The DEvOTION Algorithm for Delurking in Social Networks

Lurkers are silent members of a social network (SN) who gain benefit from others’ information without significantly giving back to the community. The study of lurking behaviors in SNs is nonetheless important, since these users acquire knowledge from the community, and as such they can be social capital holders. Within this view, a major goal is to delurk such users, i.e., to encourage them to more actively be involved in the SN. Despite the main strategies have been conceptualized in social science and human–computer interaction, no computational approach has been so far defined to turn lurkers into active participants in the SN. In this work we fill this gap by presenting a delurking-oriented targeted influence maximization problem under the linear threshold (LT) model. We define a novel objective function, in terms of the lurking scores associated with the nodes in the final active set, and we show it is monotone and submodular. We provide an approximate solution by developing a greedy algorithm, named DEvOTION, which computes a k-node set that maximizes the value of the delurking-capital-based objective function, for a given lurking threshold. Results on SN datasets of different sizes have demonstrated the significance of our delurking approach via LT-based targeted influence maximization. A comparative evaluation with state-of-the-art algorithms for non-targeted and targeted LT-based influence maximization has also shown the superiority of DEvOTION in terms of delurking capital that is obtained.