MIT Sloan School of Management engagement report
@MITSloan - 305K followers on X
Measured over 38 original posts from a 30-day window, last computed on September 5, 2026.
Engagement
A typical post picks up 34 interactions against 305K followers, an engagement rate of 0.011%. Measured over 38 original posts, its engagement rate beats 26% of 6,874 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 3.0K times each, and 1.11% of those impressions turn into an interaction. That is about 0.991% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, with activity on roughly 47% of days. Most posts go out around 22:00 UTC, and Wednesday is the busiest day of the week. Of the 38 posts sampled, 11% carry an image or video and 100% link out. The account's strongest tracked post pulled 463 interactions, about 14x its own typical post.
Measured over 38 original posts from a 30-day window, last computed on September 5, 2026.
Compared with accounts its own size
MIT Sloan School of Management's engagement rate beats 26% of the tracked X accounts closest to it in follower count (6,874 accounts, accounts of similar size (decile 7 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 49% 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 0.011%, MIT Sloan School of Management sits above the 10th percentile of the 66,128 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.016%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.016% |
| 50th percentile | 0.1% |
| 75th percentile | 0.499% |
| 90th percentile | 2.09% |
| 99th percentile | 119.9% |
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 22:00 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.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 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 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
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.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 11% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 100% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | no effect | -2% to -1% | 42K |
- 11% 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 220 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
- Aug 22, 202614x 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
- Aug 30, 20266.4x 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
- Aug 26, 20262.7x 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
- Aug 23, 20262.4x their median
At the MIT IDE’s 2026 annual conference, MIT Sloan’s Andrew McAfee discussed what it takes to build a successful company in the modern economy and offered three predictions on who will succeed. https://t.co/jLxM9yo5g6
- Aug 31, 20262.1x 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/StYHsGIiEJ
- Aug 24, 20261.8x their median
In an AI-enabled startup, humans should act as future-forward architects, supplying the judgment, relationships, creativity, and experimentation that machines can’t replicate. https://t.co/I6AHYZDUqq
- Sep 1, 20261.6x their median
Companies building and marketing digital advisers can win users’ trust by positioning AI as a safe, judgment-free tool. https://t.co/sU29Y5bNR2
- Aug 29, 2026
“The question is still ‘Do you have a paying customer? Are you profitable? Do you actually have product-market fit? Do you have a channel-market fit?’ And that question is becoming more elusive because it’s so easy to move quickly,” senior lecturer Jenny Larios Berlin said. https://t.co/xPjGXDt3zm
- Aug 27, 2026
MIT Sloan professor of the practice Bill Aulet sees AI as an increasingly essential tool in the entrepreneurial arsenal, not something to fear. AI can help founders sharpen their judgment, develop products, test hypotheses, research markets, and experiment — lowering the barrier to starting a company. https://t.co/xPjGXDt3zm
- Sep 3, 2026
A new paper co-authored by MIT Sloan associate professor Mert Demirer concluded that although AI tools substantially boost productivity when software developers write code, the effect is muted when it comes to shipping code. https://t.co/YtNIwoHwtz
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 34 interactions against 305K followers, an engagement rate of 0.011%. Measured over 38 original posts, its engagement rate beats 26% of 6,874 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 3.0K times each, and 1.11% of those impressions turn into an interaction. That is about 0.991% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, with activity on roughly 47% of days. Most posts go out around 22:00 UTC, and Wednesday is the busiest day of the week. Of the 38 posts sampled, 11% carry an image or video and 100% link out. The account's strongest tracked post pulled 463 interactions, about 14x 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 0.011%, based on the median interactions across 38 original posts from the last 30 days against 305,331 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.011%, MIT Sloan School of Management sits above the 10th percentile of the 66,128 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 26% of the tracked X accounts closest to it in follower count (6,874 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 @MITSloan post?
- Most posts go out around 22:00 UTC, and Wednesday is its busiest day, at roughly 1.3 posts per day across the measured window.