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Methodology

How the R-Word Index is built, scored, and published.

What the Index Measures

The R-Word Index is a normalized daily score from 0 to 100 representing the relative level of recession-related concern in everyday online conversation and search behavior. It answers one question: how much are real people talking and searching about recession right now, and how worried do they sound?
It is not a GDP forecast, not a market signal, and not an official economic indicator. It is a recession pulse index — a daily measure of how frequently and intensely recession appears in human-generated online activity.

Data Sources

The index is built from multiple sources of everyday human online activity — community discussions, personal accounts, and search behavior. The emphasis is on authentic human expression rather than official news outlets or institutional commentary.
Community Discussions
Posts and comments from online communities covering economics, jobs, personal finance, housing, investing, small business, and general life discussion. These capture how ordinary people describe their own experience with economic stress, job loss, rising prices, and uncertainty.
Search Trends
Aggregated search interest for recession-related terms. When more people search for "recession," "layoffs," or "unemployment," it reflects a rising awareness and concern in the broader population — even among those who don't participate in online discussions.
Tech Community
Posts from tech-focused communities covering startups, hiring, and industry sentiment. The tech sector is often an early indicator of broader economic shifts — layoff announcements, hiring freezes, and funding slowdowns tend to appear here before mainstream discussion.

Scoring Pipeline

Each piece of content passes through a two-stage scoring pipeline:
1. Relevance Classification
The system checks whether the text mentions recession or closely related economic distress terms — things like "recession," "economic downturn," "layoffs," "unemployment," "inflation," "housing crisis," and similar. Content that doesn't mention these topics is filtered out. Historical references and metaphorical uses are penalized to reduce noise. Some sources (like search trend data) arrive pre-scored since they are inherently about recession search behavior.
2. Concern Intensity Scoring
Relevant content receives a concern intensity score based on three weighted signals:
3. Document Score
doc_score = relevance_prob × concern_intensity
Where:

Daily Aggregation

For each UTC day, the system:
  1. Collects all scored content from that day across all enabled sources
  2. Filters to documents with relevance above the minimum threshold
  3. Computes per-source scores using top-20% averaging — only the highest-scoring 20% of documents per source contribute, reducing noise from low-quality matches
  4. Combines source scores using configured weights — community discussions (40%), search trends (35%), and tech community (25%)
  5. Applies a volume signal — if today's document count is significantly above or below the 30-day average, the score is adjusted accordingly (±15%)
  6. Normalizes to a 0–100 scale using percentile rank against a rolling 365-day baseline (with min-max scaling during the cold-start period)
  7. Applies dynamic EMA smoothing — normally 30% today + 70% yesterday, but more responsive (60/40) during the cold-start period, and further boosted during sudden spikes to avoid underreporting rapid changes

State Labels

The daily index maps to five human-readable states:
0–19: Quiet20–39: Low Concern40–59: Elevated60–79: High Concern80–100: Alarmed

Update Frequency

The public index is published once per day. Collection and scoring run in the background, but the public value updates daily for consistency and clarity.

Limitations

The R-Word Index is an experimental public signal. It is designed to be transparent, restrained, and honest about what it can and cannot measure.