Abstract

The affiliation with various social groups can be a critical factor when it comes to quality of life of each individual, making such groups an essential element of every society. The group dynamics, longevity and effectiveness strongly depend on group’s ability to attract new members and keep them engaged in group activities. It was shown that high heterogeneity of scientist’s engagement in conference activities of the specific scientific community depends on the balance between the numbers of previous attendances and non-attendances and is directly related to scientist’s association with that community. Here we show that the same holds for leisure groups of the Meetup website and further quantify individual members’ association with the group. We examine how structure of personal social networks is evolving with the event attendance. Our results show that member’s increasing engagement in the group activities is primarily associated with the strengthening of already existing ties and increase in the bonding social capital. We also show that Meetup social networks mostly grow trough big events, while small events contribute to the groups cohesiveness.

Highlights

  • One of the consequences of the rapid development of the Internet and growing presence of information communication technologies is that a large part of an individual’s daily activities, both off and online, is regularly recorded and stored

  • We argued in [20] that the forces behind conference participation dynamics are of social origin, and it follows from Fig 1 that the same can be argued for the case of the Meetup group participation dynamics

  • In this article we explore the event participation dynamics and underlying social mechanism of the Meetup groups

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Summary

Introduction

One of the consequences of the rapid development of the Internet and growing presence of information communication technologies is that a large part of an individual’s daily activities, both off and online, is regularly recorded and stored. This newly available data granted us a substantial insight into activities of a large number of individuals during long period of time and led to the development of new methods and tools, which enable better understanding of the dynamics of social groups [1]. It is not surprising that the theory of complex networks has proven to be very successful in uncovering mechanisms governing the behavior of individuals and social groups [7, 8].

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