dnysj/898047 Aug 2026, 15:23 UTC | @hyxw2/62571 · this entry7 Aug 2026, 15:23 UTC | 1.00 | under a minute |
| @dnysj/898077 Aug 2026, 15:48 UTC | @hyxw2/62574 · this entry7 Aug 2026, 15:48 UTC | 1.00 | under a minute |
| @dnysj/898117 Aug 2026, 16:45 UTC | @hyxw2/62578 · this entry7 Aug 2026, 16:45 UTC | 1.00 | under a minute |
| Channel | Matching posts | Text overlap | Typical gap | Published first |
|---|---|---|---|---|
| @dnysj | 17 (6/8 hand-verifiable sample passed) | 1.00 | under a minute | @dnysj (17–0) |
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. 17 of the 17 matches recorded here fall after that date and carried no header when we read them. The rest predate reliable capture and are not evidence either way.
“Published first” means first in this corpus. We hold 17 comparable posts for this entry, running 7 August 2026 to 7 August 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.
Across the whole group of 2, the earliest publisher we hold is @dnysj. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_copy, last confirmed 7 August 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.
This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 17 Sept 2026, 09:57 | 75,053 | -18 |
| 15 Sept 2026, 06:22 | 75,071 | -2 |
| 13 Sept 2026, 12:41 | 75,073 | -7 |
| 11 Sept 2026, 18:20 | 75,080 | -13 |
| 9 Sept 2026, 08:00 | 75,093 | +748 |
| 6 Sept 2026, 00:36 | 74,345 | -737 |
| 3 Sept 2026, 21:15 | 75,082 | -9 |
| 2 Sept 2026, 15:48 | 75,091 | -14 |
| 1 Sept 2026, 16:35 | 75,105 | -6 |
| 31 Aug 2026, 13:28 | 75,111 | -8 |
| 30 Aug 2026, 16:13 | 75,119 | -3 |
| 29 Aug 2026, 13:23 | 75,122 | -5 |
| 28 Aug 2026, 10:03 | 75,127 | -1 |
| 27 Aug 2026, 11:02 | 75,128 | -7 |
| 26 Aug 2026, 08:55 | 75,135 | -14 |
| 25 Aug 2026, 05:54 | 75,149 | -8 |
| 24 Aug 2026, 07:08 | 75,157 | -17 |
| 22 Aug 2026, 12:47 | 75,174 | -8 |
| 21 Aug 2026, 04:26 | 75,182 | -2 |
| 20 Aug 2026, 04:33 | 75,184 | first reading |
1,180 posts held, back to 7 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 91 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. It is computed over the 9 of 808 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 20 September 2026 |
|---|---|
| Posts held | 1,180 (7 August 2026 – 20 September 2026) |
| Views total | 794,269 |
| Reactions total | 9 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 21 Sept 2026, 00:18 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.
Lifetime counters from Telegram’s own channel header, read 21 September 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.
Measured directly from 245 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.
9 reactions across 9 posts, in 5 distinct kinds. The most used accounts for 33.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 😁 | 3 | 33.3% | |
| 🎉 | 2 | 22.2% | |
| 🔥 | 2 | 22.2% | |
| 🤩 | 1 | 11.1% | |
| 🥰 | 1 | 11.1% |
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 9 of the 1,000 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 9 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 1,000 most recent posts we hold (the sample is capped at 1,000), published 13 August 2026 to 20 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.
