Their headline is a bit hyperbolic (oh, the irony, given stance detection!) but the FAQ [1] covers what's really going on: Q: Why did you choose the stance det

Their headline is a bit hyperbolic (oh, the irony, given stance detection!) but the FAQ [1] covers what's really going on: Q: Why did you choose the stance detection task rather than the task of labeling a claim, headline or story True/False, which seems to be what the fake news problem is all about? A: (...) Our extensive discussions with journalists and fact checkers made it clear both how difficult "truth labeling" of claims really is, and how they'd rather have reliable semi-automated tool to help them in do their job better rather than fully-automated system whose performance will inevitably fall far short of 100% accuracy. (...) Q: OK, but what does stance detection have to do with detecting fake news? A: (...) From our discussions with real-life fact checkers, we realized that gathering the relevant background information about a claim or news story, including all sides of the issue, is a critical initial step in a human fact checker's job. One goal of the Fake News Challenge is to push the state-of-the-art in assisting human fact checkers, by helping them quickly gather the information they need to make their assessment. In particular, a good Stance Detection solution would allow a human fact checker to enter a claim or headline and instantly retrieve the top articles that agree, disagree or discuss the claim/headline in question. They could then look at the arguments for and against the claim, and use their human judgment and reasoning skills to assess the validity of the claim in question. Such a tool would enable human fact checkers to be fast and effective. (...) This means they're very much aware that 'solving the fake news issue' in a fully-automated way is a folly, so they are instead looking for tools to classify and retrieve corroborating or dissenting reports about the same topic. I feel this approach demonstrates an awareness of the problem, addresses some of the criticisms raised in this thread, and could lead to useful tools and datasets down the road. [1] http://www.fakenewschallenge.org/#faq http://www.fakenewschallenge.org/#faq