Mining Lurkers in Online Social Networks

This SpringerBrief brings order to the wealth of research studies thatcontribute to shape our understanding of on-line social networks(OSNs) lurking phenomena. This brief also drives the development ofcomputational approaches that can be effectively applied to answerquestions related to lurking behaviors, as well as to the engagement oflurkers in OSNs. All large-scale online social networks (OSNs) are characterized by aparticipation inequality principle, i.e., the crowd of an OSN does notactively contribute, rather it takes on a silent role. Silent users are alsoreferred to as lurkers, since they gain benefit from others' informationwithout significantly giving back to the community. Nevertheless, lurkersacquire knowledge from the OSN, therefore a major goal is to encouragethem to more actively participate. Lurking behavior analysis has been long studied in social science andhuman-computer interaction fields, but it has also matured over the lastfew years in social network analysis and mining. While the main target audience corresponds to computer, network, andweb data scientists, this brief might also help increase the visibility of thetopic by bridging different closely related research fields. Practitioners, researchersand students interested in social networks, web search, data mining,computational social science and human-computerinteraction will also find this brief useful research material .