Inspiration

Fake news is very divisive so its good to be able to identify it.

What it does

Identifies fake news based on semantics analysis. We generate the parts of speech list for an article and calculate the probability that such a transition came from a fake or real news source

How we built it

python

Challenges we ran into

Trying to combine two different classification models without leaking information was challenging but fun

Accomplishments that we're proud of

It works! The model accuracy also succeeds our expectations which is nice

What we learned

machine learning techniques

What's next for News Organiser based On Semantics Evaluation (NOOSE)

Get a larger dataset. Extend it to a browser or bot.

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