Time-aware Analysis and Ranking of Lurkers in Social Networks

TL;DR

This paper investigates the temporal behavior of lurkers in social networks and enhances lurker ranking methods by incorporating time-aware properties, providing deeper insights into lurking dynamics through analysis of real-world social media data.

Contribution

It introduces a time-aware approach to analyze and rank lurkers, advancing beyond static network analysis to include temporal information about user actions.

Findings

Temporal analysis reveals distinct lurking behaviors over time.

Time-aware ranking improves identification of active versus passive lurkers.

Insights into lurking dynamics across different social media platforms.

Abstract

Mining the silent members of an online community, also called lurkers, has been recognized as an important problem that accompanies the extensive use of online social networks (OSNs). Existing solutions to the ranking of lurkers can aid understanding the lurking behaviors in an OSN. However, they are limited to use only structural properties of the static network graph, thus ignoring any relevant information concerning the time dimension. Our goal in this work is to push forward research in lurker mining in a twofold manner: (i) to provide an in-depth analysis of temporal aspects that aims to unveil the behavior of lurkers and their relations with other users, and (ii) to enhance existing methods for ranking lurkers by integrating different time-aware properties concerning information-production and information-consumption actions. Network analysis and ranking evaluation performed on Flickr, FriendFeed and Instagram networks allowed us to draw interesting remarks on both the understanding of lurking dynamics and on transient and cumulative scenarios of time-aware ranking.