The influence of background data size on the performance of a score-based likelihood ratio system: A case of forensic text comparison

This study investigates the robustness and stability of a likelihood ratio–based (LRbased) forensic text comparison (FTC) system against the size of background population data. Focus is centred on a score-based approach for estimating authorship LRs. Each document is represented with a bagof-words model, and the Cosine distance is used as the score-generating function. A set of population data that differed in the number of scores was synthesised 20 times using the Monte-Carol simulation technique. The FTC system’s performance with different population sizes was evaluated by a gradient metric of the log–LR cost (Cllr). The experimental results revealed two outcomes: 1) that the score-based approach is rather robust against a small population size—in that, with the scores obtained from the 40~60 authors in the database, the stability and the performance of the system become fairly comparable to the system with a maximum number of authors (720); and 2) that poor performance in terms of Cllr, which occurred because of limited background population data, is largely due to poor calibration. The results also indicated that the score-based approach is more robust against data scarcity than the feature-based approach; however, this finding obliges further study.