Channel
@dongnanyadashijian9
On this record: Topic · Observations · Also posting the same content · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
145,775subscribers
-8,447 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 100,000–316,228.
| Telegram ID | -1002485313713 |
|---|---|
| Type | Channel |
| Username | @dongnanyadashijian9 |
| Description | ✍️投稿文案找： @didizhou 🛎广告招商： @didizhou ✅订阅频道： @dongnanyadashijian9 |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 18 September 2026 |
| Measurements held | 32 |
| Confirmed unchanged | 1 time, most recently 18 September 2026 |
| On Telegram | t.me/dongnanyadashijian9 |
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 9 August 2026 and assigned it the closest of 31 fixed categories, at 59% 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 2 other registered channels. 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 |
|---|---|---|---|
| @dongnanyadashijian9/35233 · this entry7 Aug 2026, 14:54 UTC | @jianpuzhai_01/339457 Aug 2026, 14:54 UTC | 1.00 | under a minute |
| @dongnanyadashijian9/35234 · this entry7 Aug 2026, 14:54 UTC | @jianpuzhai_01/339467 Aug 2026, 14:54 UTC | 1.00 | under a minute |
| @dongnanyadashijian9/35235 · this entry7 Aug 2026, 14:55 UTC | @jianpuzhai_01/339487 Aug 2026, 14:55 UTC | 1.00 | under a minute |
| @dongnanyadashijian9/35239 · this entry7 Aug 2026, 14:55 UTC | @jianpuzhai_01/339527 Aug 2026, 14:56 UTC | 1.00 | under a minute |
| @dongnanyadashijian9/35243 · this entry7 Aug 2026, 14:56 UTC | @jianpuzhai_01/339567 Aug 2026, 14:57 UTC | 1.00 | under a minute |
| @dongnanyadashijian9/35244 · this entry7 Aug 2026, 15:55 UTC | @jianpuzhai_01/339577 Aug 2026, 15:55 UTC | 1.00 | under a minute |
| Channel | Matching posts | Text overlap | Typical gap | Published first |
|---|---|---|---|---|
| @jianpuzhai_01 | 10 (8/8 hand-verifiable sample passed) | 1.00 | under a minute | this entry (10–0) |
| @sililanka007 | 9 (8/8 hand-verifiable sample passed) | 1.00 | under a minute | this entry (9–0) |
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. 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 13 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 3, the earliest publisher we hold is @dongnanyadashijian9 — 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 8 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 2 other registered channels, 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 |
|---|---|---|
| 18 Sept 2026, 05:59 | 145,775 | -1,787 |
| 15 Sept 2026, 21:58 | 147,562 | -1,823 |
| 14 Sept 2026, 07:57 | 149,385 | +6,416 |
| 12 Sept 2026, 19:58 | 142,969 | +28,121 |
| 10 Sept 2026, 19:37 | 114,848 | +16,328 |
| 7 Sept 2026, 17:58 | 98,520 | -2,402 |
| 4 Sept 2026, 20:02 | 100,922 | -1,137 |
| 3 Sept 2026, 05:57 | 102,059 | -1,057 |
| 1 Sept 2026, 23:06 | 103,116 | -987 |
| 31 Aug 2026, 22:05 | 104,103 | -750 |
| 31 Aug 2026, 00:17 | 104,853 | -544 |
| 30 Aug 2026, 00:07 | 105,397 | -671 |
| 29 Aug 2026, 03:24 | 106,068 | -1,316 |
| 27 Aug 2026, 23:54 | 107,384 | -3,598 |
| 27 Aug 2026, 01:04 | 110,982 | -6,372 |
| 26 Aug 2026, 01:14 | 117,354 | -391 |
| 25 Aug 2026, 03:26 | 117,745 | -459 |
| 23 Aug 2026, 20:27 | 118,204 | -494 |
| 22 Aug 2026, 06:38 | 118,698 | -533 |
| 20 Aug 2026, 22:28 | 119,231 | first reading |
1,192 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 106 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 13 of 635 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 27 September 2026 |
|---|---|
| Posts held | 1,192 (7 August 2026 – 27 September 2026) |
| Views total | 142,440 |
| Reactions total | 10 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 27 Sept 2026, 21:57 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 27 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 311 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.
