Triple Branch BERT Siamese Network for fake news classification on LIAR-PLUS dataset in PyTorch
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Updated
Sep 13, 2022 - Python
Triple Branch BERT Siamese Network for fake news classification on LIAR-PLUS dataset in PyTorch
A baseline implementation for FNC-1
Fake News Detection by Learning Convolution Filters through Contextualized Attention
Chrome extension that battles fake news
Deep Learning model to tackle the Fake News Challenge
Detecting fake news articles by analyzing patterns in writing.
Workspace for the Global AI Hackthon ("Make News Real Again" Challenge): Data Scraping & Cleaning, Text Analytics/NLP, Predictive Modeling, and Feature Engineering in Python
Analyse the connection between crypto news and market caps
Detecting Fake News using AI
A single page web application developed to find whether the entered news is real or fake.
This repository contains the system description and the codes that we implemented for participating in EACL-2024 Shared Task-1
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
Evaluate credibility of online news articles by classifying them as fake or real.
Submission for the Fake News Challenge.
Detects fake news... or, maybe generates fake news... not so sure.
Add a description, image, and links to the fake-news-challenge topic page so that developers can more easily learn about it.
To associate your repository with the fake-news-challenge topic, visit your repo's landing page and select "manage topics."