Mapping the global free expression landscape using machine learning

• Published in: SN Applied Sciences
• Main Authors: Sandra Ortega-Martorell, Ryan A. A. Bellfield, Steve Harrison, Drewery Dyke, Nik Williams, Ivan Olier
• Format: Article
• Language: English
• Published: Springer 2023-11-01
• Subjects: Generative topographic mapping; Machine learning; Data visualisation; Freedom of expression; Human rights, censorship, media freedom, academic freedom, digital freedom
• Online Access: https://doi.org/10.1007/s42452-023-05554-x
• ISSN: 2523-3963; 2523-3971

Abstract Freedom of expression is a core human right, yet the forces that seek to suppress it have intensified, increasing the need to develop tools that can measure the rates of freedom globally. In this study, we propose a novel freedom of expression index to gain a nuanced and data-led understanding of the level of censorship across the globe. For this, we used an unsupervised, probabilistic machine learning method, to model the status of the free expression landscape. This index seeks to provide legislators and other policymakers, activists and governments, and non-governmental and intergovernmental organisations, with tools to better inform policy or action decisions. The global nature of the proposed index also means it can become a vital resource/tool for engagement with international and supranational bodies.