The ability of assessing any type of linguistic complexity of any given contents could potentially improve knowledge reproduction, especially tacit knowledge which can be expensive during a pandemic. In this paper, we develop a simple and crosslinguistic model of complexity which considers formal accounts on the study of linguistic systems, but can be easily implemented by non-linguists’ groups, e.g., communication experts and policymakers. To test our model, we conduct a study on a corpus extracted from the World Health Organization (WHO)’s emergency learning platform in 6 languages. Data extracted from open-access encyclopaedic entries act as control groups. The results show that the measurements adopted signal a trend for a minimization of complexity and can be exploited as features for (automatic) text classification.