This work leverages a unique database that identifies written causal statements between economic indicators and events with daily precision, from a total of 128 million documents processed since 2014, covering over 40,000 firms across 187 exchanges worldwide. This database offers a detailed view of causal structures within written media content, allowing a detailed analysis of its impact on the world’s economy. Our study models these interactions as an evolving dynamic network, represented by daily weighted directed graphs. We use and adapt the preferential attachment (PA) model to estimate the probability that new nodes connect to existing ones. PA is an appealing edge-generating mechanism for capturing the growth dynamics of evolving networks and for modelling the tail-heaviness of the distributions of node degrees and strengths. Model parameters are estimated using both parametric and nonparametric methods, which are then used to efficiently generate future day-ahead weighted and directed PA networks. The predictive accuracy of the proposed approach is assessed by examining how well the generated networks reproduce the daily characteristics and empirical linking probabilities observed in the data. This methodology enhances our understanding of media’s influence on the global economy and evaluates the model’s suitability for complex dynamic network analysis.