{"title":"Causal evidence for social group sizes from Wikipedia editing data.","authors":"M Burgess, R I M Dunbar","doi":"10.1098/rsos.240514","DOIUrl":null,"url":null,"abstract":"<p><p>Human communities have self-organizing properties in which specific Dunbar Numbers may be invoked to explain group attachments. By analysing Wikipedia editing histories across a wide range of subject pages, we show that there is an emergent coherence in the size of transient groups formed to edit the content of subject texts, with two peaks averaging at around <math><mrow><mi>N</mi> <mo>=</mo> <mn>8</mn></mrow> </math> for the size corresponding to maximal contention, and at around <math><mrow><mi>N</mi> <mo>=</mo> <mn>4</mn></mrow> </math> as a regular team. These values are consistent with the observed sizes of conversational groups, as well as the hierarchical structuring of Dunbar graphs. We use a model of bipartite trust to derive a scaling law that fits the data and may apply to all group size distributions when these are based on attraction to a seeded group process. In addition to providing further evidence that even spontaneous communities of strangers are self-organizing, the results have important implications for the governance of the Wikipedia commons and for the security of all online social platforms and associations.</p>","PeriodicalId":21525,"journal":{"name":"Royal Society Open Science","volume":"11 10","pages":"240514"},"PeriodicalIF":2.9000,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11444758/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Royal Society Open Science","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1098/rsos.240514","RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Human communities have self-organizing properties in which specific Dunbar Numbers may be invoked to explain group attachments. By analysing Wikipedia editing histories across a wide range of subject pages, we show that there is an emergent coherence in the size of transient groups formed to edit the content of subject texts, with two peaks averaging at around for the size corresponding to maximal contention, and at around as a regular team. These values are consistent with the observed sizes of conversational groups, as well as the hierarchical structuring of Dunbar graphs. We use a model of bipartite trust to derive a scaling law that fits the data and may apply to all group size distributions when these are based on attraction to a seeded group process. In addition to providing further evidence that even spontaneous communities of strangers are self-organizing, the results have important implications for the governance of the Wikipedia commons and for the security of all online social platforms and associations.
人类社区具有自组织特性,在这种特性中,特定的邓巴数(Dunbar Numbers)可被用来解释群体依附关系。通过分析维基百科上各种主题页面的编辑历史,我们发现,为编辑主题文本内容而形成的瞬时群体的规模存在着明显的一致性,其中有两个峰值,平均规模约为 N = 8(与最大争论相对应)和 N = 4(作为常规团队)。这些数值与观察到的会话组规模以及邓巴图的层次结构一致。我们使用一个双方信任模型推导出了一个符合数据的缩放定律,当这些数据基于对种子群进程的吸引力时,该定律可能适用于所有群的规模分布。除了进一步证明即使是陌生人自发组成的社区也是自组织的,这些结果对维基百科公共资源的管理以及所有在线社交平台和协会的安全性都有重要意义。
期刊介绍:
Royal Society Open Science is a new open journal publishing high-quality original research across the entire range of science on the basis of objective peer-review.
The journal covers the entire range of science and mathematics and will allow the Society to publish all the high-quality work it receives without the usual restrictions on scope, length or impact.