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MIT Sloan School of Management engagement report

@MITSloan - 307K followers on X

Measured over 41 original posts from a 30-day window, last computed on October 17, 2026.

Engagement

Bottom quarter for its size
Per follower
0.007%
of 307K followers
Per impression
0.91%
2.3K views on a typical post
Reach
0.75%
of its followers see a post
Typical post
21
interactions (median)
Saved
0.39%
9 bookmarks on a typical post
Posting rate
1.47/day
active 47% of days
Peak time
23:00 UTC
Thursday

A typical post picks up 21 interactions against 307K followers, an engagement rate of 0.007%. Measured over 41 original posts, its engagement rate beats 20% of 20,444 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 2.3K times each, and 0.91% of those impressions turn into an interaction. That is about 0.751% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.5 posts a day over the last 30 days, with activity on roughly 47% of days. Most posts go out around 23:00 UTC, and Thursday is the busiest day of the week. Of the 41 posts sampled, 12% carry an image or video and 100% link out. The account's strongest tracked post pulled 1.4K interactions, about 64x its own typical post.

Measured over 41 original posts from a 30-day window, last computed on October 17, 2026.

Compared with accounts its own size

MIT Sloan School of Management's engagement rate beats 20% 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 40% 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 0.007%, MIT Sloan School of Management sits above the 10th percentile of the 205,261 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.

p100.004%
p250.024%
p50 (median)0.143%
p750.678%
p902.55%
p9982.4%
Engagement rate as a share of followers, across the 205,261 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,894 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 23:00 UTC, and Thursday 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: 23:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 23:00 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: Thursday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Thursday
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 video12% of posts+111%+108% to +115%34K
Outbound link100% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 12% 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.
  • 100% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
  • Its average post runs 239 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

  • Oct 7, 202664x their median

    What tasks should AI be trusted with, and when should humans remain involved? Designing those decision rights is one of the defining challenges of the AI era. Researchers at @MIT_CISR have developed a framework to help. Learn more: https://t.co/QuqQxI4X8x https://t.co/DFrTfPKgU0

    1.0K296172756K viewsView on X
  • Oct 13, 202635x their median

    What tasks should AI be trusted with, and when should humans remain involved? Designing those decision rights is one of the defining challenges of the AI era. Researchers at the MIT Center for Information Systems Research have developed a framework to help. Learn more: https://t.co/NbTi0Vvd7Z

    548167121421K viewsView on X
  • Aug 22, 202622x their median

    MIT economists @DAcemogluMIT, @davidautor, and @baselinescene offer a framework for understanding how different technologies affect workers. Only new task-creating technologies are “unambiguously pro-worker,” because they create demand for new forms of human expertise rather than make existing expertise less necessary. Learn more: https://t.co/WWlLWn6PVj

    3588381419K viewsView on X
  • Sep 5, 202615x their median

    A new study asked 272 experts to evaluate AI risks and the sectors that are most vulnerable over the next five years. Learn more: https://t.co/0Eq7ES0QiY https://t.co/sx4hkBu79i

    214915812K viewsView on X
  • Aug 30, 202610x their median

    Experts said the five risks with highest expected severity are AI possessing dangerous capabilities, competitive dynamics, weapons and cyberattacks, power centralization, and AI spreading false or misleading information. https://t.co/NESQAI84FN

    1356710712K viewsView on X
  • Sep 10, 20267.1x their median

    Companies that realize returns from AI do so because they change the way they operate, according to MIT Sloan senior lecturer George Westerman. He developed six questions for business leaders to consider when implementing AI: https://t.co/d1m4BdhkAO https://t.co/PeLjsGVhuN

    12027215.9K viewsView on X
  • Sep 9, 20266.0x their median

    MIT economists @DAcemogluMIT, @davidautor, and @baselinescene offer a framework for understanding how different technologies affect workers. Only new task-creating technologies are “unambiguously pro-worker,” because they create demand for new forms of human expertise rather than make existing expertise less necessary. Learn more: https://t.co/WWlLWn6PVj

    10122317.1K viewsView on X
  • Oct 15, 20265.5x their median

    MIT Sloan professor Eric So believes that people who use artificial intelligence fall into one of two groups: those who use the technology to think better, and those who use AI instead of thinking. Without intervention, the latter group could pay a heavy price. https://t.co/ScrxXhAORd

    7330664.6K viewsView on X
  • Oct 11, 20265.3x their median

    In his new book, MIT Sloan professor Eric So examines the ways artificial intelligence is reshaping human cognition and offers advice on protecting your uniquely human skills while using AI. https://t.co/CeEVhnPZIK

    8621408.8K viewsView on X
  • Aug 26, 20264.4x their median

    Artificial intelligence presents business leaders with a difficult management problem: The risks are numerous and fast-moving and fall unevenly across organizations, sectors, and stakeholders. https://t.co/NESQAI84FN

    7018234.6K 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.

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Reading these numbers

A typical post picks up 21 interactions against 307K followers, an engagement rate of 0.007%. Measured over 41 original posts, its engagement rate beats 20% of 20,444 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 2.3K times each, and 0.91% of those impressions turn into an interaction. That is about 0.751% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.5 posts a day over the last 30 days, with activity on roughly 47% of days. Most posts go out around 23:00 UTC, and Thursday is the busiest day of the week. Of the 41 posts sampled, 12% carry an image or video and 100% link out. The account's strongest tracked post pulled 1.4K interactions, about 64x its own typical post.

What is MIT Sloan School of Management's engagement rate on X?
MIT Sloan School of Management (@MITSloan) has an engagement rate of 0.007%, based on the median interactions across 41 original posts from the last 30 days against 306,999 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.007%, MIT Sloan School of Management sits above the 10th percentile of the 205,261 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 @MITSloan have real engagement?
Its engagement rate beats 20% of the tracked X accounts closest to it in follower count (20,444 accounts), which puts it in the bottom quarter for its size 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 @MITSloan post?
Most posts go out around 23:00 UTC, and Thursday is its busiest day, at roughly 1.47 posts per day across the measured window.

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