Stance detection aims to identify the attitude expressed in text towards a given target, with applications in public opinion analysis and misinformation mitigation.Despite recent advances in large language models (LLMs), two key challenges remain: (1) spurious correlations between superficial features and stance labels, and ( 2) lack of cognitive modeling that simulates the transition from intuitive perception to deliberate reasoning.To address these issues, we propose Cognitive-Driven Stance Detection (CDSD), inspired by Kahneman's Dual-Process Theory.CDSD integrates fast intuitive judgment (System 1) and analytical reasoning (System 2), enhanced by three key modules: attention-based cognitive alignment to compare system focus, uncertainty-aware belief update using Bayesian inference, and self-doubt-triggered counterfactual reasoning for re-evaluation under low consistency or high uncertainty.Experimental results on SEM16, P-Stance, and VAST show that CDSD outperforms state-of-the-art methods across multiple LLMs.Notably, CDSD exhibits strong robustness against textual perturbations such as emotional word removal and rhetorical restructuring.By integrating cognitive theory with NLP, our work provides a promising path toward more reliable and interpretable stance detection systems.

Zero-shot stance detection (ZSSD) aims to classify stances towards previously unseen targets without direct supervision on those topics during training. While recent approaches have explored various strategies, they often suffer from limited linguistic diversity or unstable semantic representations, restricting generalization to new domains. To address these challenges, we propose DPSD, a dynamic framework that integrates LLM-assisted data augmentation, multi-granularity feature fusion with contrastive learning, and adaptive prototype updating. Our method enriches the training data with both diverse targets and stylistically varied texts. A gate-controlled fusion mechanism combines deep contextualized features from BERT with shallow lexical patterns via TF-IDF, while contrastive learning refines the feature space by pulling similar instances closer and pushing dissimilar ones apart, thereby improving representation discriminability. Furthermore, we introduce a sliding-window prototype pool that dynamically maintains class-specific prototypes while preserving historical knowledge, ensuring stable and interpretable inference over time. We also incorporate LLM-calibrated semantic similarity as an auxiliary scorer for controlled reasoning. Experimental results on three benchmark datasets -SEM16, P-Stance, and VAST - show that DPSD achieves strong performance in various zero-shot settings, especially in cross-dataset and unseen target scenarios.

Stance detection is a pivotal task in Natural Language Processing (NLP), identifying textual attitudes toward various targets.Despite advances in using Large Language Models (LLMs), challenges persist due to hallucinationmodels generating plausible yet inaccurate content.Addressing these challenges, we introduce MPVStance, a framework that incorporates Multi-Perspective Verification (MPV) with Retrieval-Augmented Generation (RAG) across a structured five-step verification process.Our method enhances stance detection by rigorously validating each response from factual accuracy, logical consistency, contextual relevance, and other perspectives.Extensive testing on the SemEval-2016 and VAST datasets, including scenarios that challenge existing methods and comprehensive ablation studies, demonstrates that MPVStance significantly outperforms current models.It effectively mitigates hallucination issues and sets new benchmarks for reliability and accuracy in stance detection, particularly in zero-shot, few-shot, and challenging scenarios.

Stance detection, a critical task in Natural Language Processing (NLP), aims to identify the attitude expressed in text toward specific targets.Despite advancements in Large Language Models (LLMs), challenges such as limited interpretability and handling nuanced content persist.To address these issues, we propose the Multi-Path Reasoning Framework (MPRF), a novel framework that generates, evaluates, and integrates multiple reasoning paths to improve accuracy, robustness, and transparency in stance detection.Unlike prior work that relies on single-path reasoning or static explanations, MPRF introduces a structured end-to-end pipeline: it first generates diverse reasoning paths through predefined perspectives, then dynamically evaluates and optimizes each path using LLM-based scoring, and finally fuses the results via weighted aggregation to produce interpretable and reliable predictions.Extensive experiments on the SEM16, VAST, and PStance datasets demonstrate that MPRF outperforms existing models.Ablation studies further validate the critical role of MPRF's components, highlighting its effectiveness in enhancing interpretability and handling complex stance detection tasks.