STEM: Unsupervised STructural EMbedding for Stance Detection

Abstract

Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion -- we construct the interaction network from which we derive topological embedding for each speaker. These speaker embedding enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent speakers with opposing stances. These embedding are then used to divide the speakers into stance-partitions. We evaluate our method on three different datasets from different platforms. Our method outperforms or is comparable with supervised models while providing confidence levels for its output. Furthermore, we demonstrate how the structural embedding relate to the valence expressed by the speakers. Finally, we discuss some limitations inherent to the framework.

Stance detection for online public opinion awareness: An overview

“…The model consists of contextual conditional encoding, generalized topic representation, and topic‐grouped attention. Pick et al 62 derive topological embedding for each speaker from the interaction network, then use this embedding to divide the speakers into stance partitions.…”