An Evaluation of Learning-to-Rank Methods for Lurking Behavior Analysis

Computational approaches to lurking behavior analysis in online social networks (OSNs) have been developed in the last few years. However, the complexity of the problem hints at the opportunity of learning from past lurking experiences as well as of using a variety of behavioral features, including any available information on the activity and interaction of lurkers and active users in an OSN. In this paper, we leverage this opportunity in a principled way, by proposing a machine-learning framework which, once trained on lurking/non-lurking examples from multiple OSNs, allows us to predict the ranking of unseen lurking behaviors, ultimately enabling the prioritization of user engagement tasks.