Ranking-based Method for News Stance Detection

ABSTRACT

A valuable step towards news veracity assessment is to understand stance from different information sources, and the process is known as the stance detection. Specifically, the stance detection is to detect four kinds of stances ("agree'', "disagree'', "discuss'' and "unrelated'') of the news towards a claim. Existing methods tried to tackle the stance detection problem by classification-based algorithms. However, classification-based algorithms make a strong assumption that there is clear distinction between any two stances, which may not be held in the context of stance detection. Accordingly, we frame the detection problem as a ranking problem and propose a ranking-based method to improve detection performance. Compared with the classification-based methods, the ranking-based method compare the true stance and false stances and maximize the difference between them. Experimental results demonstrate the effectiveness of our proposed method.

References

1. William Ferreira and Andreas Vlachos. 2016. Emergent: a novel data-set for stance classification Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies. 1163--1168.
2. Benjamin Riedel, Isabelle Augenstein, Georgios P Spithourakis, and Sebastian Riedel. 2017. A simple but tough-to-beat baseline for the Fake News Challenge stance detection task. arXiv preprint arXiv:1707.03264 (2017).
3. Jen Weedon, William Nuland, and Alex Stamos. 2017. Information operations and Facebook. version Vol. 1 (2017), 27.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Natural language processing

      • Information extraction