Evidence map›Paper›PMID 41331252›Full record

ArticleScientific data2025

HALT-PROP: Human-Annotated Lithuanian Textual Corpus for Propaganda Narratives and Techniques.

Ieva Rizgelienė, Vilma Zubaitienė, Nerijus Maliukevičius, Virginijus Marcinkevičius

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ieva RizgelienėVilnius University Institute of Data Science and Digital Technologies, Vilnius, Lithuania. ieva.rizgeliene@mif.stud.vu.lt.
Vilma ZubaitienėVilnius University Faculty of Philology Institute of Applied Linguistics, Vilnius, Lithuania.
Nerijus MaliukevičiusVilnius University Institute of International Relations and Political Science, Vilnius, Lithuania.
Virginijus MarcinkevičiusVilnius University Institute of Data Science and Digital Technologies, Vilnius, Lithuania.

Funding

Lietuvos Mokslo Taryba (Research Council of Lithuania) S-VIS-23-8
6 · The paper itself

Abstract

In the contemporary technological landscape, propaganda has become one of the most pervasive tools in information warfare. Social media platforms and entire media ecosystems are leveraged to disseminate hostile propaganda aimed at polarizing societies, destabilizing states, and eroding longstanding democratic processes. Malign propaganda is not only common in widely-spoken languages but also targets less-spoken languages to maximize its reach and influence. While progress has been made in developing models capable of detecting propaganda, most advances have focused on high-resource languages. In contrast, low-resource languages continue to face significant limitations, the most critical being the scarcity of annotated datasets. In many regions and countries, such resources are entirely absent. To address this gap, we present the HALT-PROP dataset, the first human-annotated Lithuanian textual propaganda corpus. The corpus comprises two complementary datasets: (1) 2,870 news articles manually labeled by five experts at the article level to identify the presence of propaganda; and (2) a subset of 1,000 articles annotated for specific propaganda techniques and narratives using a cross-annotation approach.

Indexed as

Social MediaHumansLanguageLithuaniaNarration

Identifiers

PMID41331252
PMCPMC12816137

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.