Being Rational or Aggressive? A Revisit to Dunbar's Number in Online Social Networks

TL;DR

This paper revisits Dunbar's number in online social networks, validating its existence around 200-300 users, and classifies user behaviors into rational and aggressive, with implications for marketing and privacy.

Contribution

It systematically validates the online Dunbar's number, classifies user behaviors, and models social network evolution considering cognitive and time constraints.

Findings

Existence of online Dunbar's number in [200,300] range.

Rational users develop close, reciprocated relationships.

Aggressive users show inconsistent behaviors.

Abstract

Recent years have witnessed the explosion of online social networks (OSNs). They provide powerful IT-innovations for online social activities such as organizing contacts, publishing contents, and sharing interests between friends who may never meet before. As more and more people become the active users of online social networks, one may ponder questions such as: (1) Do OSNs indeed improve our sociability? (2) To what extent can we expand our offline social spectrum in OSNs? (3) Can we identify some interesting user behaviors in OSNs? Our work in this paper just aims to answer these interesting questions. To this end, we pay a revisit to the well-known Dunbar's number in online social networks. Our main research contributions are as follows. First, to our best knowledge, our work is the first one that systematically validates the existence of the online Dunbar's number in the range of [200,300]. To reach this, we combine using local-structure analysis and user-interaction analysis for extensive real-world OSNs. Second, we divide OSNs users into two categories: rational and aggressive, and find that rational users intend to develop close and reciprocated relationships, whereas aggressive users have no consistent behaviors. Third, we build a simple model to capture the constraints of time and cognition that affect the evolution of online social networks. Finally, we show the potential use of our findings in viral marketing and privacy management in online social networks.