---

name: "algo-social-engagement" description: "Calculate and benchmark social media engagement rates across platforms and variants. Use this skill when the user needs to compute engagement metrics, compare performance across accounts or posts, or set engagement benchmarks — even if they say 'what is my engagement rate', 'benchmark engagement', or 'social media KPIs'." metadata: category: "WP-38 社群演算法" tags: ["social-media", "engagement-rate", "analytics", "benchmarking"]

Engagement Rate Calculation

Overview

Engagement rate measures audience interaction relative to reach or audience size. Formula: (reactions + comments + shares) / denominator × 100%. The denominator choice (reach, impressions, followers) significantly affects the result. Computes in O(n) per post set.

When to Use

Trigger conditions:

• Computing engagement metrics for social media reporting
• Benchmarking account or post performance against industry averages
• Comparing content performance across posts or accounts

When NOT to use:

• When evaluating influence holistically (use influence measurement)
• When modeling content spread dynamics (use virality models)

Algorithm

IRON LAW: Engagement Rate Denominator MATTERS
By reach, by impressions, and by followers produce DIFFERENT numbers:
- ER by Reach = engagements / reach × 100% (most accurate, requires analytics access)
- ER by Impressions = engagements / impressions × 100% (always lower than by reach)
- ER by Followers = engagements / followers × 100% (public data, but inflated by non-reaching followers)
ALWAYS specify which variant when reporting or comparing.

Phase 1: Input Validation

Collect per post: likes, comments, shares/retweets, saves (platform-specific), reach or impressions or follower count. Gate: Consistent denominator across all posts being compared.

Phase 2: Core Algorithm

1. Sum engagements per post: likes + comments + shares (+ saves, clicks if available)
2. Weight engagements if desired: share=3×, comment=2×, like=1× (shares indicate higher commitment)
3. Divide by chosen denominator (reach preferred, followers as fallback)
4. Compute: per-post ER, average ER across posts, median ER, ER trend over time

Phase 3: Verification

Compare against platform benchmarks. Flag anomalies (ER > 20% likely data error or viral outlier). Gate: Results within plausible range for platform.

Phase 4: Output

Return engagement metrics with benchmarking context.

Output Format

{
  "metrics": {"avg_er_by_reach": 3.2, "avg_er_by_followers": 1.8, "median_er": 2.9, "top_post_er": 8.5},
  "benchmark": {"platform": "instagram", "industry": "fashion", "benchmark_er": 2.5, "percentile": 72},
  "metadata": {"posts_analyzed": 30, "period": "2025-Q1", "denominator": "reach"}
}

Examples

Sample I/O

Input: Post: 150 likes, 20 comments, 5 shares, reach=5000 Expected: ER by reach = (150+20+5)/5000 × 100% = 3.5%

Edge Cases

Input	Expected	Why
Reach = 0	Undefined, skip post	Can't divide by zero
Boosted/paid post	Separate from organic	Paid reach inflates denominator, deflates ER
Viral outlier (10x avg)	Flag, analyze separately	Skews averages

Gotchas

• Platform algorithm changes: Instagram's algorithm shifts regularly. Historical ER benchmarks become outdated. Use rolling 90-day benchmarks.
• Vanity metric trap: High ER doesn't mean business impact. 1000 likes on a meme ≠ 10 link clicks on a product post. Track meaningful engagements.
• Story/Reel metrics differ: Story engagement (taps, replies) and Reel engagement (plays, shares) need different formulas than feed posts. Don't mix.
• Follower-based ER is noisy: Not all followers see each post (reach < followers). ER by followers underestimates true engagement among those who saw the post.
• Comparing across account sizes: Smaller accounts naturally have higher ER by followers. Normalize or segment by account size for fair comparison.

References

• For platform-specific benchmark data, see references/platform-benchmarks.md
• For weighted engagement scoring models, see references/weighted-engagement.md