Back to @0xVeryBigOrange's profile

很大很大的橙子 engagement report

@0xVeryBigOrange - 95K followers on X

Measured over 72 original posts from a 30-day window, last computed on September 17, 2026.

Engagement

Middle of its size range
Per follower
0.102%
of 95K followers
Per impression
0.4%
24K views on a typical post
Reach
25.4%
of its followers see a post
Typical post
96
interactions (median)
Saved
0.019%
4 bookmarks on a typical post
Posting rate
2.87/day
active 87% of days
Peak time
01:00 UTC
Friday

A typical post picks up 96 interactions against 95K followers, an engagement rate of 0.102%. Measured over 72 original posts, its engagement rate beats 51% of 14,930 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 24K times each, and 0.4% of those impressions turn into an interaction. That is about 25.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.9 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 01:00 UTC, and Friday is the busiest day of the week. Of the 72 posts sampled, 69% carry an image or video, 7% are part of a thread and 6% link out. The account's strongest tracked post pulled 1.7K interactions, about 18x its own typical post.

Measured over 72 original posts from a 30-day window, last computed on September 17, 2026.

Compared with accounts its own size

很大很大的橙子's engagement rate beats 51% of the tracked X accounts closest to it in follower count (14,930 accounts, accounts of similar size (decile 6 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 20% 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 0.102%, 很大很大的橙子 sits above the 25th percentile of the 151,319 accounts in this comparison. That places it in the below the median band, which runs 0.021% to 0.126%.

p100.003%
p250.021%
p50 (median)0.126%
p750.6%
p902.30%
p9984.3%
Engagement rate as a share of followers, across the 151,319 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 27,202 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 percentile0.003%
25th percentile0.021%
50th percentile0.126%
75th percentile0.6%
90th percentile2.30%
99th percentile84.3%

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 01:00 UTC, and Friday 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: 01:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 01:00 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: Friday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Friday
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 video69% of posts+111%+108% to +115%34K
Outbound link6% of posts-41%-42% to -40%32K
Typical length--3%-3% to -2%54K
  • 69% 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.
  • 6% 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 94 characters, which falls in the 80 - 180 characters band. Across the catalog, posts of 80 to 180 characters run 3% below the same accounts' other posts.

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

  • Aug 18, 202618x their median

    冯小刚: 我《抓特务》票房1.2亿 投资3亿 信雨萌: 我《牛来》预计票房也是1.2亿 投资3万 冯小刚:???

    1.2K185309348K viewsView on X
  • Sep 4, 202614x their median

    川普身子骨要不行了? https://t.co/f2kCTssNkb

    1.2K296911315K viewsView on X
  • Aug 9, 20268.8x their median

    这一代年轻人似乎不愿去留学了。经济学人分析统计,中国愿出去留学的人数,正加速下降,同期在海外毕业回国的人群比例,正加速上升。 欧美留学成本持续增加,海归与本地高校毕业生薪资差距缩小,毕业留在当地的工签名额愈加紧张,成为了主要原因。 https://t.co/p0BPK2frfd

    4624931421164K viewsView on X
  • Mar 27, 20258.7x their median

    啥也不说了,看图吧。 https://t.co/sHIslVtS32

    469343315400K viewsView on X
  • Aug 28, 20267.9x their median

    😂😂😂 https://t.co/JIFEEMEwZI

    631141104101K viewsView on X
  • Aug 29, 20265.9x their median

    币圈男有几个典型特点:精算、赌性强、爱套利,能白嫖绝不付费。 这个其实也没办法,币圈可能是全球金融衍生品最齐全、套利机会最多的市场之一。一个原本完全不懂金融的人,在币圈摸爬滚打几年,经历过合约、期权、杠杆、清算、套保,再玩过一轮 DeFi,实际金融博弈经验未必比金融 PhD 差。 尤其 DeFi 出来以后,借贷、循环贷、LP、对冲、套利,各种金融结构天天拿真金白银练手。 所以真要把币圈男当“猎物”,其实是最难捞的一群人。 你以为你在算他的钱,他可能从第一天就在算你的赔率、成本和退出路径。 就算前面真让你捞到了,最后也很容易被反向套利。 所以捞女去捞币圈男,往往是最不划算的一门生意。 你以为自己在做局,他可能从认识你的第一天,就已经把风险收益比算完了。 币圈最不缺的,就是见过泡沫、骗局、暴涨暴跌之后,还能面不改色的人。

    45048561181K viewsView on X
  • Aug 11, 20265.6x their median

    昨天体验了一下带呼吸机睡觉,睡眠质量提高巨大,我平时半夜要醒四五次,昨晚只醒了一次,强烈推荐! https://t.co/aU0p2gKcBA

    317172004144K viewsView on X
  • Aug 10, 20264.8x their median

    勋勋,币圈哲学家,21年就靠炒币赚了A9的哥们。 https://t.co/zITadElGbQ

    23318210455K viewsView on X
  • Aug 17, 20263.9x their median

    干细胞千万不要打了,有明确验证结果打了干细胞未来致癌率提升80%。

    1254248082K viewsView on X
  • Aug 19, 20263.6x their median

    多年以后大家发现,原来《牛来》的上映是大牛市的起点。

    2671662441K 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.

Buy or sell Twitter (X) accounts - escrow-protected

PlayerSells is an escrow marketplace for Twitter (X) accounts. Every deal is protected, with no middleman risk.

Reading these numbers

A typical post picks up 96 interactions against 95K followers, an engagement rate of 0.102%. Measured over 72 original posts, its engagement rate beats 51% of 14,930 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 24K times each, and 0.4% of those impressions turn into an interaction. That is about 25.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.9 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 01:00 UTC, and Friday is the busiest day of the week. Of the 72 posts sampled, 69% carry an image or video, 7% are part of a thread and 6% link out. The account's strongest tracked post pulled 1.7K interactions, about 18x its own typical post.

What is 很大很大的橙子's engagement rate on X?
很大很大的橙子 (@0xVeryBigOrange) has an engagement rate of 0.102%, based on the median interactions across 72 original posts from the last 30 days against 95,319 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.102%, 很大很大的橙子 sits above the 25th percentile of the 151,319 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 @0xVeryBigOrange have real engagement?
Its engagement rate beats 51% of the tracked X accounts closest to it in follower count (14,930 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 @0xVeryBigOrange post?
Most posts go out around 01:00 UTC, and Friday is its busiest day, at roughly 2.87 posts per day across the measured window.

Keep going