A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
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Identifying public misinformation is a complicated and challenging task. Stance detection, i.e. determining the relative perspective a news source takes towards a specific claim, is an important part of evaluating the veracity of the assertion. Automating the process of stance detection would arguably benefit human fact checkers. In this paper, we present our stance detection model which claimed third place in the first stage of the Fake News Challenge. Despite our straightforward approach, our model performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our model as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance detection task.
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