Code for "CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection" (V. Blaschke, M. Korniyenko & S. Tureski, 2020)

[EMNLP 2023] Official repository of paper titled "Detecting Propaganda Techniques in Code-Switched Social Media Text"

Fine-grained Propaganda Detection at NLP4IF 2019

This is the repo for the NLP4IF workshop shared task at EMNLP 2019

WANLP 2022: Multilingual Multi-Granularity Network for Propaganda Detection

An end-to-end pipeline for detecting propaganda in text. The model, built using GloVe word embeddings and a Multi-Layer Perceptron architecture, classifies sentences as "propaganda" or "non-propaganda". The project integrates training, model evaluation, serialization, and inference for streamlined deployment and usage.

Telegram posts from Russian news, misinformation, and propaganda channels made during the first weeks of the 2022 Russian invasion of Ukraine.

Hierarchical, multilingual, multimodal detection of persuasion techniques in memes (SemEval‑2024 Task 4), with a custom hierarchical loss and strong H‑F1 across 4 languages.

DETECTION OF PROPAGANDA TECHNIQUES IN NEWS ARTICLES

Единый Реестр Кремлеботов Ютуба

An AI pipeline that detects propaganda techniques in text and rewrites them into neutral, factual language showing a before/after comparison with quality scores.

Computationally analyzing propaganda network created amid a global pandemic and attempting to influence perceptions at a global scale- Proof of Concept

Computational pipeline for detecting Russian foreign-broadcast propaganda and its amplification of social cleavages in Cyprus. Features multilingual NLP (XLM-RoBERTa, BERTopic, stanza), multi-source scraping (Telegram, Twitter/X, RT, Sputnik), and interrupted time series analysis.

Multi agent framework to evaluate news articles. 5-agent adversarial news bias analysis · PDS + Manipulation Index · Qwen/Mistral · LangGraph · free Colab

Screen text scraping, AI text analysis and mouse automation.

NLP project comparing classical and transformer approaches to propaganda detection on SemEval-2020 Task 11

Telegram-бот для анализа объективности новостей с использованием AI (OpenRouter). Оценивает по трём осям: фактическая точность, объективность, конструктивность. / Telegram bot for news objectivity analysis using AI.

A machine learning project focused on detecting fake news by generating propaganda-style training data. It introduces the PropaNews dataset and uses techniques like natural language inference and self-critical training to improve detection of human-written disinformation.

High-performance propaganda detection for SemEval-2020 Task 11. Includes SOTA NLP models, a real-time Chrome extension, and claim extraction tools.

NLP-based historical text analyzer with GUI and web interface. Supports similarity analysis, theme detection, and propaganda classification.