Social Influence Measurement 社群影響力衡量
Released已發布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'.
演算法技能：Social Influence Measurement 分析與應用。
Overview概述
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使用時機
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 演算法
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輸出格式
{
"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範例
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注意事項
- 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參考資料
- For audience authenticity detection methods, see
references/authenticity-detection.md - For influencer ROI measurement framework, see
references/influencer-roi.md