Understanding Subject Scores
A subject score is a normalized value that reflects:
The proportion of flagged posts relative to posts analyzed, and
The average sentiment of the subject’s posts
under the report profile settings used for the check (flags, keywords, and related options).
It describes composition of analyzed activity—not a prediction of workplace behavior or organizational risk.
Continue → Step 2
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Step 2: Score range and how it appears
### Range
Subject scores run from 0 to 1000. Higher values generally mean fewer relative flags and/or more positive overall sentiment under your active settings; lower values mean the opposite. Treat the number as relative, not absolute.
### Where you see it
As part of subject status after a completed check
In subject reports (when included for the use case)
### Color coding
Scores are usually shown with a solid background:
Color (typical) | Relative band |
|---|---|
--- | --- |
Dark red | Lower score |
Orange | Mid-range score |
Dark green | Higher score |
Color is a visual aid only. Always open the underlying posts before drawing conclusions.
Continue → Step 3
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Step 3: How Ferretly calculates scores
Ferretly derives the score from analyzed social activity, including:
Content and detected behaviors against your flag settings
Sentiment of posts
Because the score depends on your profile settings, two organizations (or two profiles) can produce different scores for the same public activity. Changing flags or keywords and re-running (or refreshing after post-level edits) can change the score.
After you redress or edit posts, refresh reports so the score and PDF stay aligned.
Continue → Step 4
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Step 4: How to use scores responsibly
Appropriate use (when scoring is enabled for your use case)
Compare two or more subjects under the same report profile for a consistent, uniform view of flag and sentiment composition
Prioritize which subjects to review first in a large queue
Spot-check whether review and redress moved the composition after refresh
Not appropriate
Hire / no-hire or other FCRA employment decisions based on the number alone
Treating the score as a risk rating for the organization
Ignoring missing scores under FCRA as if they were “high” or “clean”
Always interpret scores in context (role, industry, volume of posts, and the specific flags you enabled). The posts remain the source of truth.
Continue → Step 5
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