Lurker Dynamics in Digital Networks: Quantifying Silent Influence and Predictive Re-engagement

This study introduces a novel framework that quantifies the often-ignored impact of digital networks’ passive users, or lurkers. Unlike prior research focused on active users, we present three new metrics: (1) Shadow Centrality, measuring a lurker’s indirect influence on future points of interest (POI) engagement; (2) Dormancy-Aware Recall@k, assessing recommendation systems by considering lurkers’ delayed re-engagement; and (3) Geotemporal Consistency, examining how lurking patterns affect network activity stability. Using the Yelp dataset, which includes user reviews and spatiotemporal data, we develop a Lurker-Aware Graph Neural Network (LA-GNN) that models the passive influence of lurkers and their re-engagement triggers through survival analysis. Our results show that lurkers contribute to 22% of hidden POI popularity signals overlooked by traditional metrics. This research redefines lurker analysis with behaviour-based metrics, highlighting their significant impact on platforms like Yelp.