LISTENING FROM AFAR: AN ALGORITHMIC ANALYSIS OF TESTIMONIES FROM THE INTERNATIONAL CRIMINAL COURTS

Despite the recognized importance of witness testimony in addressing systematic violence and human rights violations, reflected in the participation of large numbers of witnesses in international legal processes, establishing facts based on oral testimonies in international criminal tribunals remains a contentious matter. The article develops a new model for assessing judicial attention to and engagement with testimonial narratives, in particular of victims of sexual violence, by conceptualizing the testimonies as “textual datasets." This article presents the results of an algorithm-based approach for analyzing testimonial corpora, applying a generative statistical model known as unsupervised topic modeling. I employ LDA topic modeling for empirically assessing the international courts' capacity to “listen" to large quantities of witness testimonies. Harnessing the large number of testimonies in international criminal trials, I use topic modeling in order to explore latent themes and semantic fields that could benefit the legal process and its critical scholarly appreciation. This article proposes Automated Content Analysis, in particular topic modeling method, as a novel method to assist scholars and practitioners in making sense of complex legal cases, involving large amounts of testimonies, documents, and data, while preserving the voice and vocabulary of the individual witness. This article highlights the potential of topic modeling methods, rooted in Natural Language Processing and Digital Humanities, to overcome critical impediments in empirical legal studies. It demonstrates the method's capacity to transform both as a practical heuristic mechanism that can be employed during the legal proceeding, and in its ex-post analysis in legal scholarship.