🔍 Fake News Detection App – Built with ML & NLP!

Show the Community!
1

Hey Streamlit Community! :wave:

I just deployed my Fake News Detection App, designed to analyze and verify the authenticity of news articles. It’s powered by ML + NLP, allowing users to enter news text or a URL and get a prediction with explainability.

:rocket: Features:

:heavy_check_mark: Text & URL-based news analysis
:heavy_check_mark: ML-powered predictions with confidence scores
:heavy_check_mark: LIME explainability for transparency
:heavy_check_mark: User voting to refine predictions
:heavy_check_mark: Firebase integration for user interactions

:computer: Tech Stack:
:small_blue_diamond: ML Model: Random Forest with TF-IDF + HashingVectorizer
:small_blue_diamond: Framework: Streamlit for UI
:small_blue_diamond: Hosting: Hugging Face
:small_blue_diamond: Database: Firebase

:link: Try it here: https://fake-news-detection-c5wy5kuvhc7gnn6cfruegk.streamlit.app/

Would love feedback from the community! What features should I add next? :thinking:

streamlit #MachineLearning ai nlp #FakeNewsDetection #DataScience

2

Hey @Risaal, would like to check your app out, but the link doesn’t work. Did you try running this link in a guest browser?

3

Ok let me check

4

Now, please check and let me know and please give your suggestions & feedback

5

Good work, try using an ensemble of various models to make stronger prediction, then taking majority vote, and replacing TF-IDF with transformer. Are you redirecting user feedback back into model training? That will enhance your model’s accuracy, while simultaneously dealing with Model Drift.