# Automated multilingual detection of Pro-Kremlin propaganda in newspapers and Telegram posts

arXiv: https://arxiv.org/abs/2301.10604 · HTML: https://mlsanity.com/paper/2301.10604/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 56 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking](https://mlsanity.com/paper/2004.14992/) (2020)
- [Interpretable Propaganda Detection in News Articles](https://mlsanity.com/paper/2108.12802/) (2021)
- [Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English](https://mlsanity.com/paper/2101.02359/) (2021)
- [Towards Detection of Subjective Bias using Contextualized Word Embeddings](https://mlsanity.com/paper/2002.06644/) (2020)

## Built on by (most cited first, 6 of 6)

- [From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation](https://mlsanity.com/paper/2404.09138/) (2024)
- [Check News in One Click: NLP-Empowered Pro-Kremlin Propaganda Detection](https://mlsanity.com/paper/2401.15717/) (2024)
- [HQP: A Human-Annotated Dataset for Detecting Online Propaganda](https://mlsanity.com/paper/2304.14931/) (2023)
- [Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation](https://mlsanity.com/paper/2402.03642/) (2024)
- [Uncovering Temporal Framing in the News](https://mlsanity.com/paper/2606.00294/) (2026)
- [SWARM: A Multilingual Human-Annotated Dataset for Russian Propaganda Detection in Search Engine Results](https://mlsanity.com/paper/2609.12653/) (2026)

## Abstract (arXiv)

The full-scale conflict between the Russian Federation and Ukraine generated an unprecedented amount of news articles and social media data reflecting opposing ideologies and narratives. These polarized campaigns have led to mutual accusations of misinformation and fake news, shaping an atmosphere of confusion and mistrust for readers worldwide. This study analyses how the media affected and mirrored public opinion during the first month of the war using news articles and Telegram news channels in Ukrainian, Russian, Romanian and English. We propose and compare two methods of multilingual automated pro-Kremlin propaganda identification, based on Transformers and linguistic features. We analyse the advantages and disadvantages of both methods, their adaptability to new genres and languages, and ethical considerations of their usage for content moderation. With this work, we aim to lay the foundation for further development of moderation tools tailored to the current conflict.