User Growth Validation

Generated: October 03, 2025 at 08:39 PM

Chapter 1 Methodological Validation

User Growth vs Content Growth Validation Analysis

Generated: 2025-10-03 20:39:57

Executive Summary

This analysis distinguishes between real crisis intensification versus platform adoption/disclosure shift by comparing user growth rates against content growth rates across CUNY subreddits.

Key Metrics Explained:

  • User Growth Ratio: How many more unique users participated post-2020 vs pre-2020
  • Content Growth Ratio: How much more content was created post-2020 vs pre-2020
  • Posts Per User Ratio: How much individual posting behavior changed

Interpretation Framework:

  • If user growth ≈ content growth: Platform adoption shift (more people discussing same issues)
  • If content growth » user growth: Real crisis intensification (same people experiencing more/worse crises)
  • If posts/user increased significantly: Individual crisis intensity increased

Overall Growth Analysis (All CUNY Subreddits)

By Subreddit

Subreddit Pre-2020 Users Post-2020 Users User Growth Pre-2020 Posts Post-2020 Posts Content Growth Posts/User Change
Baruch 997 7,866 7.9x 11,061 109,976 9.9x 1.3x
BrooklynCollege 71 358 5.0x 194 1,084 5.6x 1.1x
CCNY 109 1,367 12.5x 371 10,015 27.0x 2.2x
CUNY 206 8,966 43.5x 1,052 93,107 88.5x 2.0x
HunterCollege 288 2,995 10.4x 1,102 26,553 24.1x 2.3x
JohnJay 14 74 5.3x 17 186 10.9x 2.1x
QueensCollege 522 2,644 5.1x 4,185 24,442 5.8x 1.2x

Crisis Topic Analysis

Financial Aid

Subreddit User Growth Content Growth Posts/User Change Interpretation
Baruch 9.7x 10.8x 1.1x Platform adoption
BrooklynCollege 7.8x 4.9x 0.6x Mixed pattern
CCNY 17.2x 18.6x 1.1x Platform adoption
CUNY 51.4x 71.1x 1.4x Mixed pattern
HunterCollege 15.7x 25.3x 1.6x Mixed pattern
QueensCollege 8.1x 9.3x 1.1x Platform adoption

Food Insecurity

| Subreddit | User Growth | Content Growth | Posts/User Change | Interpretation | |———–|————-|—————-|——————-|—————-|

Housing

| Subreddit | User Growth | Content Growth | Posts/User Change | Interpretation | |———–|————-|—————-|——————-|—————-|

Mental Health

Subreddit User Growth Content Growth Posts/User Change Interpretation
Baruch 10.3x 12.4x 1.2x Mixed pattern
CCNY 33.0x 37.7x 1.1x Mixed pattern
CUNY 71.9x 94.2x 1.3x Mixed pattern
HunterCollege 22.8x 24.4x 1.1x Platform adoption
JohnJay 1.0x 4.0x 4.0x Crisis intensification
QueensCollege 4.3x 4.6x 1.1x Platform adoption

Cunyfirst

| Subreddit | User Growth | Content Growth | Posts/User Change | Interpretation | |———–|————-|—————-|——————-|—————-|

Key Findings

  1. Average User Growth: 12.8x
  2. Average Content Growth: 24.6x
  3. Average Posts/User Change: 1.7x

VALIDATION RESULT: Evidence supports REAL CRISIS INTENSIFICATION

The disproportionate content growth compared to user growth indicates that existing community members were experiencing and discussing crises at significantly higher rates post-2020, rather than simply more users joining the platform.

Methodological Notes

  • Analysis excludes deleted users to focus on identifiable community members
  • CUNYuncensored may show anomalous patterns as it was created during the pandemic
  • Keyword matching uses case-insensitive partial string matching
  • Pre-2020 period: All data before January 1, 2020
  • Post-2020 period: January 1, 2020 onwards

SQL Queries for Reproducibility

-- Overall User and Content Growth
SELECT
  CASE WHEN created_utc < 1577836800 THEN 'pre-2020'
       ELSE 'post-2020' END as period,
  COUNT(DISTINCT author) as unique_active_users,
  COUNT(*) as total_posts,
  CAST(COUNT(*) AS REAL) / COUNT(DISTINCT author) as posts_per_user
FROM (
  SELECT author, created_utc FROM submissions
  WHERE author != '[deleted]' AND author IS NOT NULL
  UNION ALL
  SELECT author, created_utc FROM comments
  WHERE author != '[deleted]' AND author IS NOT NULL
)
GROUP BY period;

-- Crisis Topic Analysis (example for financial aid)
SELECT
  CASE WHEN created_utc < 1577836800 THEN 'pre-2020'
       ELSE 'post-2020' END as period,
  COUNT(DISTINCT author) as unique_users_discussing_topic,
  COUNT(*) as total_topic_posts,
  CAST(COUNT(*) AS REAL) / COUNT(DISTINCT author) as topic_posts_per_user
FROM (
  SELECT author, created_utc FROM submissions
  WHERE (title LIKE '%financial aid%' OR selftext LIKE '%financial aid%' OR
         title LIKE '%TAP%' OR selftext LIKE '%TAP%' OR
         title LIKE '%FAFSA%' OR selftext LIKE '%FAFSA%')
    AND author != '[deleted]' AND author IS NOT NULL
  UNION ALL
  SELECT author, created_utc FROM comments
  WHERE (body LIKE '%financial aid%' OR body LIKE '%TAP%' OR body LIKE '%FAFSA%')
    AND author != '[deleted]' AND author IS NOT NULL
)
GROUP BY period;

Data Files

Download JSON Data