\"algo-social-engagement\"

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