STANCY: Stance Classification Based on Consistency Cues
This work addresses the problem of analyzing online claims from different perspectives for applications in media and debate analysis, representing an incremental improvement.
The paper tackles stance classification for controversial claims by proposing a neural network model that enhances BERT representations with a novel consistency constraint, achieving improved performance over state-of-the-art baselines on the Perspectrum dataset.
Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users' perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines.