  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.
