An effect of background population sample size on the performance of a likelihood ratio-based forensic text comparison system: A Monte Carlo simulation with Gaussian mixture model

This is a Monte Carlo simulation-based study that explores the effect of the sample size of the background database on a likelihood ratio (LR)-based forensic text comparison (FTC) system built on multivariate authorship attribution features. The text messages written by 240 authors who were randomly selected from an archive of chatlog messages were used in this study. The strength of evidence (= LR) was estimated using the multivariate kernel density likelihood ratio (MVKD) formula with a logistic-regression calibration. The results are reported along two points: the system performance (= accuracy) and the stability of performance based on the standard metric for LR-based systems; namely the log-likelihood ratio cost (Cllr). It was found in this study that the system performance and its stability improve as a function of the sample size (= author count) in the background database in a non-linear manner, and that the more features used for modelling, the more background data the system generally requires for optimal results. The implications of the findings to the real casework are also discussed.