A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
This provides a simple baseline for stance detection in fake news identification, which is incremental but practical for researchers and practitioners.
The authors tackled the problem of automatic stance detection for fact-checking by developing a straightforward system that achieved third place in the Fake News Challenge, performing competitively with more complex top teams.
Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towards the assertion. Automatic stance evaluation, i.e. stance detection, would arguably facilitate the process of fact checking. In this paper, we present our stance detection system which claimed third place in Stage 1 of the Fake News Challenge. Despite our straightforward approach, our system performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our system as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance detection task.