  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.
