Documentation Index
Fetch the complete documentation index at: https://learn.social.plus/llms.txt
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Identify
Evaluate
Filter
Sort
Reward
Support
Overview
Community managers face a fundamental challenge: identifying who truly adds value versus who just adds noise. High activity doesn’t always mean high impact. A user rapidly posting low-quality content creates volume without value, while thoughtful contributors creating insightful posts may stay hidden in the metrics. This feature solves that problem with two intelligent scoring systems:- Contribution Level: Transparent points-based scoring that rewards meaningful actions (posts, comments, reactions)
- Content Quality Level: Average quality score of all user-generated posts
Key Benefits
Data-Driven Decisions
Quick Identification
Strategic Insights
Transparent Scoring
Accessing User Insights
View Score Labels
Apply Filters
Sort for Priority
Understanding Contribution Levels
Contribution Level measures how actively a user participates in your community through posts, comments, and reactions. It’s calculated using a transparent points-based system over the last 30 days.Scoring System
Points Breakdown
Points Breakdown
- Create a post: 10 points
- Write a comment: 5 points
- Add a reaction: 1 point
Contribution Level Labels
Contribution Level Labels
| Label | Points Range | Meaning |
|---|---|---|
| Very High | 501+ points | Power contributor driving significant engagement |
| High | 201-500 points | Active contributor with consistent participation |
| Medium | 51-200 points | Regular contributor with steady activity |
| Low | 1-50 points | Emerging contributor, just getting started |
| None | 0 points | No contribution activity in the last 30 days |
Customizable Weights (API)
Customizable Weights (API)
- Emphasize posts if you value original content creation
- Increase comment weights to reward discussion participation
- Adjust reaction values if engagement style differs
Understanding Content Quality Levels
Content Quality Level measures how well a user creates content, based on the average quality score of their posts. This helps identify users who consistently produce valuable, well-structured content versus those posting spam or low-effort material.Quality Assessment
How Quality is Calculated
How Quality is Calculated
- Individual Post Scores: Each post receives a quality score (based on AI content moderation and community signals)
- User Average: The user’s Content Quality Level is the average of all their post scores
- Active Posts Only: Deleted posts are excluded from the calculation
Content Quality Level Labels
Content Quality Level Labels
| Label | Description | Typical Characteristics |
|---|---|---|
| Excellent | Highest quality content | Insightful, well-structured, valuable posts |
| Good | Above-average quality | Solid content with clear value |
| Fair | Acceptable quality | Adequate posts, room for improvement |
| Weak | Below-average quality | Low-effort or problematic content |
| Broken | Violates community standards | Spam, inappropriate, or harmful content |
| No Posts | User hasn’t created posts | New users or comment-only participants |
Using Insights in Manage Users
Display and Tooltips
Both Contribution Level and Content Quality Level are displayed prominently on:- Manage Users Page: See labels for all users at a glance
- User History Panel: View scores when investigating individual users
- How the score is calculated in general
- The specific threshold or range for that label
- What the label means for user behavior
Sorting Capabilities
Click column headers to sort the user list and quickly identify specific segments:Sort by Contribution Level
Sort by Contribution Level
- Highest First: Very High → High → Medium → Low → None (identify top contributors)
- Lowest First: None → Low → Medium → High → Very High (find inactive or low-engagement users)
Sort by Content Quality Level
Sort by Content Quality Level
- Highest First: Excellent → Good → Fair → Weak → Broken → No Posts (surface quality creators)
- Lowest First: Broken → Weak → Fair → Good → Excellent → No Posts (flag problem content)
Secondary Sorting
Secondary Sorting
Pagination Handling
Pagination Handling
Advanced Filtering
Focus on specific user segments by filtering on Contribution Level or Content Quality Level—or both together:Contribution Level Filter
Contribution Level Filter
- Very High (🔥): Your power users
- High (💪): Strong contributors
- Medium (⚡): Regular participants
- Low (🌱): Emerging members
- None (👻): Inactive users
- Filter for “Very High” to identify users for a rewards program
- Filter for “None” to find disengaged users for re-engagement campaigns
Content Quality Level Filter
Content Quality Level Filter
- Excellent (✅): Top-quality creators
- Good (👍): Above-average contributors
- Fair (😐): Acceptable content
- Weak (👎): Below-average quality
- Broken (🚫): Policy violations
- No Posts (📝): Non-posting users
- Filter for “Excellent” to identify content for featured sections
- Filter for “Broken” to review users for potential bans
Combined Filters (AND Logic)
Combined Filters (AND Logic)
- High Contribution + Excellent Quality: Your most valuable members
- Very High Contribution + Weak Quality: High-volume, low-value posters (potential spam)
- Low Contribution + Good Quality: Rising stars with potential
- None Contribution + No Posts: Completely inactive users
Filter Limitations
Filter Limitations
Empty States
Empty States
Strategic Use Cases
- Recognition Programs
- Moderation Priority
- Rising Star Detection
- Re-engagement Campaign
- Spam Detection
- Sort by Contribution Level (Highest First)
- Filter for Content Quality = “Excellent” or “Good”
- Review top 10-50 users
- Award badges, privileges, or recognition
Best Practices
Regular Review Cadence
Regular Review Cadence
- Weekly: Check for new “Broken” quality users requiring immediate action
- Bi-weekly: Identify top contributors for recognition
- Monthly: Analyze trends (are quality scores improving? Is contribution concentrated?)
