TSPA: Efficient Target-Stance Detection on Twitter

Abstract

Target-stance detection on large-scale datasets is a core component of many of the most common stance detection applications. However, despite progress in recent years, stance detection research primarily occurs at the document-level on small-scale data. We propose a highly efficient Twitter Stance Propagation Algorithm (TSPA) for detecting user-level stance on Twitter that leverages the social networks of Twitter users and runs in near-linear time. We find TSPA achieves SoTA accuracy against BERT, homogenous Graph Attention Networks (GAT), and heterogenous GAT baselines. Additionally, TSPA's wall-clock time was 10x faster than our best baseline on a GPU and over 100x faster than our best baseline on a CPU.