Collective intelligence turns many individual contributions into shared knowledge, decisions, or discoveries. The statistics below show its scale across Wikimedia projects, question-and-answer communities, citizen science, development work, and academic research, with measurement periods identified where the source provides them.
Table of contents
- The scale of Wikimedia collaboration
- Wikipedia’s knowledge and reach
- Questions, answers, and community editing
- Citizen science at Zooniverse
- Collective intelligence in development and research
- Evidence about group performance
- A 2025 view of Wikimedia Norway
The scale of Wikimedia collaboration
The Wikimedia Foundation’s Annual Report 2023 describes a large, distributed system in which volunteers create, edit, organize, fund, and maintain public knowledge. Wikipedia received 5.67 edits every second, while Wikimedia projects had about 280,000 volunteer editors around the world. The same report says Wikimedia had 188 affiliates around the world and that editors and technical volunteers kept the projects operating at high levels during fiscal year 2022–2023.
The Foundation’s financial participation was also geographically broad. More than 7.5 million donors contributed in fiscal year 2022–2023, with an average gift of $11.38. People in more than 200 countries donated, and the annual report highlights more than 200 countries for donor participation. These are donor counts and participation measures, not estimates of the number of people who edit or read Wikimedia projects.
Participation is not evenly represented across every group. The 2023 annual report says 30% of Wikimedia organizers identified as women. That measure concerns organizers, so it should not be read as a demographic description of every editor, donor, reader, or affiliate.
Several other Wikimedia projects show how collective intelligence can be structured in different ways. Wikidata had more than 100 million data items, while Wikimedia Commons had 104 million freely usable media files and counting. Kiwix had more than 4 million users and was used in over 200 countries. These figures describe data, media, and access networks alongside the human communities that maintain them.
Wikipedia’s knowledge and reach
The Wikimedia Foundation Annual Report 2023 reported 62 million Wikipedia articles and counting, spread across 323 languages and counting. Wikipedia was described as one of the top 1 most-visited websites in the world and received 15 billion or more views every month. Together, these measures capture three different dimensions: the amount of published knowledge, linguistic reach, and audience scale.
Medical information is one especially large area of the encyclopedia. Wikipedia had almost 350,000 medical articles, and those articles were accessed over 6 billion times a year. The annual report also says that more than 40 countries were represented in the Reading Wikipedia in the Classroom educator cohort. That cohort statistic reflects educator participation, while the medical-article figure reflects content volume and the annual access figure reflects readership; the three measures should not be combined into one audience estimate.
The public knowledge model also depends on activity over time. A rate of 5.67 edits every second indicates continuous changes rather than a one-time publishing event. The annual report says Wikimedia projects added new knowledge throughout fiscal year 2022–2023. Because the report uses both ongoing language such as “counting” and period-specific language such as fiscal year 2022–2023, the article totals and activity statements represent different reporting frames.
| Wikimedia measure | Reported statistic | Measurement context |
|---|---|---|
| Wikipedia edits | 5.67 per second | Wikimedia Foundation Annual Report 2023 |
| Wikipedia articles | 62 million+ | “And counting,” Annual Report 2023 |
| Wikipedia languages | 323 | “And counting,” Annual Report 2023 |
| Monthly Wikipedia views | 15 billion+ | Annual Report 2023 |
| Volunteer editors | About 280,000 | Around the world, Annual Report 2023 |
| Wikidata data items | 100 million+ | Annual Report 2023 |
| Wikimedia Commons media files | 104 million+ | Freely usable files, Annual Report 2023 |
Questions, answers, and community editing
Stack Exchange provides another model of collective intelligence: people ask specific questions, contribute answers, vote, comment, and edit posts to improve them. The Stack Exchange About page says the network comprises 173 question-and-answer communities and that over 100 million people visit every month. It also reports 418.8 million monthly visits and 806.3 million monthly pageviews for the Stack Exchange network.
The network’s contribution statistics show several forms of participation. Stack Exchange had 3.1 million questions asked, 3.5 million answers submitted, 23 million upvotes, 3 million downvotes, 13.5 million comments, and 4.9 million edits to improve post quality. These are separate reported totals; they do not establish that each question received an answer or that votes, comments, and edits came from distinct people.
Stack Overflow is one community within that broader network. The Stack Exchange About page reports 51.2 million monthly unique visitors, 257.7 million monthly visits, and 11.7 million registered users for Stack Overflow. The difference between unique visitors, visits, registered users, and pageviews matters: each measures a different aspect of reach or participation.
This structure makes collective intelligence visible as a sequence of actions. A question identifies a knowledge gap, an answer proposes information, voting supplies community feedback, comments add context, and edits improve the shared record. The figures do not measure answer accuracy directly, but they quantify the activity surrounding collaborative problem-solving at a very large scale.
Citizen science at Zooniverse
Zooniverse applies distributed human judgment to research projects. Its homepage reported more than 3 million volunteers and 989,032,811 classifications so far. The homepage also said Zooniverse was approaching 1 billion classifications and that 10,420,195 classifications remained to reach a stated milestone.
