Social Media Post Virality & Engagement Decay

Post Setup

Follower count 50.0K

Content quality 60

Avg reach per share 120

Decay half-life (h) 6.0

Posting Time

Live Analytics

Impressions

Reach (unique)

Engagement rate

0.0%

Virality coefficient 0.00

How it works

A post's raw impression rate decays exponentially after publishing, driven by feed-ranking algorithms deprioritizing older content:

I(t) = I0 · e^(-t/τ),  τ = half-life / ln(2)
I0 = 0.05 · followers · postingMultiplier

Each impression converts to an engagement with probability p_e (scales with content quality), and each engagement converts to a share with probability p_s. Shares expose new audiences, feeding a decaying "viral pool" V(t) that adds back into the impression rate — the same feedback loop behind any viral-growth model:

engRate  = (I(t)+V(t)) · p_e
shrRate  = engRate · p_s
V(t+dt)  = V(t)·e^(-dt/(τ/2)) + shrRate · reachPerShare · dt

The virality coefficient (K-factor), the same metric used to evaluate real growth loops, tells you whether the secondary wave compounds or dies out:

K = p_s · reachPerShare · p_e

K < 1 — each engaged user produces less than one new engagement on average; the loop decays and the post's reach is bounded by its initial audience.
K > 1 — each engaged user produces more than one new engagement; the secondary wave compounds and total reach keeps climbing even as the original post decays — the definition of "going viral".

Reach (unique accounts) is estimated as cumulative impressions discounted for repeat views, capped at an extended-network ceiling of ~40× the follower count via shares.

t = 0.0h since publish

Predicted 24h impression curve Live engagement pulse

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Social Media Post Virality & Engagement Decay Simulator