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The Structure and Productivity of Web-based Groups: Testing the Berners-Lee Hypothesis. Andrea Baltassarri, Alain Barrat, Andrea Cappocci, Harry Halpin Ulrike Lehner, J.J. Ramasco, Valentin Robu, Dario Taraborelli.

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the structure and productivity of web based groups testing the berners lee hypothesis
The Structure and Productivity of Web-based Groups: Testing the Berners-Lee Hypothesis

Andrea Baltassarri, Alain Barrat, Andrea Cappocci, Harry Halpin Ulrike Lehner, J.J. Ramasco, Valentin Robu, Dario Taraborelli

Tim Berners-Lee: It seems from experience that groups are stable when they have a set of peers, when they have a substructure'' “Neither the set of peers nor the substructure must involve huge numbers” “Groups cannot `scale', i.e. work effectively with a large number of peers, or when composed as of a large number of parts.'‘Compromise between stability and diversity is served by the same amount of structure at all scales,'' in other words, a ``fractal distribution” Hypothesis: a user that belongs to groups whose membership follows a power-law distribution will have a higher-amount of activity than a user that belonging to groups whose membership does not follow a power-law distribution"

dunbar number
Dunbar “number”

Hypothesized cognitive upper limit to the number of individuals one can form a social relationship with at a given time. One of a series of `circles of intimacy' as hypothesized by Dunbar to apply to human social relationships, where he hypothesized that the number and quality of relationships people have follow a vaguely exponential curve, where one in general has 5 intimate friends, followed by 12-15 members in a sympathy group, followed by 150 friends one can maintain, followed by 1500 acquaintances.Originally extrapolated by Dunbar from the size of the human neo-cortexA study of Christmas Cards distribution found a mean network size of 153.5 with the large deviation of 84.5 - (Hill and Dunbar 2003)

towards a more testable hypothesis
Towards a more testable hypothesis

A system displaying a power-law distribution of members per online community is (1) demographically more stable over time than a system with a uniform distribution of members per community.(2) likely to have a power-law distribution of user activity within of each of its communities(3) likely to produce a higher overall amount of activity than a system with any other member distribution