16 reactions across 16 posts, in 2 distinct kinds. The most used accounts for 93.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 15 | 93.8% | |
| 👎 | 1 | 6.25% |
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 19 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 16 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 16 August 2026 to 27 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.
微信严查头像昵称：今年已处置9万多个账号，严重违规直接封号 9月24日，“微信珊瑚安全”公布账号资料专项治理情况。今年以来，微信已处置相关违规账号92079个，其中2463个账号被关闭。 这次重点整治冒充官方、伪装身份和违规引流等问题。有人用“服务中心”等名称冒充官方机构，有人把账号包装成新闻媒体，还有人在没有认证的情况下使用财经、医疗、司法、教育等专业身份。 微信已对相关账号采取清空资料、禁言、封号等措施。近期部分冒充“村干部”直播带货的账号，已被永久封禁。头像、昵称和简介如果存在冒充官方、身份伪装，或夹带色情、赌博等违规引流内容，严重的可直接封号。 👻😇🥰😘🔥🔥🔥🔥🔥💩✍️🍡🎈 ✅👩💻我要投稿 😘安妮宝贝： ——————————————————— 😳求职招聘 📑迪拜新闻 ✴️二手市场 🍉吃瓜中心 💻搭建开发
柬埔寨波贝洪水持续上涨 据现场目击者称，柬埔寨波贝市（Poipet City）近日积水不断上涨，有人拍到一名中国公民推着一辆豪车，在积水中艰难前行。 目前当地积水情况仍在持续，照这个趋势发展下去，汽车可能都快派不上用场了，船只或将成为接下来的主要出行工具。 昨天开豪车，今天推豪车，明天可能就得坐船了。 👻😇🥰😘🔥🔥🔥🔥🔥💩✍️🍡🎈 ✅👩💻我要投稿 😘迪迪周： ——————————————————— 😳东南亚招聘 📑东南亚大事件 ✴️柬埔寨二手 🍉吃瓜中心 💻搭建开发
#网友投稿：老挝特内的壹号公馆二期现在检查，现场已经通知跑出来了很多人了 目前只有治安局，没听到有老挝警察过来，还在办公的赶紧跑吧 👻😇🥰😘🔥🔥🔥🔥🔥💩✍️🍡🎈 ✅👩💻我要投稿 😘迪迪周： ——————————————————— 😳东南亚招聘 📑东南亚大事件 ✴️柬埔寨二手 🍉吃瓜中心 💻搭建开发
#巴基斯坦资讯：卡拉奇NCCIA突袭两处涉嫌诈骗呼叫中心，73人被带走调查 9月27日，巴基斯坦国家网络犯罪调查局（NCCIA）卡拉奇分局对位于卡拉奇Tariq Road、PECHS Block 6的两处涉嫌从事诈骗活动的呼叫中心展开突袭行动。 据当地媒体报道，两处呼叫中心均隐藏在住宅别墅内运营，现场共有73名人员被带走调查。涉案人员包括客服代理、客服主管、运营负责人以及IT工作人员等。 执法人员在现场查获一套正在运行的CRM系统，其中存有客户信息及呼叫中心运营数据，同时扣押其他电子设备和数字证据。相关设备及数据已被送交进行进一步的取证和技术分析。 初步调查显示，涉案呼叫中心涉嫌以Zain Communications名义运营，并被指通过虚假金融服务等方式，将美国公民作为目标。据报道，相关人员涉嫌以新信用卡、低利率金融服务等名义实施诈骗。 目前案件已立案调查，相关FIR编号为92/2026及93/2026。其中一名涉案…
#泰国新闻 中国公司老板涉骗2000多万潜逃泰国 芭提雅豪华公寓落网 9月27日，泰国警方在春武里府芭提雅一处豪华公寓抓获65岁的中国籍陈姓男子。陈某是中国警方通缉的诈骗嫌疑人，长期担任两家贸易公司的负责人和实际控制人。 2023年3月至2024年1月，陈某通过自己控制的公司与一名大客户签订食糖买卖合同，但公司实际上没有相应供货能力，也没有合同约定的货物。 受害方先后支付超过2000万元人民币货款，折合约1亿泰铢，最终一直没有收到货。相关资金后来被用于偿还陈某此前的债务和个人开支。 2025年8月，中国警方接到报案后立案追查。陈某后来逃往泰国藏匿，泰国中央调查局特警和移民局调查人员锁定其行踪，在芭提雅纳库鲁路一处公寓将其抓获。 泰方已撤销陈某在泰停留许可，并将其交由移民部门拘留，准备送回中国接受调查和司法处理。 👻😇🥰😘🔥🔥🔥🔥🔥💩✍️🍡🎈 ✅👩💻我要投稿 😘迪迪周： ——————————————————— …
