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停班停課最新通知 🇹🇼 Taiwan Alerts engagement report

@TaiwanAlerts - 175K followers on X

Measured over 47 original posts from a 30-day window, last computed on October 19, 2026.

Engagement

Middle of its size range
Per follower
0.075%
of 175K followers
Per impression
0.434%
30K views on a typical post
Reach
17.2%
of its followers see a post
Typical post
130
interactions (median)
Saved
0.01%
3 bookmarks on a typical post
Posting rate
1.77/day
active 83% of days
Peak time
14:00 UTC
Wednesday

A typical post picks up 130 interactions against 175K followers, an engagement rate of 0.075%. Measured over 47 original posts, its engagement rate beats 54% of 20,444 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 30K times each, and 0.434% of those impressions turn into an interaction. That is about 17.1% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.8 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 14:00 UTC, and Wednesday is the busiest day of the week. Of the 47 posts sampled, 98% carry an image or video and 9% are part of a thread. The account's strongest tracked post pulled 1.9K interactions, about 14x its own typical post. Recurring topics include #薔琵, #即時天氣, #天氣小叮嚀.

Measured over 47 original posts from a 30-day window, last computed on October 19, 2026. Recurring tags: #薔琵, #即時天氣, #天氣小叮嚀.

Compared with accounts its own size

停班停課最新通知 🇹🇼 Taiwan Alerts's engagement rate beats 54% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 9 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.

On engagement per impression rather than per follower it beats 24% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.

Where this sits in the catalog

At 0.075%, 停班停課最新通知 🇹🇼 Taiwan Alerts sits above the 25th percentile of the 204,798 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.

p100.004%
p250.024%
p50 (median)0.143%
p750.678%
p902.55%
p9982.4%
Engagement rate as a share of followers, across the 204,798 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,879 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.004%
25th percentile0.024%
50th percentile0.143%
75th percentile0.678%
90th percentile2.55%
99th percentile82.4%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 14:00 UTC, and Wednesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 14:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 14:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%89K
01:00 UTC-2%90K
02:00 UTC-3%88K
03:00 UTC-4%94K
04:00 UTC-5%76K
05:00 UTC-4%75K
06:00 UTC-5%86K
07:00 UTC-5%93K
08:00 UTC-4%108K
09:00 UTC-4%124K
10:00 UTC-3%129K
11:00 UTC-3%141K
12:00 UTC-3%154K
13:00 UTC-3%167K
14:00 UTC-4%173K
15:00 UTC-2%176K
16:00 UTC-3%171K
17:00 UTC-3%159K
18:00 UTC-2%149K
19:00 UTC-2%141K
20:00 UTC-1%131K
21:00 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Wednesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Wednesday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Formats this account uses

Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video98% of posts+111%+108% to +115%34K
Outbound link0% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 98% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
  • 0% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
  • Its average post runs 208 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.

These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.

Best tweets

  • Sep 2, 202614x their median

    沙德爾海警機率降低 預計週四~週五二度登陸中國 https://t.co/aQmiBoDk4S

    1.6K1255255160K viewsView on X
  • Jul 1, 20268.4x their median

    🎂【#十四週年|謝謝你們陪我們走到這裡】 今年,是我們成立這個頻道(臉書以來)滿 14 週年的日子。 回首這 14 年,除了吃飯、睡覺、呼吸之外,好像很少有什麼事情,真的能從當年一路撐到現在。而「停班停課最新通知」竟然就是其中之一。 14 年前,這裡只是因為一個很單純的念頭開始:希望大家能更快知道重要資訊,希望混亂的時候,有一個地方可以讓人安心確認消息。 那時候的我們,未必想過這件事會走這麼久,更沒有想過,它後來會成為許多人颱風天、豪雨夜、地震後,第一時間想起的資訊節點。 這些年,我們一起經歷過很多天氣事件,也一起度過許多不安的時刻。每一次更新背後,其實都是希望大家能早一點知道、早一點準備,也早一點平安。 能走到今天,從來不是因為我們有多厲害,而是因為一路上有太多人願意相信、提醒、補充、分享,也在我們出錯或不足的時候,給予指正與包容。 這個頻道不只是我們建立的,也是大家一起陪它長大的。 謝謝你們,在每一次風雨之前、風雨之中、風雨之後,都還願意來到這裡。願我們都在變動的世界裡,繼續成為彼此平安的提醒。

