Fake News Challenge Results Announced

Results for the fake news challenge have been announced. The system we submitted was an ensemble of 5 student classifiers written for the Sheffield University COM6513 Natural Language Processing MSc module. Our team came 11th overall – out of 50 submissions. Because the module deadline was so close to the FNC deadline, we didn’t have […]

Fact Checking in the News

With the continued growth of misinformation and the looming European and British election there is an ever-growing need to automatically fact-check publications. There’s been some news coverage regarding the work my supervisor and I have been conducting: Nieman Foundation for Journalism at Harvard, New York Times.

FNC-1 Baseline Announced

Today we announce the baseline accuracy for the fake news challenge and provide code which allows the public to reproduce and build-upon the features/classifier to improve the scores on the hold-out test set. Target to beat: 79.52% Learn more about the Fake News Challenge: http://fakenewschallenge.org Code is available on GitHub: https://github.com/FakeNewsChallenge/fnc-1-baseline