Modeling the fake news challenge as a cross-level stance detection task

The 2017 Fake News Challenge Stage 1, a shared task for stance detection of news articles and claims pairs, has received a lot of attention in recent years [3]. The provided dataset is highly unbalanced, with a skewed distribution towards unrelated samples - that is, randomly generated pairs of news and claims belonging to different topics. This imbalance favored systems which performed particularly well in classifying those noisy samples, something which does not require a deep semantic understanding. In this paper, we propose a simple architecture based on conditional encoding, carefully designed to model the internal structure of a news article and its relations with a claim. We demonstrate that our model, which only leverages information from word embeddings, can outperform a system based on a large number of hand-engineered features, which replicates one of the winning systems at the Fake News Challenge [6], in the stance detection of the related samples