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

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.

arXiv comment24 pages, 10 figures, 16 tables

References in corpus (5)

• Community structure in social and biological networks
• Defining and identifying communities in networks
• Analysis of a large-scale weighted network of one-to-one human communication
• Nepotistic Relationships in Twitter and their Impact on Rank Prestige Algorithms
• Analyzing User Behavior across Social Sharing Environments

Cited by in corpus (7)

• Information Consumption and Boundary Spanning in Decentralized Online Social Networks: the case of Mastodon Users
• Time-aware Analysis and Ranking of Lurkers in Social Networks
• Topology-driven Diversity for Targeted Influence Maximization with Application to User Engagement in Social Networks
• Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques
• Centralities for Networks with Consumable Resources
• Adult content in Social Live Streaming Services: Characterizing deviant users and relationships
• Modeling Evolutionary Dynamics of Lurking in Social Networks