Modeling Human-Like Cognition for Stance Detection: Integrating Intuitive Judgment and Analytical Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have revolutionized stance detection, enabling complex reasoning strategies such as chain-of-thought prompting. |
| Approach: | They propose Cognitive-Driven Stance Detection (CDSD) that integrates fast intuitive judgment and analytical reasoning 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. |
| Outcome: | The proposed method outperforms state-of-the-art methods on SEM16, P-Stance, and VAST. |
Similar Papers
MPRF: Interpretable Stance Detection through Multi-Path Reasoning Framework (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing stance detection methods treat the task as a classification problem, where models output a stance label without providing interpretable reasoning paths. |
| Approach: | They propose a framework that generates, evaluates, and integrates multiple reasoning paths to improve accuracy, robustness, and transparency in stance detection. |
| Outcome: | The proposed framework outperforms existing models on the SEM16, VAST, and PStance datasets and is highly interpretable and reliable. |
LLM-Driven Knowledge Injection Advances Zero-Shot and Cross-Target Stance Detection (2024.naacl-short)
Copied to clipboard
| Challenge: | Existing methods for stance detection focus on background information and not on the accompanying input texts. |
| Approach: | They propose to prompt Large Language Models to explicitly extract the relationship between paired text and unseen target as contextual knowledge and inject it into a generation model BART to exploit the rich contexts and semantics. |
| Outcome: | The proposed model is able to detect stance labels in zero-shot and cross-target scenarios. |
MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods operate by learning to fuse modalities, leading to frequent misjudgments. |
| Approach: | They propose a paradigm shift from *learning to fuse* to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop. |
| Outcome: | The proposed model significantly outperforms baseline models and exhibits strong generalization. |
Human Rationales as Attribution Priors for Explainable Stance Detection (2021.emnlp-main)
Copied to clipboard
| Challenge: | In this work, we present a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. |
| Approach: | They propose a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. |
| Outcome: | The proposed method improves the reasoning of a state-of-the-art classifier in a data-scarce setting at no cost in predictive performance. |
Guiding Computational Stance Detection with Expanded Stance Triangle Framework (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that strategically-enriched data can significantly improve the performance on out-of-domain and cross-target evaluation. |
| Approach: | They propose to decompose a stance detection task from a theoretical perspective and extend it with additional annotations. |
| Outcome: | The proposed task improves performance on out-of-domain and cross-target evaluations using a linguistic framework. |
DEEM: Dynamic Experienced Expert Modeling for Stance Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing work on stance detection tasks using large language models shows promising results, but it may not be able to provide detailed background knowledge. |
| Approach: | They propose a method which leverages the generated experienced experts and lets LLMs reason in a semi-parametric way. |
| Outcome: | The proposed method outperforms methods with self-consistency reasoning and reduces bias. |
Multi-Task Stance Detection with Sentiment and Stance Lexicons (D19-1)
Copied to clipboard
| Challenge: | Recent studies show improvements in stance detection by using attention mechanism or sentiment information. |
| Approach: | They propose a multi-task framework that incorporates attention mechanism and takes sentiment classification as an auxiliary task. |
| Outcome: | The proposed model outperforms state-of-the-art deep learning methods on the SemEval-2016 dataset. |
SSR: Utilizing Simplified Stance Reasoning Process for Robust Stance Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for stance detection are task-agnostic, which fail to utilize task knowledge to better discriminate between genuine and bias features. |
| Approach: | They propose to incorporate stance reasoning process as task knowledge to aid in learning genuine features without using targets. |
| Outcome: | The proposed model achieves better performance than previous task-agnostic debiasing methods on new test sets. |
Can Large Language Models Address Open-Target Stance Detection? (2025.findings-acl)
Copied to clipboard
| Challenge: | Stance detection (SD) identifies a text’s position towards a target, typically labeled as favor, against, or none. |
| Approach: | They introduce Open-Target Stance Detection (OTSD) which aims to determine the position of a text towards a target, typically labeled as favor, against, or none. |
| Outcome: | The proposed model outperforms the only existing task, Target-Stance Extraction (TSE), which benefits from predefined targets. |
A Survey on Stance Detection for Mis- and Disinformation Identification (2022.findings-naacl)
Copied to clipboard
| Challenge: | Understanding attitudes expressed in texts plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentional false information). |
| Approach: | They examine the relationship between stance detection and mis- and disinformation detection online and examine the results of previous studies. |
| Outcome: | The proposed task is a component of fact-checking, rumour detection, and detecting previously fact- checked claims, and is compared with other related tasks such as argumentation mining and sentiment analysis. |