📖 A curated list of resources dedicated to Fake News Detection

✨ Fake news classification using source adaptive framework - BE Project 🎓The repository contains Detailed Documentation of the project, Classification pipeline, Architecture, System Interface Design, Tech stack used.

[NeurIPS 2022 Oral (Spotlight)] Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification

Classifying Verified used users on Twitter based on how likely they are to share Fake News articles

a consolidated and cleaned up fake news dataset classified in the following categories: reliable, unreliable, political, bias, fake, conspiracy, rumor clickbait, junk science, satire, hate

The project classifies fake news using k-Nearest Neighbors and Multilayer Perceptron, with preprocessing, TF-IDF and PCA for feature extraction. Evaluation metrics include accuracy, precision, recall and F1-score, with insights visualized through word frequency analyses, feature distributions, and model performance graphs.

Collection of Portuguese Fact-Checking Benchmarks.

In this notebook we analyze and classify news articles using machine learning techniques, including Logistic Regression, Naive Bayes, Support Vector Machines, and Random Forests. Explore text vectorization and NLP for accurate news categorization.

In this project, a dataset about fake news is collected and combined with pre-existing datasets. In addition, a model that can detect if an input text is a piece of fake news is created.

English and Turkish Misinformation Detection Dataset from "MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection"

This WebApp uses a combination of a Large Language Model and Logistic Regression to make predictions to identify fake news.

Red wine quality prediction based on multi-dimensional vectors. Each dimension is a different sensor metric. In the same repository, there is another model that predicts if a news title is fake or not (onion-or-not dataset).