ShareGPT

Use a weighted ranking algorithm that normalizes engagement across community size and highlights statistical outliers.

Goal: Equal visibility potential for small and large communities by emphasizing relative performance over absolute volume.

Outline:

1. Inputs

   • ( u ): upvotes
   • ( d ): downvotes
   • ( t ): post age (in hours)
   • ( n_c ): number of active users in the community
   • ( μ_c ), ( σ_c ): mean and standard deviation of engagement (e.g., score = upvotes − downvotes) within community ( c )

2. Compute raw score [ s = u - d ]

3. Normalize by community size [ s' = \frac{s}{\sqrt{n_c}} ] (Square root dampens the effect of community population size.)

4. Compute z-score (outlier detection) [ z = \frac{s' - μ_c}{σ_c} ] This measures how exceptional a post is compared to typical posts in its own community.

5. Apply time decay [ z_t = z \times e^{-λt} ] ( λ ) is a decay constant controlling how fast posts lose prominence.

6. Sort order

   • Primary key: ( z_t ) descending.
   • Secondary key: recency or total engagement.

Effect:

• A post that performs far above its community norm (high z-score) rises regardless of community size.
• Large communities no longer dominate since normalization and outlier weighting make “exceptional” small-community posts equally competitive.

Optional adjustments:

• Cap visibility to prevent manipulation (e.g., minimum engagement threshold).
• Use Bayesian shrinkage if communities have low post counts to stabilize ( μ_c ) and ( σ_c ).

This yields a discovery feed surfacing statistical outliers across all communities.