I'm with @wadkar on this. I think the Fake News Challenge Stage 1 (FNC-1) was a good step towards this effort. They acknowledge (almost) all of these concerns a

I'm with @wadkar on this. I think the Fake News Challenge Stage 1 (FNC-1) was a good step towards this effort. They acknowledge (almost) all of these concerns and start with Stance Detection as their first stage. In this problem, pairs of article headlines and body text were classified into {Agrees, Disagrees, Discusses, Unrelated}. Constructively criticism to the OP: I'd suggest they read the nuance and discussions on the Fake News Challenge [0] and then look into their datasets + evaluation code [1] instead of hand-coding their own "biases" into a {"Fake news","Not-Fake-News"} binary classifier. Feel free to replace "Fake News Challenge" with any other similar effort so that OP isn't tasking themselves with the massive task of "Solving Fake News" all alone. Disclaimer: I don't have any stake in FNC-1 References: [0] http://www.fakenewschallenge.org/ http://www.fakenewschallenge.org/ [1] https://github.com/FakeNewsChallenge/fnc-1 https://github.com/FakeNewsChallenge/fnc-1