Content Production

In national economics, GDP (Gross Domestic Product) measures the total value of goods and services produced. On Wikipedia, we borrow that metaphor: output is the content produced by editors, measured in bytes and edits. Gross output counts every byte added; net output subtracts deletions and reverts, showing what actually remains in the encyclopedia. Together these metrics reveal how productive the wiki economy is -- and how much of its labor goes to churn rather than lasting growth.

Gross Output

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Net Output

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Total Edits

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Revert Rate

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Gross vs Net Output

Blue (faint) = gross bytes added. Green = net bytes (after deletions). Toggle "Break down by user type" to see per-type output.

Methodology

Gross Output = Σ positive byte diffs (content added) Net Output = Σ all byte diffs = Bytes Added − Bytes Deleted Content Churn = Gross − Net

Gross output is the sum of positive byte diffs only -- content added. Net output is the sum of all byte diffs (additions minus deletions). The gap between the two represents content churn: edits that were later removed or reverted. A growing gap suggests increasing maintenance overhead; a narrow gap means most new content sticks.

Revert Rate

Methodology

Revert Rate = Reverted Edits / Total Edits × 100%

Revert rate is the fraction of edits that were identity-reverted -- i.e., the same content was restored by a subsequent edit. High revert rates may indicate edit wars or vandalism-and-cleanup cycles. A sustained high rate for a particular user type can signal systemic issues with content quality from that group.

Productivity: Net Bytes Per Edit

Productivity can be negative when more content is deleted than added in a period. This is common during cleanup campaigns or policy changes that trigger mass removals.

Methodology

Productivity = Net Bytes / Total Edits (bytes per edit)

Productivity is computed as net bytes divided by total edits. It measures the average content contribution per edit. Positive values mean the wiki is growing; negative values mean more content is being removed than added. This metric is sensitive to outliers -- a single large page creation or deletion can swing it dramatically.

Activity Tiers

Editors are bucketed by their edit count in the selected calendar period, using monthly-rate-equivalent thresholds: 100+ per month, 300+ per quarter, or 1200+ per year. Filtered by selected user types and time range (namespace filter does not apply -- tiers are computed across all namespaces).

Stacked area shows what fraction of the selected metric comes from each activity tier. Use the metric radio to switch between editors, edits, and bytes.

Methodology

Tier(editor, period) = bucket(period_edit_count, period_months × [1, 5, 25, 100])

The five boundaries scale with the selected period's fixed calendar length. Monthly tiers are 1, 2–4, 5–24, 25–99, and 100+ edits; quarterly tiers are 1–3, 4–14, 15–74, 75–299, and 300+; annual tiers are 1–12, 13–59, 60–299, 300–1199, and 1200+. Each editor is classified once per selected period, so changing granularity both changes the threshold and deduplicates that editor across its constituent months.

All user types are shown regardless of the filter selection above, so you can always see the full composition of the editing population.

Methodology

Share(type) = Edits by type / Total Edits × 100%

This stacked area shows what fraction of total edits comes from each user type over time. All user types are displayed regardless of the filter so the full composition is always visible. The shift from anonymous to registered editing over the years reflects both policy changes (account creation incentives, CAPTCHA) and the maturation of the editor community.

Sectoral Output (by Namespace)

The Article namespace (ns 0) is typically dominant, but Talk, User, and Wikipedia namespaces reveal the community maintenance work that keeps the encyclopedia running.

Methodology

Sector Edits = Σ edits WHERE page_namespace = ns

Edits are broken down by namespace (sector). The Article namespace is typically dominant, but Talk, User, and Wikipedia namespaces show community maintenance work -- discussions, user page housekeeping, and policy pages. Other namespaces like Template and Module reflect technical infrastructure. Comparing sectoral output over time can reveal shifts in where the community invests its effort.