    1.7K68270148K viewsView on X
  • Oct 9, 202612x their median

    我們要聲援「天氣即時預報」! 令人難以理解的是,「天氣即時預報」在新聞媒體發布的天氣相關 Threads 貼文下留言,試圖撥亂反正、澄清資訊,竟然疑似因此遭到封鎖。 新聞媒體經常引用氣象粉專的貼文作為報導素材,既然願意引用這些資訊,就應同樣尊重氣象粉專提出澄清與指正的權利。 如果媒體只願意引用氣象粉專的資訊,卻無法容納他們對天氣報導提出的不同意見,那麼,往後是否也不該再引用這些粉專的貼文? 新聞媒體應該追求事實,而不是只接受自己想聽的聲音。

    1.3K1615265K viewsView on X
  • Oct 17, 202612x their median

    🌀【颱風 #薔琵 快訊】 今年第29號颱風「薔琵」已於今(17)晚增強為強烈颱風。 (不是強屁= = 〔發布❯❯2026-10-17∙22:33〕 https://t.co/lgxZkKGnk6

    1.4K901423198K viewsView on X
  • Sep 3, 202611x their median

    🌀【颱風 #沙德爾 動態】 沙德爾再度登陸中國! 今年第18號颱風「沙德爾」重新增強後,今(3)日上午約6時30分於中國福建省漳州市漳浦縣古雷沿海登陸。 若依北京當局中央氣象台統計,這已是沙德爾生命史中的第3次登陸;此前曾於8月28日在浙江沿海兩度登陸,之後一度減弱,近期又重新發展。 這種「登陸、減弱、再增強、再登陸」的生命史並不常見,這是我們對「沙德爾」颱風的最後一次報告。 〔發布❯❯2026-09-03∙11:14〕

    1.2K107652687K viewsView on X
  • Aug 31, 202610x their median

    🌀【熱帶低氣壓 #TD20|沙德爾重新增強為熱帶性低氣壓 24小時內有再發展為颱風趨勢】 今年第18號颱風「沙德爾」先前已減弱為低氣壓,不過今(31)日上午再度增強為熱帶性低氣壓,預計未來24小時內仍有持續發展、再次增強為颱風的趨勢。 至於後續是否符合再次發布颱風警報的條件,目前仍有待觀察,中央氣象署將持續掌握其強度、路徑及可能影響。 〔發布❯❯2026-08-31∙12:31〕

    1.2K1153745178K viewsView on X
  • Oct 6, 20267.4x their median

    🌍【#國際疫情|俄羅斯西伯利亞實驗室出現疑似感染鼠疫死亡案例】 針對近日國際媒體報導,🇷🇺俄羅斯西伯利亞伊爾庫茨克州出現1例不明原因肺炎死亡病例,疾管署今(10/6)表示,依俄方官方聲明及 WHO 發言人說法,目前對一般大眾影響風險很低。 這起病例是一名28歲女性實驗室技術人員,任職於伊爾庫茨克防疫研究所。 她在出現嚴重肺炎後,於10月2日死亡,感染源疑似來自實驗室中的鼠疫病原體。 依俄羅斯官方傳染病管理機構 Rospotrebnadzor 聲明,俄方已針對該名個案檢體進行擴大檢驗,目前沒有發現與其工作職務相關的特殊微生物;俄羅斯生物安全專家委員會也表示,該實驗室並未發生病原體外洩意外。 俄方目前也已針對所有接觸者進行採檢與住院隔離觀察,沒有發現其他特殊傳染病病原體,相關接觸者也沒有出現異常症狀。 疾管署表示,當地一般民眾呼吸道疾病發病率仍在歷年季節性正常範圍內。不過,相關資訊仍待進一步釐清,後續仍需持續關注官方說明。 🔎 鼠疫(plague)是一種由鼠疫桿菌(Yersinia pestis)引起的細菌性人畜共通傳染病。它歷史上曾造成包括「#黑死病」在內的大規模流行,但現代已經不是「感染就等於沒救」的疾病;如果能及早診斷並接受適當抗生素治療,通常可以有效治療。 〔發布❯❯2026-10-07∙07:36〕

