Abstract
In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a ‘community’ as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of ‘community.’ In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure.
Highlights
Networks play a central role in online social media services like Twitter, Facebook, and Google+
We begin by showing that the communities emerging from the different weightings of the structural network quantitatively differ both at the macroscopic and microscopic scale in interesting ways
One advantage of OSLOM over many other community detection algorithms is that it explicitly accounts for singleton ‘communities’: those nodes who do not belong to any extant communities
Summary
Networks play a central role in online social media services like Twitter, Facebook, and Google+. These services allow a user to interact with others based on the online social network they curate through a process known as contact filtering [1]. ‘friends’ on Facebook represent reciprocal links for sharing information, while ‘followers’ on Twitter allow a single user to broadcast information in a one-to-many fashion. Central to all these interactions is the fact that the structure of the social network influences how information can be broadcast or diffuse through the service.
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