#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…

Channel
东南亚黑暗事件
@bl9872
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
39,864subscribers
-20,630 since we began measuring on 11 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
Register entry
| Telegram ID | -1002398258322 |
|---|---|
| Type | Channel |
| Username | @bl9872 |
| Description | #东南亚 #柬埔寨 #菲律宾 #缅甸 #泰国 揭露东南亚黑暗事件,不信谣,不传谣,理性吃瓜! 注意甄别贴文发布时间线,被搬运贴文进行敲诈勒索的,一律与本频道无关! ☎️商务合作:@bl859 投稿爆料:@bl859 |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 August 2026 |
| Last confirmed live | 27 September 2026 |
| Measurements held | 29 |
| Confirmed unchanged | 1 time, most recently 27 September 2026 |
| On Telegram | t.me/bl9872 |
Topic
Gambling & betting — 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 9 September 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.
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 27 Sept 2026, 09:41 | 39,864 | -723 |
| 17 Sept 2026, 15:37 | 40,587 | -155 |
| 15 Sept 2026, 10:17 | 40,742 | -140 |
| 13 Sept 2026, 18:38 | 40,882 | -158 |
| 11 Sept 2026, 23:19 | 41,040 | -243 |
| 9 Sept 2026, 10:16 | 41,283 | -341 |
| 6 Sept 2026, 04:17 | 41,624 | -260 |
| 4 Sept 2026, 00:19 | 41,884 | -84 |
| 2 Sept 2026, 17:08 | 41,968 | -213 |
| 1 Sept 2026, 14:25 | 42,181 | -203 |
| 31 Aug 2026, 11:55 | 42,384 | -124 |
| 30 Aug 2026, 12:58 | 42,508 | -106 |
| 29 Aug 2026, 14:25 | 42,614 | -179 |
| 28 Aug 2026, 11:48 | 42,793 | -196 |
| 27 Aug 2026, 09:33 | 42,989 | -144 |
| 26 Aug 2026, 07:45 | 43,133 | -248 |
| 25 Aug 2026, 07:26 | 43,381 | -194 |
| 24 Aug 2026, 10:57 | 43,575 | -209 |
| 22 Aug 2026, 19:38 | 43,784 | -124 |
| 21 Aug 2026, 11:58 | 43,908 | first reading |
Engagement
733 posts held, back to 10 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 92 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 1.30%
- avg views ÷ 39,864 subscribers
- Avg views / post
- 518
- 451 posts measured
- Reaction rate
- 1.49%
- reactions ÷ views · ER floor
- Posts in window
- 451
- of 733 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 191 of 451 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 | 733 (10 August 2026 – 27 September 2026) |
| Views total | 233,628 |
| Reactions total | 1,854 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 27 Sept 2026, 14:45 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
- ≈13,500
- Videos
- ≈2,890
- Links
- ≈8,010
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.
- Video runtime
- 2h 56m
- Average length
- 55s
Measured directly from 194 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
4,029 reactions across 409 posts, in 11 distinct kinds. The most used accounts for 51.4% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 2,072 | 51.4% | |
| 🔥 | 1,122 | 27.8% | |
| 🤔 | 774 | 19.2% | |
| 👍 | 20 | 0.496% | |
| 🤯 | 18 | 0.447% | |
| 👎 | 11 | 0.273% | |
| 😁 | 4 | 0.099% | |
| 😢 | 4 | 0.099% | |
| 😱 | 2 | 0.05% | |
| 👏 | 1 | 0.025% | |
| 🥰 | 1 | 0.025% |
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 410 of the 733 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 4,029 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 733 most recent posts we hold, published 10 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.
