Microsoft Research engagement report
@MSFTResearch - 556K followers on X
Measured over 12 original posts from a 30-day window, last computed on October 13, 2026.
Engagement
A typical post picks up 81 interactions against 556K followers, an engagement rate of 0.015%. Measured over 12 original posts, its engagement rate beats 39% 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 14K times each, and 0.588% of those impressions turn into an interaction. That is about 2.48% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.4 posts a day over the last 30 days, though only 33% of days saw any activity at all. Most posts go out around 16:00 UTC, and Monday is the busiest day of the week. Of the 12 posts sampled, 100% carry an image or video and 100% link out. The account's strongest tracked post pulled 319 interactions, about 3.9x its own typical post.
Measured over 12 original posts from a 30-day window, last computed on October 13, 2026.
Compared with accounts its own size
Microsoft Research's engagement rate beats 39% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 10 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 34% 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 0.015%, Microsoft Research sits above the 10th percentile of the 204,880 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.678% |
| 90th percentile | 2.55% |
| 99th percentile | 82.4% |
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 16:00 UTC, and Monday 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 | 100% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 100% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 100% 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 309 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above 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 3, 20263.9x their median
Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure. https://t.co/uxVNQdB1fQ https://t.co/lcTRD9gMdO
- Jul 30, 20263.7x their median
LLMs do not get smarter just by remembering more. EvoLib turns experience into evolving knowledge, taking reusable skills and insights that help models learn and adapt across tasks long after deployment. https://t.co/FR89BgW6Vx https://t.co/CiNfXGb3ux
- Sep 29, 20263.3x their median
Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab. Experimental results provide important feedback, helping researchers sharpen future research directions. https://t.co/gt8Dt2I5sa
- Oct 12, 20262.6x their median
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics: https://t.co/Sktw7dt8fj
- Sep 21, 20262.5x their median
Custom-made molecules are advancing medicine, materials, and agriculture, but producing them is slow and expensive. A new Nature paper highlights RetroChimera, a predictive model that helps accelerate chemical synthesis, helping researchers explore a wide range of molecules.: https://t.co/BOfKphfhh3
- Aug 3, 20262.1x their median
Small language models learn to negotiate with SocialRL, PazaBench V2 expands speech AI evaluation across African languages, and EvoLib helps agents turn experience into knowledge. Plus: new methods for more reliable A/B testing and advances in AI-driven precision oncology. https://t.co/0N1CGFXZfj
- Sep 23, 20262.1x their median
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. https://t.co/Ks9cUCbn9r https://t.co/nMrI6xRX4t
- Oct 7, 20262.0x their median
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them.: https://t.co/oInaVHBmIS
- Jul 30, 20261.9x their median
Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve. https://t.co/8STvmt2miZ
- Aug 4, 20261.7x their median
Can AI learn pathology through clinical dialogue? Introducing PRISM2, a multimodal foundation model trained on pathology images and language from real pathology reports. Using simple question-answering, PRISM2 matches specialized cancer-detection systems across several benchmark tasks without requiring a separate model for each task. https://t.co/nFcMESL9Gh
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 81 interactions against 556K followers, an engagement rate of 0.015%. Measured over 12 original posts, its engagement rate beats 39% 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 14K times each, and 0.588% of those impressions turn into an interaction. That is about 2.48% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.4 posts a day over the last 30 days, though only 33% of days saw any activity at all. Most posts go out around 16:00 UTC, and Monday is the busiest day of the week. Of the 12 posts sampled, 100% carry an image or video and 100% link out. The account's strongest tracked post pulled 319 interactions, about 3.9x its own typical post.
- What is Microsoft Research's engagement rate on X?
- Microsoft Research (@MSFTResearch) has an engagement rate of 0.015%, based on the median interactions across 12 original posts from the last 30 days against 556,164 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.015%, Microsoft Research sits above the 10th percentile of the 204,880 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 @MSFTResearch have real engagement?
- Its engagement rate beats 39% 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 @MSFTResearch post?
- Most posts go out around 16:00 UTC, and Monday is its busiest day, at roughly 0.4 posts per day across the measured window.