↓ Skip to main content

PLOS

Collective Phenomena and Non-Finite State Computation in a Human Social System

Overview of attention for article published in PLOS ONE, October 2013
Altmetric Badge

Mentioned by

news
1 news outlet
twitter
36 X users
facebook
3 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
36 Mendeley
citeulike
1 CiteULike
Title
Collective Phenomena and Non-Finite State Computation in a Human Social System
Published in
PLOS ONE, October 2013
DOI 10.1371/journal.pone.0075818
Pubmed ID
Authors

Simon DeDeo

Abstract

We investigate the computational structure of a paradigmatic example of distributed social interaction: that of the open-source Wikipedia community. We examine the statistical properties of its cooperative behavior, and perform model selection to determine whether this aspect of the system can be described by a finite-state process, or whether reference to an effectively unbounded resource allows for a more parsimonious description. We find strong evidence, in a majority of the most-edited pages, in favor of a collective-state model, where the probability of a "revert" action declines as the square root of the number of non-revert actions seen since the last revert. We provide evidence that the emergence of this social counter is driven by collective interaction effects, rather than properties of individual users.

X Demographics

X Demographics

The data shown below were collected from the profiles of 36 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 11%
Portugal 2 6%
France 1 3%
China 1 3%
Sri Lanka 1 3%
Spain 1 3%
Luxembourg 1 3%
Unknown 25 69%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 31%
Researcher 8 22%
Student > Master 6 17%
Professor 4 11%
Student > Doctoral Student 2 6%
Other 3 8%
Unknown 2 6%
Readers by discipline Count As %
Computer Science 9 25%
Physics and Astronomy 5 14%
Agricultural and Biological Sciences 3 8%
Social Sciences 3 8%
Psychology 3 8%
Other 10 28%
Unknown 3 8%