Post Reach Decay & Algorithmic Amplification

Post Setup

Follower audience size 50,000

Engagement rate 4.0%

Base reach half-life 6.0h

Algorithm boost threshold 3.0%

Live Analytics

Organic Algorithm boost Decayed

Total impressions

Reach velocity

0 /h

Effective half-life

6.0 h

Elapsed time

0.0 h

How it works

A post's organic reach decays exponentially once its initial audience has been exposed — the same "reach decay" pattern platforms observe in their own dashboards:

R(t) = R0 · 2^(-t / H_eff)

R0     = initial exposure rate (∝ audience size)
H_eff  = effective half-life (hours)
t      = time since publish (hours)

Engagement rate (likes+comments+shares ÷ impressions) feeds back into the distribution algorithm. When it clears a threshold, the algorithm re-injects the post into more feeds, which we model as an extension of the half-life:

H_eff = H_base · (1 + k · max(0, E − E_thresh))

E        = engagement rate
E_thresh = algorithm boost threshold
k        = amplification gain (fixed at 4.0 here)

Audience size — scales the initial burst of impression particles leaving the post.
Engagement rate — above the threshold, the post glows and its half-life stretches (algorithmic amplification); below it, decay runs at the unboosted base rate.
Half-life slider — the baseline organic decay speed before any boost is applied.
Publish new post — resets the clock and impression count to watch a fresh decay curve from t=0.

Real-world relevance: this is the same reach/impressions/engagement-rate relationship social platforms' own analytics dashboards report, and the amplification feedback loop mirrors how ranking algorithms redistribute high-engagement content to secondary audiences.

Impression particle Post node

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