Recent posts
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。 彰化地检署…
巴基斯坦NCCIA在伊斯兰堡查处非法呼叫中心,9人被捕【含5名中国人】 据巴基斯坦联合通讯社(APP)报道,9月27日,巴基斯坦国家网络犯罪调查局(NCCIA)伊斯兰堡分部对一处涉嫌从事网络诈骗活动的非法呼叫中心展开突击行动。 此次行动依据第156/2026号第一信息报告(FIR)展开,案件涉及涉嫌利用电子手段实施欺诈活动。 行动中,执法人员共逮捕9名嫌疑人,其中包括5名外国籍人员和4名巴基斯坦籍人员。同时,执法人员还查扣多部电子设备及其他疑似与非法呼叫中心活动有关的证据。 NCCIA表示,目前已对查扣的设备及相关材料展开进一步调查,以确认涉案人员的具体分工、诈骗手法及案件涉及规模,并查明是否有其他人员或相关犯罪网络牵涉其中。 目前,案件仍在进一步调查之中。 👌订阅频道: @bl9872 👌吃瓜交友: @dny628 👌投稿爆料: @bl859 👍自助能量闪兑-靓号购买:@DNYNLSD_bot ❤️🔥关注群组:…
🤩🤩🤩🤩🤩🤩🤩🤩🤩🤩🤩🤩 🤩亿级域名|78.COM 🤩官方飞投|充提自由 🤩NO钱包担保3亿U|可查验! 😀 东南亚大事件联合担保🤩🤩🤩🤩🤩(电子) 📱官方频道:@DF78 ➡️关注领VIP 🔝大品牌,实力派:78.COM 本身就是最好的辨识度。 ✅️香港六合彩 · 独家盈利无上限! 🤩所有的特码🤩🤩倍,封顶赔率! 🤩连码三中三🤩🤩🤩倍 🤩连码四中四🤩🤩🤩🤩🤩倍 🤩百家乐全网最高返水🤩% 🤩电子|棋牌|捕鱼返水🤩.🤩% 🤩十三水|德州自主组队,无抽佣; 🏆百家乐真人视讯台红🤩🤩万U; 💥新人超会赢,精彩时刻分享: 🥇六合彩 玩家用7个号码组合下注连码三全中(35注)➕四全中(35注),每注100U,结果开出4个号码。命中4注878倍及一注10000倍大奖,稳稳落袋135万U; 🥈PG电子 9月4号,壹玩家游戏中爆出268万U大奖,本金仅用2.6万U; 🥉DB真人 玩百家乐的老板,第一天存款50万U,连续7天每日提款…
😀😀😀😃😀😃😀😀😃 😭☕️◀️ 🥴🤢😯🙄 ↙️↘️↘️↙️🔼↖️⏯↙️↖️↖️ 😀😀😀😀😀😀😀😀😀😀 😦新用户专享五重大礼包: 😦 新用户注册赠送18.88$体验金 😦新用户跳槽最高领取2888.88$ 😦新会员首存最高赠送10000.80$ 😦新会员二存/三存最高可领彩金8888$ 😦新用户注册满3/7/15天再送5888$ 😦电子游艺实时反水3%,无上限 😦 VIP特权晋级礼金,周周俸禄领不停 😦代理佣金55%永久扶持 更多豪礼等您前来领取 😦😦😦😐😦😦😐😦😦 🐶官方网址: 8HYL.VIP 🐶官方网址: 8H818.COM 🤢🤢🤢🥴🤢🥴 👍官方频道:@BHGJYL8 👍官方客服:@BHGJYL01 👍彩金专员:@BHGJYLCJ02 👍投诉专员:@BHGJYLTS01 👍招商专员:@BHGJYLZS01 😐😧😦😐😐🫤 ↙️↙️↘️↘️↘️⬇️↘️⬇️ 😦😦😦😦😦😦😦😦😦😦
山东枣庄警方侦破一起侵犯公民个人信息案,抓获涉案人员28名,查获公民个人信息4000余万条 近日,山东省枣庄市公安局网安部门在“净网—2026”专项行动中,成功侦破一起侵犯公民个人信息案,打掉犯罪团伙2个,抓获涉案人员28名,查获被非法获取、倒卖的公民个人信息4000余万条,初步查明涉案金额600余万元。 2026年5月,枣庄市滕州公安网安部门在工作中发现,网民马某某长期通过非法渠道大量购买公民个人信息,并将相关信息批量用于贷款业务电话推销,严重侵害群众合法权益。 经进一步侦查,警方发现,马某某组织多人非法获取、整理、倒卖公民个人信息,逐步形成了“获取—整理—分销—变现”的黑色产业链。该犯罪团伙涉案人员众多、涉及信息数量巨大,社会危害性较强。 在查清案件事实、固定相关证据后,枣庄警方组织警力分赴多地开展抓捕行动,成功将28名涉案人员全部抓获,依法捣毁相关犯罪团伙。目前,案件正在进一步办理中。 警方提醒,公民个人信息一旦…
#网友投稿:新型盗U,兄弟们注意了! 骗子找到了新套路:不发钓鱼链接,诱导用户主动清空自己的钱包。他们在 YouTube 上传教程,教大家用 Claude 搭建 AI 加密货币交易机器人。 最近出现一种全新的虚拟币骗局,和以往发钓鱼链接骗密码的套路完全不一样,全程没有钓鱼链接,而是引导受害者亲手操作,自己把钱包里的资产转给骗子。 骗子在 YouTube 大量发布视频教程,用 AI 生成虚拟主播配音,对外宣称:可以借助 Claude 大模型,一键搭建自动套利的加密货币交易机器人,开启之后自动赚钱,稳赚不赔。 骗局完整流程 1. 视频里演示全套教程,教观众复制代码,去一个仿冒的合约编译网站部署智能合约,看起来非常专业。 2. 这个编译网站是骗子自己搭建的,你粘贴进去的正常代码会在后台偷偷替换成盗币代码。屏幕上展示的代码是干净的,实际部署到链上的是盗币合约。 3. 按照视频指引,受害者自己把加密货币转入合约钱包,手动确认授…
