Graph Pruning and Representation Learning for Stance Detection

Stance detection has attracted wide attention, especially on social media platforms, which is particularly challenging. It can be regarded as a short text classification task, which aims to identify the author's stance (Favor, Against, or None) expressed within the text towards a specific target. Most of the existing research based on the pre-training language model ignores global word co-occurrence with discontinuous and long-distance semantics in the corpus itself. To overcome such drawbacks, we propose a graph neural network framework that integrates semantic features and structural features to improve the performance of stance detection. Through the fine-tuning of the pre-training model on the task of stance detection, and graph pruning and sampling in graph pre-training model, the representation of text nodes takes into account both global structure and semantic information. Experimental results on the public dataset SemEval-2016 show that our model outperforms other baseline methods. In addition, the ablation experiments demonstrates the effectiveness of each part of our proposed method.