algo-social-influence

Original：🇺🇸 English

Translated

Measure social media influence using engagement-weighted metrics beyond follower count. Use this skill when the user needs to evaluate influencer effectiveness, compare influence across accounts, or build an influence scoring system — even if they say 'who is more influential', 'influencer ranking', or 'measure social impact'.

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Sourceasgard-ai-platform/skills

Added on2026-04-15

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npx skill4agent add asgard-ai-platform/skills algo-social-influence

SKILL.md Content

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Social Influence Measurement

Overview

Influence scoring evaluates an account's ability to drive actions (engagement, sharing, conversions) beyond mere reach. Combines reach, resonance (engagement depth), and relevance (topical authority). Computes as weighted composite score.

When to Use

Trigger conditions:

Evaluating and comparing influencers for marketing campaigns
Building an influence scoring or ranking system
Assessing brand ambassador effectiveness

When NOT to use:

When measuring content virality dynamics (use viral spread models)
When computing basic engagement rates (use engagement rate calculator)

Algorithm

IRON LAW: Follower Count ≠ Influence
Influence requires ENGAGEMENT. An account with 1M followers and
0.01% engagement rate has less influence than one with 10K followers
and 5% engagement. Measure: reach × engagement rate × relevance.

Phase 1: Input Validation

Collect per account: follower count, avg likes/comments/shares per post, posting frequency, audience demographics, topic categories. Gate: Minimum 20 recent posts for stable metrics.

Phase 2: Core Algorithm

Reach score: Normalize follower count to log scale (diminishing returns)
Engagement score: (avg engagements / followers) × 100, weighted by type (share > comment > like)
Relevance score: Topic overlap between influencer content and target campaign
Composite: Influence = w₁×Reach + w₂×Engagement + w₃×Relevance (weights tuned per campaign goal)
Adjust for: audience authenticity (bot follower %), post frequency consistency

Phase 3: Verification

Spot-check: do high-scoring accounts actually drive actions? Cross-reference with historical campaign performance data if available. Gate: Top-ranked accounts have demonstrable engagement history.

Phase 4: Output

Return ranked influence scores with component breakdown.

Output Format

json

{
  "rankings": [{"account": "@handle", "influence_score": 82, "reach": 75, "engagement": 90, "relevance": 85}],
  "metadata": {"accounts_analyzed": 50, "weights": {"reach": 0.2, "engagement": 0.5, "relevance": 0.3}}
}

Examples

Sample I/O

Input: Account A: 500K followers, 0.5% engagement. Account B: 50K followers, 4.2% engagement. Same relevance. Expected: B scores higher due to engagement dominance in weighting.

Edge Cases

Input	Expected	Why
Viral one-hit account	High recent engagement, low stability	Need temporal consistency check
Celebrity with low engagement	High reach, low influence per dollar	Reach-only strategy, expensive
Micro-influencer niche	High relevance + engagement	Best ROI for targeted campaigns

Gotchas

Fake engagement: Bot likes/comments inflate metrics. Use authenticity tools (HypeAuditor, etc.) to detect.
Platform differences: 2% engagement on Instagram is average; 2% on Twitter/X is excellent. Normalize by platform benchmarks.
Engagement pods: Groups of influencers artificially engaging with each other's content. Check if engagement comes from diverse sources.
Influence ≠ conversion: High engagement doesn't guarantee purchase intent. Track downstream metrics (link clicks, promo code usage) for campaign ROI.
Temporal decay: Influence changes. Quarterly reassessment is minimum; monthly is better for fast-moving categories.

References

For audience authenticity detection methods, see
```
references/authenticity-detection.md
```
For influencer ROI measurement framework, see
```
references/influencer-roi.md
```