All that Glitters is not Gold: Relational Events Models with Spurious Events

TL;DR

This paper introduces REMSE, a new relational event model that accounts for spurious events caused by false discoveries or random co-locations, improving the reliability of relational data analysis.

Contribution

The paper develops REMSE, an extension to relational event models that explicitly models and controls for spurious events using an empirical Bayesian estimation approach.

Findings

REMSE effectively reduces bias from spurious events in simulated data.

Application to Syrian civil war events shows improved inference accuracy.

Application to student co-location data demonstrates model's practical utility.

Abstract

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.