X (Twitter) Tweet Lifespan & Organic Decay Calculator

The calculator needs the view count of one post at several moments after posting. Three or more marks give a fit that can be checked.

1. Post, then note the view count at set times, such as 30 minutes, 1, 2, 4 and 8 hours.
2. Pick hours or minutes as the time unit.
3. Type each pair on its own line: the time, a space, then the running view count.
4. Read the fitted half-life, then check the fit error and the table under the result.

Tweet lifespan is the period in which a post on X collects most of its views, and organic decay is the slowdown of new views over that period. Organic means views that did not come from paid promotion. The X developer documentation separates organic metrics from promoted metrics.

Half-life is the standard measure of decay. OpenStax defines half-life as the length of time an exponentially decaying quantity takes to fall to half its original amount. Here the decaying quantity is the pace of new views.

X publishes no lifespan or half-life for posts. The X Help pages read on 2026-10-03 state no such figure. A fixed claim such as "a tweet lives for 18 minutes" has no official source, and posts differ widely.

This page fits a curve to one post you have tracked. It does not forecast a post you have not published. For the decay of other quantities, the general half-life calculator applies the same law.

1. The tool reads your time marks and running view counts.
2. It tries 401 half-lives, spaced evenly on a log scale from 1/100 of your first mark to 100 times your last mark.
3. For each half-life it finds the final total that fits best. That step is linear least squares with one unknown.
4. It keeps the half-life with the smallest sum of squared gaps, then refines it with a golden-section search.
5. It reports the average percent gap between your counts and the curve as the fit error.
6. It finds the time at which your counts passed half of the latest count by straight-line interpolation.

The NIST handbook describes linear least squares regression as by far the most widely used modeling method. The curve assumes one smooth burst of attention. The fit error shows how well that assumption holds for your post.

Example values only. A post shows these running view counts: 300 at 0.5 hours, 560 at 1 hour, 950 at 2 hours, 1,400 at 4 hours and 1,750 at 8 hours.

• Fitted half-life = 1.94 hours.
• Fitted final total = 1,854 views, as a projection.
• Share so far = 1,750 / 1,854 = 94.4%.
• 90% of the fitted total = 1.94 x 3.32 = 6.46 hours after posting.
• Fit error = 0.6% average gap.
• Half of the latest count, 875 views, was passed at 1 + (875 - 560) / (950 - 560) = 1.81 hours.
• Latest pace = (1,750 - 1,400) / 4 = 87.5 views per hour.

The curve projects about 104 more views after the 8 hour mark. The second preset adds real marks at 24 and 48 hours, where the count reaches 1,910. The late trickle sits above the curve, which is common.

A Second Wave

A Repost or Quote from a large account starts a new burst hours later. One curve cannot follow two bursts, and the fit error rises.

Where the Marks Sit

Marks only in the first hour show a near-straight line. The tool then reports that views are not slowing yet. Include marks from after the pace has dropped.

A Late First Mark

A first mark taken after most views arrived hides the early curve. Take one mark within the first half hour.

The Long Tail

Posts keep collecting a few views from search and profile visits for days. A single half-life underestimates that tail.

Number of Marks

Two marks fit the curve exactly and leave nothing to check. Each extra mark tests the shape.

Promotion

Paid views follow the ad schedule. Fit organic posts only, or the curve describes the campaign.

The fitted half-life comes from a model, and the observed halfway time comes straight from your marks. The two agree when the post has finished and decayed smoothly. The table shows how much of the final total the model places inside each half-life.

The observed halfway time uses the latest count as its total. On a post that is still growing it reads early, because the latest count is below the final total. For the share that any count is of a total, the percentage calculator gives the figure.

Learning Your Own Pattern

Track 5 to 10 posts and note each fitted half-life. Your own range replaces any generic claim.

Timing a Follow-Up

The 90% line shows when a post has done most of its work. That moment suits a reply, a thread addition or a new post.

Comparing Formats

Fit a text post and a video post from the same week. Compare half-lives and final totals side by side.

Reporting Early

The fitted final total gives a labelled projection before a campaign post has finished. Pair it with the impression to engagement ratio calculator once the count settles.

1. Quoting a Universal Lifespan

No official figure exists. A fixed number of minutes describes someone else's sample, at best.

2. Defining Half-Life as Half the Lifespan

Half-life is a measured rate. It is not a lifespan divided by 2.

3. Entering Views per Interval

The tool expects running totals. Enter the count shown on the post at each mark.

4. Mixing Time Units

30 minutes is 0.5 hours. Keep every mark in the unit you selected.

5. Trusting a Projection from Early Marks

A fit made in the first hour extrapolates far. The tool warns when the half-life exceeds your last mark.

6. Ignoring the Fit Error

A loose fit means the post did not follow one smooth curve. Read the table for the mark where the gap opens.

The fit is the least-squares optimum for the model and the marks you enter. The model is a simplification, and the fit error measures how far your post departs from it.

The fitted final total and the 90% time are projections. They are arithmetic on your own marks under the stated model, and they are not predictions.

X publishes no lifespan for posts, so no official figure exists. Track the view count of your own post at set times and fit a half-life to it.

It is the time in which the pace of new views halves. In the example on this page the fitted half-life is 1.94 hours. Your posts will differ.

Two marks are the minimum, and they fit the curve exactly. Three or more marks let the tool report a fit error. Five marks spread over the first day work well.

Enter one of them for every mark. X Help calls the public number a view count. The X developer documentation defines impressions as the count of times a post appeared on a screen.

Your counts rise in a straight or steepening line. A decay curve needs a slowdown. Add marks from later in the day.

Yes. Posts collect a slow trickle of views for days, and a single half-life flattens sooner. The tool then shows about 100% for the share so far.

No. It is a projection: the level at which the best-fitting curve flattens. A later Repost or Quote can lift the real count well above it.

X Help states that it may take up to a minute after a post is posted for the count to show.

The X developer documentation states that non-public, organic and promoted metrics are only available through the API for posts created within the last 30 days. The public view count stays on the post.

No. The maths runs in your browser and nothing is sent to us. Save keeps a result only in this browser.

• View counts (X Help Center, read 2026-10-03; view count definition, repeat views, up to a minute for a count to show).
• X API metrics: fields and definitions (X developer documentation, read 2026-10-03; impression definition, organic and promoted metrics, 30-day limit for non-public metrics).
• OpenStax College Algebra 2e: exponential and logarithmic models (OpenStax, Rice University, read 2026-10-03; exponential decay model and the definition of half-life).
• NIST e-Handbook: least squares regression (NIST/SEMATECH e-Handbook of Statistical Methods, read 2026-10-03; least squares as the method for fitting a model to data).