Areesha-Tahir/Fake-News-Detection-Using-Naive-Bayes
Fake news detection using Naïve Bayes in Python along with confusion matrix calculated using sklearn.
This tool helps journalists, content moderators, or anyone managing information separate news that is real from news that is fake. You input text-based news articles, and it classifies them, indicating whether each piece of news is likely authentic or fabricated. This is designed for individuals who need to quickly assess the veracity of news content.
No commits in the last 6 months.
Use this if you need to quickly identify potentially fake news articles from a collection of text data, without deep technical expertise.
Not ideal if you require highly nuanced or context-specific analysis beyond simple 'real' or 'fake' categorization.
Stars
10
Forks
2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 16, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/Areesha-Tahir/Fake-News-Detection-Using-Naive-Bayes"
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