Check News in One Click: NLP-Empowered Pro-Kremlin Propaganda Detection

Veronika Solopova, Viktoriia Herman, Christoph Benzmüller, Tim Landgraf
1/28/2024
cs.CL

No confident code implementation found for this paper.

Abstract

Many European citizens become targets of the Kremlin propaganda campaigns, aiming to minimise public support for Ukraine, foster a climate of mistrust and disunity, and shape elections (Meister, 2022). To address this challenge, we developed ''Check News in 1 Click'', the first NLP-empowered pro-Kremlin propaganda detection application available in 7 languages, which provides the lay user with feedback on their news, and explains manipulative linguistic features and keywords. We conducted a user study, analysed user entries and models' behaviour paired with questionnaire answers, and investigated the advantages and disadvantages of the proposed interpretative solution.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

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

84% match1 repo32023

Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Tool

82% match2024

EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles

79% match1 repo22024

What Makes You CLIC: Detection of Croatian Clickbait Headlines

78% match2025

RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict

77% match2024

Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models

77% match2026

Cite this paper

@article{solopova2024check,
  title  = {Check News in One Click: NLP-Empowered Pro-Kremlin Propaganda Detection},
  author = {Veronika Solopova and Viktoriia Herman and Christoph Benzmüller and Tim Landgraf},
  year   = {2024},
  eprint = {2401.15717},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.15717v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.15717.svg)](https://codeofpaper.com/paper/2401.15717)

Discussion