中国女主播被诱骗前往泰国，后遭绑架转移至缅甸，勒索9万美元赎金 27日，据《南华早报》报道，一名中国网络带货主播 Wang Ran 被指在缅甸遭到非法拘禁，对方索要 9万美元赎金。 据中国官方媒体报道，Wang Ran 最初被熟人以工作为由诱骗前往泰国，之后她告诉朋友，自己被强行控制在缅甸。 家属表示，现年24岁的 Wang Ran 遭人扣押，对方要求支付 60万元人民币（约9万美元）赎金。据称，她此前是被一名通过网络认识的人以“工作机会”为由骗去泰国的。 这也是近期又一起中国网络主播以“海外工作”为诱饵，被骗前往东南亚诈骗园区的案件。 👻😇🥰😘🔥🔥🔥🔥🔥💩✍️🍡🎈 ✅👩💻我要投稿 😘迪迪周： ——————————————————— 😳东南亚招聘 📑东南亚大事件 ✴️柬埔寨二手 🍉吃瓜中心 💻搭建开发
#群友爆料：缅甸三佛塔园区黑幕曝光最新消息，坐标：北纬 15.47331，东经 98.28373。 园区内几十家公司，十几栋楼，每栋楼3层。涉事888公司，老板都是中国人。园区主管“大炮”，二老板“三喜”，里面不少打手和管理层还吸DU。 这个园区之前就被曝光过很多次，但暴行一点没变。被骗过来的人全部被非法关押、殴打、电击、小黑屋、水牢、关兵站、塞进狗笼，往笼子里放毒蛇都是家常便饭。 男的一送进去先挨打，先来个下马威。还会给受害者看酷刑、伤人致死的视频，用来吓唬人。 园区门口有持枪大兵二十四小时轮流看守，根本无法逃脱。当地武装和园区利益牵扯甚广，不敢让人逃跑。 长得好看的女生，会被送到园区会所被逼卖淫；普通样貌的女生，和男生一样，被逼做电信诈骗敲键盘。只要稍微敢反抗，就会遭受各种暴力虐待。 很多被困的人被折磨到身体垮掉、精神失常。 这里的残忍程度不亚于当年的KK园区。恳请国际反诈联盟、国际刑警，还有中缅泰相关官方关注…
汇旺涉嫌洗钱240亿美元，主要服务电诈产业 据香港明报、香港01联合报道，汇旺集团近日陷入洗钱风波，其董事长李雄被押解回中国后，调查显示，过去四年汇旺涉嫌通过旗下平台洗钱高达 240亿美元，且主要服务对象为电信诈骗及网络犯罪团伙。 据总部位于英国伦敦的区块链分析公司 Elliptic 统计，自2021年以来，汇旺担保及旗下商家使用的加密货币钱包，已接收超过 240亿美元资金，其中大部分资金流向电诈产业。据明报援引 Elliptic 的研究，汇旺集团在去年春天之前共促成近 270亿美元交易，虽部分交易可能合法，但大规模资金流显示其存在洗钱风险。 消息还指出，汇旺旗下 汇旺担保 和 好旺担保，被认为是集团洗钱的核心工具，而其支付平台 汇旺支付（Huione Pay） 和所谓平民化银行 胖大银行（Panda Bank） 表面上服务普通用户，实际上被用于暗网交易和“杀猪盘”诈骗收益洗白，成为全球最大的暗网交易平台之一。 香港01…
#巴基斯坦资讯：“拉合尔非法呼叫中心被查，21人被捕， NCCIA Lahore与Faisalabad在拉合尔DHA Phase 6突袭一处涉嫌非法呼叫中心。 抓获21名嫌疑人，查扣29部手机和10台电脑。初查显示，该团伙涉嫌利用虚假社交账号、SIM卡及VPN开展线上任务/投资诈骗。目前嫌疑人国籍、涉案金额及受害者情况尚未披露，案件仍在调查中。 👻😇🥰😘🔥🔥🔥🔥🔥💩✍️🍡🎈 ✅👩💻我要投稿 😘迪迪周： ——————————————————— 😳东南亚招聘 📑东南亚大事件 ✴️柬埔寨二手 🍉吃瓜中心 💻搭建开发
#马来西亚资讯：森林城市诈骗窝点被端后，住宅内再现大批疑似诈骗设备 9月27日，据新加坡亚洲新闻台（CNA）报道，马来西亚柔佛州森林城市近期再次发现疑似诈骗活动迹象。 此前，柔佛警方于7月15日展开大规模行动，突查森林城市27套公寓及5栋别墅，捣毁两个涉嫌网络诈骗的团伙，共抓获335人，其中包括309名中国籍、19名印尼籍、4名缅甸籍及3名马来西亚籍人员。 警方当时查获313台电脑、1557部手机、17台笔记本电脑以及其他设备。警方称，涉案团伙涉嫌从事虚假加密货币投资、网络“杀猪盘”等诈骗活动，受害目标主要在海外。 然而，警方大规模行动后，森林城市内仍被发现疑似诈骗设备。 据CNA报道，当地一名房东发现出租公寓的每月电费突然超过1000令吉，认为情况异常，随后安排房产中介上门检查。 中介进入房屋后发现，屋内摆放着大量技术设备，怀疑租客可能利用该住宅从事诈骗活动。随后，中介终止租约并要求租客搬离，同时将相关情况报告警方…