- Quarterly: Review overall community health and adjust strategies
Combine with Other Data
Combine with Other Data
- User History: See the specific content behind the scores
- Analytics Dashboard: Track trends over time
- Moderation Feed: Cross-reference with flagged content
- Community Engagement Metrics: Understand context of scores
Act on Insights
Act on Insights
- Reward: Create recognition programs for top performers
- Moderate: Address low-quality content creators promptly
- Engage: Reach out to promising mid-tier contributors
- Support: Provide resources to help users improve
- Adjust: If scores don’t match expectations, review community guidelines or score weights
Consider Context
Consider Context
- New communities may have many “Low” contributors still finding their voice
- Niche communities may naturally have lower overall activity
- Seasonal trends may affect contribution levels
- Content quality standards vary by community type
Transparency with Users
Transparency with Users
- Include scoring criteria in community guidelines
- Explain what behaviors earn points
- Set clear content quality standards
- Show users their own scores (if your app supports it)
Metrics to Monitor
Track these key indicators to understand and optimize your community health:| Metric | What It Tells You | Action Trigger |
|---|---|---|
| Distribution of Contribution Levels | Engagement health across tiers | High concentration in “None” → improve onboarding |
| % Users with “Excellent” Quality | Content quality baseline | Low % → review guidelines, improve education |
| Very High + Broken Users | Potential spam or abuse | Any occurrence → immediate investigation |
| Low/None + Excellent Users | Untapped potential | Rising count → nurture these emerging stars |
| Trend: Contribution Levels | Growing or shrinking engagement | Declining high levels → investigate causes |
| Trend: Quality Levels | Improving or degrading content | Degrading quality → strengthen moderation |
| Top 10% Contributor Concentration | Community dependence risk | Too concentrated → diversify engagement |
| Broken Quality Rate | Moderation effectiveness | Rising rate → review moderation processes |
Troubleshooting
Scores seem inaccurate
Scores seem inaccurate
- Score reflects last 30 days only (not historical activity)
- Deleted content excluded from calculation
- Recent activity hasn’t updated yet (allow time for sync)
- Open User History to verify specific posts/comments
- Check date range of activity
- Confirm no recent bulk deletions
- Allow 5-10 minutes for score recalculation after new activity
Label not displaying
Label not displaying
- Score calculation error (backend issue)
- User data incomplete or corrupted
- Permission issue
- Refresh the page
- Check console for error details (if technical admin)
- Verify user account status (active, not deleted)
- Contact support if error persists across multiple users
Tooltip not appearing
Tooltip not appearing
- Browser caching issue
- Tooltip data not loaded
- Refresh page (clear cache if needed)
- Try different browser
- Verify internet connection
- If persistent, report as UI bug
Filters not working
Filters not working
- Conflict with search or status filters
- No users match criteria (empty state)
- Filter sync delay
- Clear any search queries or status filters first
- Verify filter selection is correct (check dropdown)
- Try single filter before combining
- Refresh page if results seem stale
Sorting not functioning
Sorting not functioning
- Active filters limiting result set
- Secondary sort (by date) creating confusion
- Cached sort state
- Note that users with same score sort by date
- Clear filters and try sorting again
- Refresh page to reset sort state
- Verify pagination—sorting applies across all pages
Can't combine filters as expected
Can't combine filters as expected
- Apply contribution + quality filters to narrow list
- Manually scan for flagged users (flagged indicator still visible)
- OR: Filter for flagged users first, then check scores in User History individually