These figures describe a platform with both broad participation and a very large number of individual classification actions. A volunteer count measures people associated with the platform, while a classification count measures completed contributions. They are therefore complementary rather than interchangeable. The reported remaining figure is tied to the milestone stated on the homepage and should not be treated as a general forecast of future activity.
Distributed classification is a useful example of how collective intelligence can support work that is difficult to complete through a single observer. The available statistics quantify the size of the volunteer community and the volume of classifications, but they do not provide a success rate, a classification-accuracy percentage, or a breakdown by project. No such measures are added here.
Collective intelligence in development and research
The United Nations Development Programme provides statistics about collective intelligence as an organizational practice. UNDP Accelerator Labs covered 115 countries, according to the UNDP Untapped download report page. A separate UNDP collective intelligence study reviewed over 200 global organizations, identified 15 methods used most frequently, and presented 6 key collective-intelligence use cases.
These numbers describe the breadth of the program and the structure of the study. The 115-country coverage is a geographic measure. The review of more than 200 organizations is a study scope measure. The 15 methods and 6 use cases are classification counts within the report. They should not be interpreted as the number of methods or use cases used by every organization in the review.
Academic research adds a different type of evidence. The Network Science Institute’s meta-analysis on the structure of collective intelligence used data from 22 studies, including 5,279 individuals and 1,356 groups. Its model supports a hierarchical three-factor view of collective intelligence. The sample sizes indicate the evidence base described for that analysis; they do not represent the size of a single online community or a worldwide population.
The combination of organizational and academic statistics shows why measurement context matters. A country count, an organization count, a method count, a use-case count, a study count, and a participant count answer different questions. Collective intelligence is not captured by one universal metric.
Evidence about group performance
The Harvard DASH record for “The wisdom of polarized crowds” describes an analysis of millions of edits to Wikipedia articles. The study covered Wikipedia’s Political, Social Issues, and Science articles. Its reported finding was that balanced, ideologically diverse teams produced higher-quality articles.
This result connects participation structure with output quality. The study’s unit of analysis was Wikipedia editing activity across the specified article areas, and its evidence involved millions of edits. It does not justify a claim that every diverse group performs better, nor does it provide a universal quality improvement percentage in the supplied statistics.
The Network Science Institute meta-analysis complements that finding with a broader model of group intelligence. Its evidence included 1,356 groups and 5,279 individuals across 22 studies, and the model supports a hierarchical three-factor view. The Harvard and Network Science findings use different designs and should be read as related evidence, not as one pooled estimate.
For readers comparing collective-intelligence systems, the useful distinction is between scale and performance. Wikimedia, Stack Exchange, and Zooniverse report enormous volumes of activity. The research findings ask how group composition, structure, or other factors relate to the quality and intelligence of group outputs. Activity totals alone cannot answer the performance question.
A 2025 view of Wikimedia Norway
The Wikimedia Norway Annual Report 2025 provides a more local and period-specific view across three language editions. It recorded 2,985 average monthly editors, 317 active editors, 51 administrators, 12 mentors, and 188 patrollers across the three editions in 2025.
The report also separates the editions. Norwegian Bokmål Wikipedia had 2,760 average monthly editors, 285 active editors, 35 administrators, 9 mentors, and 174 patrollers. Norwegian Nynorsk Wikipedia had 207 average monthly editors, 30 active editors, 13 administrators, 2 mentors, and 14 patrollers. Northern Sámi Wikipedia had 18 average monthly editors, 2 active editors, 3 administrators, and 1 mentor. These role counts describe different forms of participation and should not be added as if each represented a separate person.
Content production varied considerably. Norwegian Bokmål Wikipedia produced 26,549 new articles in 2025, while Norwegian Nynorsk Wikipedia produced 4,179. Northern Sámi Wikipedia recorded 8 new articles. The report’s pageview figures also distinguish traffic categories: Norwegian Bokmål recorded 1,283,129 desktop pageviews, 500,146 mobile/tablet pageviews, and 195,329 all-traffic pageviews; Norwegian Nynorsk recorded 55 desktop pageviews, 29 mobile/tablet pageviews, and 367,885 all-traffic pageviews; Northern Sámi recorded 3,064 desktop pageviews, 645 mobile/tablet pageviews, and 700,914 all-traffic pageviews.
The unusual traffic pattern is explicitly noted in the Wikimedia Norway report. Automated traffic for Northern Sámi Wikipedia spiked to 905,202 in November 2025 and 1,250,316 in December 2025. Those monthly spikes are identified as automated traffic, so they should not be interpreted as equivalent to human readership or compared with ordinary audience measures without that qualification.
The 2025 Norway figures illustrate why local reporting adds useful detail to global totals. A global count can show the scale of a platform, while language-edition measures reveal how editors, administrators, mentors, patrollers, article creation, and traffic differ within a particular geography and reporting year.