#国内资讯 冒充大陆公安诈骗逾50人 “天金诈骗集团”20人遭起诉 台湾彰化地方检察署9月27日公布“天金诈骗集团”案件进展。该集团涉嫌冒充中国大陆公安人员，以涉及洗钱等理由诈骗民众，受害者超过50人。检方已分6波起诉20人，并对集团核心成员林姓女子求刑25年。 检方指出，该集团由廖姓男子、林姓女子为核心，设立“天成机房”及“金云曜机房”，专门从事假冒大陆公安的电信诈骗。为躲避警方查缉，集团多次更换机房地点，先后转移至台中梧栖、乌日及北屯等地，并与其他诈骗集团合作，招募车手及收簿人员转移、洗白赃款。 其中，一名在新加坡留学的学生接到诈骗电话后，被骗称银行账户涉及洗钱，随后遭要求前往越南“配合调查”。诈骗人员进一步控制其手机，并制作学生遭捆绑的视频，再以视频向身处中国大陆的台商父亲勒索财物。 该名家长最终在中国大陆交出4公斤黄金，价值超过1600万元新台币。诈骗集团随后派遣车手前往大陆取走黄金，并通过相关管道将赃款洗白。…
粤澳警方侦破跨境卖淫集团，利用通讯群组招揽人员，77人被捕 9月24日，澳门司法警察局联合广东警方展开行动，侦破一个涉嫌利用通讯群组经营跨境操纵卖淫活动的犯罪集团，两地共拘捕77人，初步涉及金额至少530万港元。 警方调查发现，该集团至少从今年3月开始运作，主要在澳门路氹一带酒店物色嫖客，并通过通讯群组招揽卖淫女子及相关人员加入。 据警方通报，一名39岁内地女子涉嫌为集团主要成员之一，负责创建及管理通讯群组，并在内地招揽有意到澳门从事卖淫活动的女子。相关群组成员一度超过300人，加入人员每7天需缴纳200元人民币“入群费”。 集团成员还通过通讯群组进行“派单”，安排卖淫女子前往酒店与嫖客交易，并要求成员定期删除聊天记录，以逃避警方调查。 行动期间，澳门警方拘捕16名涉嫌集团成员，并在酒店带走11名涉嫌卖淫女子及4名嫖客；广东警方则在广州、珠海、中山、佛山、深圳等地拘捕相关人员。 目前两地警方共拘捕77人，案件仍在进一…
Showing the 12 most recent of 1,192 posts we hold for @dongnanyadashijian9. 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.
Republished by
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Republishes
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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 6 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.
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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.
@dongnanyadashijian9 named 2 handles that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
References a handle that is not a live channel — we have no record it ever was one.
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 18 September 2026 — this entry's latest reading, not the date you are reading this.
“💥💥东南亚大事件💥💥” (@dongnanyadashijian9), 145,775 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/dongnanyadashijian9.
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.