To make someone do something: mining alert-style directives in Bulgarian social media for low-resource language modelling
Year: 2026
Type: conference-paper
Abstract: The 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 lowresource 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.
Authors: Ruslana Margova, Stanislav Penkov
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: European Commission
OpenAlex ID: W7140121808