All that Glitters is not Gold: Modeling Relational Events with Measurement Errors

As relational event models are an increasingly popular model for studying relational structures, the reliability of large-scale event data collection becomes increasingly important. Automated or human-coded events often suffer from relatively low sensitivity in event identification. At the same time, most sensor data is primarily based on actors' spatial proximity for predefined time windows; hence, the observed events could relate either to a social relationship or random co-location. Both examples lead to false positives in the observed events that may bias the estimates and inference. We propose an Error-corrected Relational Event Model (EcREM) as an extension to existing approaches for interaction data. The model provides a flexible solution for modeling data while controlling for false positives. Estimation of our model is carried out in an empirical Bayesian approach via data augmentation. In a simulation study, we investigate the properties of the estimation procedure. Consecutively, we apply this model to combat events from the Syrian civil war and to student co-location data. Results from both the simulation and the application identify the EcREM as a suitable approach to modeling relational event data in the presence of measurement error.