CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning

TL;DR

CoSD introduces a novel contrastive heterogeneous topic graph learning framework that enhances stance detection by capturing topic-aware semantics and collaborative signals, achieving state-of-the-art results with improved explainability.

Contribution

The paper proposes a new collaborative stance detection framework using contrastive heterogeneous graph learning and a CPA module, addressing limitations of existing neural models.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Demonstrates improved explainability over traditional models.

Effectively captures topic-aware semantics and collaborative signals.

Abstract

Stance detection seeks to identify the viewpoints of individuals either in favor or against a given target or a controversial topic. Current advanced neural models for stance detection typically employ fully parametric softmax classifiers. However, these methods suffer from several limitations, including lack of explainability, insensitivity to the latent data structure, and unimodality, which greatly restrict their performance and applications. To address these challenges, we present a novel collaborative stance detection framework called (CoSD) which leverages contrastive heterogeneous topic graph learning to learn topic-aware semantics and collaborative signals among texts, topics, and stance labels for enhancing stance detection. During training, we construct a heterogeneous graph to structurally organize texts and stances through implicit topics via employing latent Dirichlet allocation. We then perform contrastive graph learning to learn heterogeneous node representations, aggregating informative multi-hop collaborative signals via an elaborate Collaboration Propagation Aggregation (CPA) module. During inference, we introduce a hybrid similarity scoring module to enable the comprehensive incorporation of topic-aware semantics and collaborative signals for stance detection. Extensive experiments on two benchmark datasets demonstrate the state-of-the-art detection performance of CoSD, verifying the effectiveness and explainability of our collaborative framework.