#马来西亚资讯:吉隆坡娱乐场所突遭检查 3名中国男子和1名菲律宾男子被扣 9月27日,马来西亚移民局通报,执法人员突查吉隆坡旧巴生路一家娱乐场所,当场扣查4名外国男子,包括3名中国籍和1名菲律宾籍,年龄21至31岁。 4人接受检查时,都无法出示合法旅行证件或有效居留准证,随后被带往布城移民局进一步调查。这次行动由马来西亚移民局总部情报与特别行动部门执行,国家反毒机构参与配合。 调查显示,这家娱乐场所从2025年开始营业,主要安排外国人进行表演。执法人员还通知3名马来西亚男子前往移民局协助调查。 PS:谁家男模被扣了🤣 👌订阅频道: @bl9872 👌吃瓜交友: @dny628 👌投稿爆料: @bl859 👍自助能量闪兑-靓号购买:@DNYNLSD_bot ❤️🔥关注群组:https://t.me/DNYNLSD
#斯里兰卡资讯:机场抓2名中国男子!行李藏3.2万支香烟 9月27日,斯里兰卡警方在科伦坡班达拉奈克国际机场查获一起涉嫌走私香烟案件,两名中国籍男子被捕,行李中共查出3.2万支外国香烟。 警方表示,机场警察缉毒局根据线索对两名入境旅客展开检查,先从一名36岁中国男子携带物品中查获70条、1.4万支香烟,随后又从一名56岁中国男子处查获90条、1.8万支香烟。 两人合计被查出160条、3.2万支香烟。警方称,相关香烟涉嫌未依法申报入境,目前两人已被控制,案件交由卡图纳亚克机场相关部门继续调查,警方正进一步追查香烟来源及入境目的。 👌订阅频道: @bl9872 👌吃瓜交友: @dny628 👌投稿爆料: @bl859 👍自助能量闪兑-靓号购买:@DNYNLSD_bot ❤️🔥关注群组:https://t.me/DNYNLSD
🇹🇭曼谷的洪水,有点大… 据曼谷市政府防灾减灾指挥部通报,当地时间9月26日,曼谷市长差察·西迪汶宣布,曼谷全市50个区全部划为洪水灾区。 近48小时持续强降雨造成曼谷大范围内涝、交通中断,部分路段积水严重。当局开放学校等场所作为临时避难所,加紧排水、分发沙袋开展救灾工作。 泰国气象部门预警,曼谷及周边区域仍有强降雨,需要防范山洪、滑坡风险。 👌订阅频道: @bl9872 👌吃瓜交友: @dny628 👌投稿爆料: @bl859 👍自助能量闪兑-靓号购买:@DNYNLSD_bot ❤️🔥关注群组:https://t.me/DNYNLSD
🇹🇭泰国曼谷机场,9月26日被洪水淹没。 👌订阅频道: @bl9872 👌吃瓜交友: @dny628 👌投稿爆料: @bl859 👍自助能量闪兑-靓号购买:@DNYNLSD_bot ❤️🔥关注群组:https://t.me/DNYNLSD
半年收20万美元!机场移民警察被指替涉诈人员“开路” 9月25日,柬埔寨反腐败机构(ACU)发布案件调查通报,披露一名在德崇国际机场工作的移民警察涉嫌利用职务便利,为部分外籍人员违规进入柬埔寨提供协助,其中涉及电诈团伙人员。 ACU公布,该警员名为皮帕平(音译),警衔为二级警尉。 通报称,调查人员通过一个电诈团伙发现其涉案线索。该团伙与中国籍人员 张建强(音译)有关,后者被指曾带领人员从中国进入柬埔寨。ACU称,皮帕平涉嫌为相关人员办理、协调入境手续。 ACU指出,其涉嫌违规协助进入柬埔寨的人员,包括被列入黑名单者、使用虚假护照者,以及需要安排人员在机场接应、引导入境的外籍人员。ACU称,包括张建强相关涉诈人员在内的一些人员因此得以顺利入境。 根据通报披露,其还对不同入境“服务”设置收费标准,包括商业签证约380至420美元、电子商务入境手续900美元、旅游签证120美元,以及VIP快速通关450美元。 尤其值得关注…
Showing the 12 most recent of 733 posts we hold for @bl9872. 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.
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.
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 27 September 2026 — this entry's latest reading, not the date you are reading this.
“东南亚黑暗事件” (@bl9872), 39,864 subscribers as measured 27 September 2026. Telegram Register, tgregister.com/channel/bl9872.
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.