Lurking in Social Networks: Topology-based Analysis and Ranking Methods

TL;DR

This paper introduces a novel topology-based method for ranking lurkers in social networks, focusing solely on network structure to identify silent members effectively.

Contribution

It presents the first centrality-based approach specifically designed for lurker ranking, utilizing only network topology without content analysis.

Findings

The proposed lurker ranking method outperforms traditional centrality measures.

Empirical tests on Twitter, Flickr, and other platforms validate its effectiveness.

The approach uniquely identifies silent, lurking users in social networks.

Abstract

The massive presence of silent members in online communities, the so-called lurkers, has long attracted the attention of researchers in social science, cognitive psychology, and computer-human interaction. However, the study of lurking phenomena represents an unexplored opportunity of research in data mining, information retrieval and related fields. In this paper, we take a first step towards the formal specification and analysis of lurking in social networks. We address the new problem of lurker ranking and propose the first centrality methods specifically conceived for ranking lurkers in social networks. Our approach utilizes only the network topology without probing into text contents or user relationships related to media. Using Twitter, Flickr, FriendFeed and GooglePlus as cases in point, our methods' performance was evaluated against data-driven rankings as well as existing centrality methods, including the classic PageRank and alpha-centrality. Empirical evidence has shown the significance of our lurker ranking approach, and its uniqueness in effectively identifying and ranking lurkers in an online social network.