  As relational event models are an increasingly popular model for studying
relational structures, the reliability of large-scale event data collection
becomes more and more important. Automated or human-coded events often suffer
from non-negligible false-discovery rates in event identification. And 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 imply spurious events that
may bias estimates and inference. We propose the Relational Event Model for
Spurious Events (REMSE), an extension to existing approaches for interaction
data. The model provides a flexible solution for modeling data while
controlling for spurious events. Estimation of our model is carried out in an
empirical Bayesian approach via data augmentation. Based on a simulation study,
we investigate the properties of the estimation procedure. To demonstrate its
usefulness in two distinct applications, we employ this model to combat events
from the Syrian civil war and student co-location data. Results from the
simulation and the applications identify the REMSE as a suitable approach to
modeling relational event data in the presence of spurious events.
