mandar196/Fake_News_Classifier_NLP
An End to End Machine Learning application for detecting fake news, built model using Passive aggressive classfier and integrated with Flask for web application and deployed in Heroku platform.
This tool helps journalists, content analysts, or media consumers quickly determine the authenticity of news articles. You input the text of a news story, and it tells you whether the story is likely to be 'real' or 'fake'. This is useful for anyone needing to verify information credibility.
No commits in the last 6 months.
Use this if you need a quick, preliminary check on a news article's legitimacy and want a clear 'real' or 'fake' classification.
Not ideal if you require a sophisticated, nuanced analysis of propaganda or deepfake content, or need explanations for the classification.
Stars
8
Forks
4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 15, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/mandar196/Fake_News_Classifier_NLP"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
mihail911/fake-news
Building a fake news detector from initial ideation to model deployment
google-research/fool-me-twice
Game code and data for Fool Me Twice: Entailment from Wikipedia Gamification...
FakeNewsChallenge/fnc-1-baseline
A baseline implementation for FNC-1
palewire/storysniffer
Inspect a URL and estimate if it contains a news story
IKMLab/CFEVER-data
AAAI-24 CFEVER: A Chinese Fact Extraction and VERification Dataset