@inproceedings{margova-penkov-2026-make,
title = "To make someone do something: mining alert-style directives in {B}ulgarian social media for low-resource language modelling",
author = "Margova, Ruslana and
Penkov, Stanislav",
editor = "Hettiarachchi, Hansi and
Ranasinghe, Tharindu and
Plum, Alistair and
Rayson, Paul and
Mitkov, Ruslan and
Gaber, Mohamed and
Premasiri, Damith and
Tan, Fiona Anting and
Uyangodage, Lasitha",
booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.loreslm-1.6/",
pages = "62--72",
ISBN = "979-8-89176-377-7",
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 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."
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%0 Conference Proceedings
%T To make someone do something: mining alert-style directives in Bulgarian social media for low-resource language modelling
%A Margova, Ruslana
%A Penkov, Stanislav
%Y Hettiarachchi, Hansi
%Y Ranasinghe, Tharindu
%Y Plum, Alistair
%Y Rayson, Paul
%Y Mitkov, Ruslan
%Y Gaber, Mohamed
%Y Premasiri, Damith
%Y Tan, Fiona Anting
%Y Uyangodage, Lasitha
%S Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-377-7
%F margova-penkov-2026-make
%X 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 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.
%U https://aclanthology.org/2026.loreslm-1.6/
%P 62-72
Markdown (Informal)
[To make someone do something: mining alert-style directives in Bulgarian social media for low-resource language modelling](https://aclanthology.org/2026.loreslm-1.6/) (Margova & Penkov, LoResLM 2026)
ACL