#群友投稿：狗推生涯，忙了这么多年，最后还是一无所有。 刚踏出国门的时候，总觉得自己能赚到钱。那时候每天想的都是业绩、提成、奖金，想着熬几年，攒够钱就回国，买车、买房，重新开始。别人问我在国外做什么，我也不敢说得太明白，只是笑着说：“做客服，赚点快钱。”后来才发现，有些钱不是那么好赚的。 白天对着电脑，晚上熬着夜，嘴里喊着“上岸”，心里却一天比一天麻木。赚到钱的时候觉得自己终于熬出头了，可钱还没捂热，就被花掉了。手机换了一个又一个，酒喝了一杯又一杯，认识的人来了又走，所谓的兄弟也不知道换了多少批。几年过去，回头一看，银行卡里的数字没留下多少，青春倒是没了大半。 真正让我清醒的，是回国以后。曾经天天坐在电脑前敲键盘，后来换了一个地方，开始“踩缝纫机”。铁门一关，什么梦想、什么老板、什么高薪、什么兄弟情义，全都变成了笑话。里面的日子很慢，慢到你能把过去几年发生的事情，一件一件重新想起来。想起当初为什么出来，想起自己吹过的牛，想…
坐牢，只是影响这一代；没钱，可能影响的是一个家族的世世代代。 很多人害怕的是失去自由，却忽略了更现实的问题：没有积蓄、没有退路、没有稳定的生活，才是一个家庭真正漫长的困境。 一个人走错一步，可能只是几年光阴；但如果连累父母、拖累伴侣，让孩子失去更好的成长环境，甚至让整个家庭重新回到起点，那么付出的代价，远不止一个人的青春。 人这一辈子，钱不是全部，但没有钱的时候，你会发现很多事情都不是靠一句“以后再说”能够解决的。 所以，别只想着眼前赚多少，先想想自己能不能守住现在拥有的，能不能给家人留一条退路。人生最怕的，不是输一次，而是输掉以后，连重新开始的本钱都没有。 🔔 点击订阅华人新闻头条 ↓ 🔗 t.me/+fBZd42IBnLowYjY8 ✅ 免费投稿爆料：@feihang
#老挝新闻：老挝推进全国收缴非法枪支弹药 为维护社会安全及促进国家发展，老挝正全面落实总理及主管部门指示，在全国范围内强力推进非法枪支、军用武器及爆炸物的收缴工作。 此前，非法使用武器引发的抢劫、恐吓及暴力杀人等案事件严重威胁民众生命财产安全。官方强调，收缴和严格管控非法武器对预防犯罪、降低暴力风险及保障人民安全至关重要。 🔔 点击订阅华人新闻头条 ↓ 🔗 t.me/+fBZd42IBnLowYjY8 ✅ 免费投稿爆料：@feihang
台湾男子勒死泰国女友 藏尸冰柜被捕 台湾警方称，一名43岁吴姓男子涉嫌勒死36岁泰国籍女友，将遗体藏于桃园工作场所冰柜中，9月20日被捕。 两人在泰国相识，远距离交往约一年。女子9月2日抵台欲提分手，后与家人失联，朋友9月8日报警。警方追踪发现，吴某9月13日凌晨拖大行李箱离开女子所住酒店，驾车前往桃园。 吴某供称因女友提分手而情绪失控将其勒死，但称并非蓄意。警方随后在其工作场所冰柜内发现遗体，初步检验为勒颈窒息死亡。吴某已被移送检方，案件仍在调查中。 PS：分手就死，太极端了！！！！ 🔔 点击订阅华人新闻头条 ↓ 🔗 t.me/+fBZd42IBnLowYjY8 ✅ 免费投稿爆料：@feihang
柬泰边境居民恐惧不安 近日，居住在柬埔寨－泰国边境一线的部分居民出现恐惧和不安情绪，担心泰国与柬埔寨军队可能再次爆发冲突，影响安全与生活。社会监测人员指出，这种恐惧已影响到居民的心理健康和生计。 观察者表示，居民忧虑加剧与他们接收到来自泰方的政治言论以及在两国领导人会晤后尚未看到积极成果有关。柬埔寨政府官员则重申，政府会持续为边境居民提供援助，并通过外交机制和国际法继续解决边界争端。 🔔 点击订阅华人新闻头条 ↓ 🔗 t.me/+fBZd42IBnLowYjY8 ✅ 免费投稿爆料：@feihang
#缅东第一深情投稿 国内的贤妻啊，对不起我不能回去了， 我和越南小姐姐，过得很好，~ PS：不是东南亚的男人不回家，而是亲爱的祖国通缉了他 🔔 点击订阅华人新闻头条 ↓ 🔗 t.me/+fBZd42IBnLowYjY8 ✅ 免费投稿爆料：@feihang
