To make someone do something: mining alert-style directives in Bulgarian social media for low-resource language modelling

AbstractThe work demonstrates how meaningful rhetorical signals can be isolated from a social media dataset even without pre-labelled data or predefined lexicons. By combining unsupervised mining with linguistic theory and interpretable machine learning, the research offers a scalable approach to understanding how language can shape political perception and behaviour in digital spaces.The study focuses on Bulgarian, a morphologically rich, relatively low-resource language, and produces reusable resources—alert constructions, post-level features, and trained classifiers—that are explicitly designed to support low-resource language modelling, including the training and evaluation of neural language models and LLMs for tasks such as content moderation and propaganda-alert detection. The finding that rhetorical salience, not just topical content, drives engagement has implications beyond Bulgarian: it suggests that how something is said may matter as much as what is said in determining a message’s viral potential and persuasive impact.