The operational manual for scoring articles and calculating your Media Reputation Score. Free, from Reputation Intelligence. Nothing you type leaves this page.
Instructions
Scoring Rubrics
Article Scorer
Pillar Definitions
Tagging Workflow
Purpose
This is the operational manual for scoring articles and calculating MRS. It is the most detailed tool in the toolkit — because execution is where most programs fail. A methodology is only as good as its consistent application.
The Key Insight: Scoring discipline matters more than scoring precision. A consistent 80% accurate system applied rigorously outperforms a theoretically perfect system applied inconsistently. Start with these rubrics, then calibrate over your first 100 articles.
How to Use This Tool
Step 1: Define Your Pillars
Go to the Pillar Definitions tab. For each reputation pillar identified through your PBI process, document its name, keywords, example headlines, and weight in the overall MRS.
Step 2: Learn the Scoring Rubrics
Review the Scoring Rubrics tab. Understand each of the five scoring dimensions — Source Authority, Prominence, Salience, Sentiment, Key Message Bonus, and Voice Bonus — before you score your first article.
Step 3: Score Articles
Use the Article Scorer tab to score individual articles. The tool calculates your raw article score automatically. Export your log to Excel for MRS roll-up calculations.
Step 4: Follow the Tagging Workflow
See the Tagging Workflow tab for daily, weekly, monthly, and quarterly cadences. Consistent workflow execution is what separates programs that sustain from programs that stall.
Step 5: Calibrate After 100 Articles
After scoring your first 100 articles, review your distribution. If scores are consistently clustered at the top or bottom, adjust your Calibration Factor. Your goal is a roughly normal distribution centered around 50.
Section B: Scoring Rubrics
Five dimensions combine to produce each article's MRS contribution. Apply these consistently across every article.
Source Authority (0–100)
Priority Outlets (Henrik's named)100
Tier 1 National — WSJ, NYT, Bloomberg, FT75
Tier 1 Trade — Leading industry pubs65
Tier 2 — Regional, specialized50
Tier 3 — General, blogs, smaller outlets25
Prominence Multiplier (1.0–1.5)
Headline — Brand in headline, primary focus1.5×
Lead — Brand in first paragraph1.3×
Body-Significant — Multiple paragraphs1.2×
Body-Mentioned — Clear mention1.1×
Passing — Brief reference1.0×
Salience Multiplier (0.8–1.5)
Sole Focus — Only your brand1.5×
Primary — You're the main subject1.3×
Shared — Equal with 1–2 competitors1.1×
Roundup — One of many1.0×
Competitor Focus — You're mentioned0.8×
Sentiment Multiplier (0.5–1.5)
Very Positive — Strong praise, endorsement1.5×
Positive — Generally favorable1.25×
Neutral — Factual, balanced1.0×
Negative — Critical, concerns0.75×
Very Negative — Damaging, crisis-level0.5×
Key Message Bonus (−20 to +30)
Strong — 2+ key messages, positive framing+30
Moderate — 1 key message, positive framing+20
Weak — Themes present indirectly+10
None — No alignment+0
Counter — Contradicts key messages−20
Voice Bonus (−15 to +25)
Expert Endorsement — Third-party praises brand+25
Executive Thought Leadership — Exec quoted as expert+20
Pillar Score = Average of article scores mapped to that pillar
Overall MRS = Weighted average of pillar scores (weights from PBI)
Article MRS Score
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Fill in scoring dimensions below
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Raw Score
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Base (Auth×Prom×Sal×Sent)
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Bonuses (KM + Voice)
Article Details
Scoring Dimensions
Date
Publication
Headline
Pillar
Authority
Prom.
Salience
Sentiment
KM
Voice
Score
Section A: Pillar Definitions
For each pillar identified through your PBI process, document the information below. Pillar definitions ensure consistent tagging across your team — especially important when AI pre-classification is involved.
Section D: Tagging Workflow
Consistent workflow execution is what separates sustainable programs from those that stall after the first month. Follow this cadence religiously for the first 90 days, then adjust based on what you learn.
Daily
→ Platform export
→ AI initial processing
→ Queue by tier
Weekly
→ Human review of Priority/T1 articles
→ Score and tag
→ QC sample check
Rule of Thumb: If your team is spending more than 2 hours per week on manual scoring after Month 2, your AI pre-classification workflow isn't working. Revisit your query setup and tier definitions before adding headcount.