#老挝新闻：占巴塞警方捣毁疑似网络诈骗窝点，22名中国籍人员被控制 9月18日上午11时，老挝占巴塞省公安厅根据群众提供的线索，对巴江县（Bachieng Chaleunsouk）Houaxae村一处出租屋展开突击检查，查获一个涉嫌利用计算机实施网络犯罪的窝点。 据当地警方通报，行动由占巴塞省公安厅外国人管理部门联合巴江县有关部门及当地行政人员实施。 现场共控制22名中国籍嫌疑人，其中包括1名女性。 执法人员同时查获大量电子设备，包括49台台式电脑、237部手机、27台电脑主机（CPU）、7台稳压/供电设备以及其他相关物品。 警方表示，目前案件仍处于调查取证阶段，22人的具体分工、涉嫌实施的诈骗类型、受害者情况及涉案金额尚未公布，最终违法犯罪事实仍有待进一步调查确认。 🔔 点击订阅华人新闻头条 ↓ 🔗 t.me/+fBZd42IBnLowYjY8 ✅ 免费投稿爆料：@feihang
西港警方逮捕两名假冒警察的柬籍男子 西港警方今天披露，9月15日，西港省警察局轻案犯罪调查科成功抓获2名柬籍男子。 据悉，两名嫌疑人分别为25岁的Hom Savy和40岁的Thai Ratha，均居住于西港。二人因涉嫌在西港一家赌场内冒充警察、擅自盘查外国游客护照而被捕。 警方现场缴获1部手机、1块手表及1辆红色的Scoopy摩托车。目前，两名嫌疑人已被押送至省警局依法审讯及处理。 🔔 点击订阅华人新闻头条 ↓ 🔗 t.me/+fBZd42IBnLowYjY8 ✅ 免费投稿爆料：@feihang
日本山口县一名 19 岁女警引发舆论哗然 白天身着警服巡逻扫黄，查处风俗行业；下班后特意跨县前往福冈，在风俗店兼职接单。 这名女警在 3 个月内获利 8 万日元（约 4900 元人民币），后被匿名举报曝光，最终辞职。 同类型案件并非孤例，日本多地也曾曝出公职人员违规从事风俗兼职丑闻。 #日本 #女警 #警界丑闻 #公职人员 #国际新闻 🔔 点击订阅华人新闻头条 ↓ 🔗 t.me/+fBZd42IBnLowYjY8 ✅ 免费投稿爆料：@feihang
#技师 #按摩 #spa 手法怎么样 ？ 🔔 点击订阅华人新闻头条 ↓ 🔗 t.me/+fBZd42IBnLowYjY8 ✅ 免费投稿爆料：@feihang
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朝鲜顶尖黑客假冒AI/Web3巨头，全球疯狂“钓鱼”IT精英！ 美国FBI 联合 日本警察厅 等多国顶级安全部门发出大红安全预警：臭名昭著的朝鲜黑客组织 “WaterPlum”（又名 Contagious Interview）正伪装成 AI、NFT 及加密货币巨头 的高管或 HR，在全球各大招聘平台大肆设局，精准围猎美、日、欧洲等地的顶级开发者与 IT 精英！ 这群黑客的手段极其逼真且致命，专门针对 AI、Web3 领域的求职者，其“夺命连环招”如下： 黑客会在 LinkedIn、GitHub、X 等平台伪装成明星企业的猎头，主动送上年薪百万、全远程办公等让人无法拒绝的“神仙工作”。 为了让你彻底放下防备，他们会组织极其正规的“技术面试”，甚至搭建精美的高仿企业官网。 面试后，黑客会以“能力评估”或“代码审查”为由，丢给你一个开源代码库、软件安装包或虚假视频会议工具，要求你下载运行。 只要你点击运行，恶意软件将瞬间黑掉你的电脑…
Showing the 12 most recent of 1,180 posts we hold for @hyxw2. 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.
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.
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 19 August 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 8 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 17 September 2026 — this entry's latest reading, not the date you are reading this.
“菲律宾新闻头条” (@hyxw2), 75,053 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/hyxw2.
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.