    753177201270K viewsView on X
  • Sep 21, 20266.2x their median

    你可以說我又發一次很擾民🥲 但還是拜託大家注意一下,稍後還會有演練警報! (小編默默吞下苦楚) https://t.co/DNWTy0qgff

    716816463K viewsView on X
  • Oct 14, 20265.9x their median

    💬【#小編的話|為什麼總有人說我們在炒作天氣?】 這幾年來,我們一直很好奇一個現象。 每當我們或其他天氣粉專發布天氣資訊時,總會有一些人出現,指點江山般地說我們在「#炒作天氣」、「#製造恐慌」,彷彿只要發布颱風動態、分享氣象預測,就是在刻意營造緊張氣氛。 但真正長期認識我們的朋友,應該都很清楚,#我們究竟在做什麼。 我們所整理、解讀的氣象資訊,都是來自政府機關、專業氣象機構,或其他具有明確來源、可以追溯查證的公開資料。 我們不會自己憑空畫出一條颱風路徑,再告訴大家颱風可能往哪裡走;更不會拿著一張不知道從哪裡來的預測圖,就當成確定會發生的事情。 我們做的是 #資訊整理與解讀,而不是 #憑空創造預測。 所以,我們其實一直很想問: 如果認為我們在炒作,能不能具體指出是哪一則資訊有問題?是哪一項資料不正確?還是哪一段解讀超出了原始資料所能支持的範圍? 如果有錯誤,我們當然應該接受檢驗。 但如果只是反覆用「炒作」、「恐慌」這些字眼來否定我們,卻始終沒有提出具體證據,那麼,這些說法又是從何而來? 我們不禁好奇,這究竟只是部分人士對氣象資訊有不同的理解,還是有人刻意透過這樣的方式,讓民眾逐漸不再信任台灣的天氣資訊粉專? 甚至,我們也曾懷疑,這背後是否可能涉及來自 #中國的組織性資訊操作? 當然,懷疑不代表事實,目前我們沒有證據可以證明這樣的情況確實存在。 我們只是把長期觀察到的現象,以及心中的疑問提出來。 我們始終相信,資訊是否可信,應該看它有沒有依據、能不能查證,而不是看誰最會貼標籤。 我們不怕被檢驗,也不認為自己永遠不會出錯。 但我們希望,對一個長期努力提供公共資訊的平台而言,所有批評都應該建立在具體事實之上。 監督可以讓資訊變得更好,但沒有證據的指控,不應該成為否定他人公信力的理由。 〔發布❯❯2026-10-14∙08:24〕

    6856117133K viewsView on X
  • Sep 6, 20265.8x their median

    氣溫非常舒服 https://t.co/P5HA3WJjKW

    70627111082K viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

Recurring topics

#薔琵#即時天氣#天氣小叮嚀#舒力基#彩雲

The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.

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Reading these numbers

A typical post picks up 130 interactions against 175K followers, an engagement rate of 0.075%. Measured over 47 original posts, its engagement rate beats 54% of 20,444 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 30K times each, and 0.434% of those impressions turn into an interaction. That is about 17.1% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.8 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 14:00 UTC, and Wednesday is the busiest day of the week. Of the 47 posts sampled, 98% carry an image or video and 9% are part of a thread. The account's strongest tracked post pulled 1.9K interactions, about 14x its own typical post. Recurring topics include #薔琵, #即時天氣, #天氣小叮嚀.

What is 停班停課最新通知 🇹🇼 Taiwan Alerts's engagement rate on X?
停班停課最新通知 🇹🇼 Taiwan Alerts (@TaiwanAlerts) has an engagement rate of 0.075%, based on the median interactions across 47 original posts from the last 30 days against 175,245 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.075%, 停班停課最新通知 🇹🇼 Taiwan Alerts sits above the 25th percentile of the 204,798 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @TaiwanAlerts have real engagement?
Its engagement rate beats 54% of the tracked X accounts closest to it in follower count (20,444 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
When does @TaiwanAlerts post?
Most posts go out around 14:00 UTC, and Wednesday is its busiest day, at roughly 1.77 posts per day across the measured window.

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