Papers with Stance detection
PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (2026.tacl-1)
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Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, Ruifeng Xu
| Challenge: | Experimental results demonstrate that a Pruned interpretable knowledge Graph Learning framework for explainable stance detection is state-of-the-art for social media stance prediction. |
| Approach: | They propose a Pruned interpretable knowledge Graph Learning framework for explainable stance detection that incorporates commonsense knowledge and prunes redundant information to ensure precision and minimize noise. |
| Outcome: | The proposed framework achieves state-of-the-art on three public datasets. |
MPRF: Interpretable Stance Detection through Multi-Path Reasoning Framework (2025.emnlp-main)
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| 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. |
(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys (2021.emnlp-main)
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Kenneth Joseph, Sarah Shugars, Ryan Gallagher, Jon Green, Alexi Quintana Mathé, Zijian An, David Lazer
| Challenge: | Existing stance detection methods have been evaluated in comparison to the public opinion data they promise to replace. |
| Approach: | They propose to compare an individual's self-reported stance to the stance inferred from their social media data. |
| Outcome: | The proposed models are compared to a public opinion survey with 1,129 individuals across four salient targets. |
LLM-Driven Knowledge Injection Advances Zero-Shot and Cross-Target Stance Detection (2024.naacl-short)
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| 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. |
Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection (2024.acl-long)
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| Challenge: | Stance detection is used to infer attitudes from human communications . stance decisions involve complex judgments generated by LLMs . |
| Approach: | They propose a method for stance detection which relies on a new prompting framework . it allows for more than one stance object type and no examples of stance attribution . |
| Outcome: | The proposed method outperforms fine-tuned stance detection systems. |
MPVStance: Mitigating Hallucinations in Stance Detection with Multi-Perspective Verification (2025.acl-long)
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| Challenge: | despite advances in large language models, challenges persist due to hallucination-models generating inaccurate content. |
| Approach: | They propose a framework that integrates multi-perspective verification with Retrieval-Augmented Generation to address these challenges. |
| Outcome: | The proposed method outperforms existing models on the SemEval-2016 and VAST datasets. |
Can Large Language Models Address Open-Target Stance Detection? (2025.findings-acl)
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| 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)
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| 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. |
Zero-Shot Conversational Stance Detection: Dataset and Approaches (2025.findings-acl)
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| Challenge: | Existing stance detection datasets are limited to a limited set of specific targets . current models are limited in their ability to detect large numbers of unseen targets based on a large number of unidentified targets. |
| Approach: | They propose a speaker interaction and target-aware prototypical contrastive learning model that can detect public opinion towards specific targets using social media data. |
| Outcome: | The proposed model achieves state-of-the-art in zero-shot conversational stance detection with only an F1-macro score of 43.81%. |
Multilingual Stance Detection in Tweets: The Catalonia Independence Corpus (2020.lrec-1)
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| Challenge: | stance detection is a method to determine the attitude of a text with respect to a specific topic or claim. |
| Approach: | They propose a multilingual dataset for stance detection in Twitter for the Catalan and Spanish languages. |
| Outcome: | The proposed dataset shows that it is well balanced for multilingual and cross-lingual research. |
Tweet Stance Detection Using an Attention based Neural Ensemble Model (N19-1)
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| Challenge: | Existing deep learning approaches to stance detection in twitter are inadequate to deal with the vanishing-gradient and overfitting problems. |
| Approach: | They propose a neural ensemble model that adopts strengths of two LSTM variants to learn better long-term dependencies. |
| Outcome: | The proposed model improves on the existing deep learning models on single and multi-target stance detection datasets. |
Improving Multi-task Stance Detection with Multi-task Interaction Network (2022.emnlp-main)
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| Challenge: | Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection but neglect to capture the fine-grained task-specific interaction between stance and sentiment tasks, thus degrading performance. |
| Approach: | They propose a novel multi-task interaction network (MTIN) that captures the word-level interaction between tasks, so as to obtain richer task representations. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two real-world datasets. |
Stance Detection with Hierarchical Attention Network (C18-1)
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| Challenge: | Recent studies have focused on document-level opinion mining, but linguistic information is correlated with the stance of the document. |
| Approach: | They propose a hierarchical attention neural model to employ various linguistic information to construct the document representation. |
| Outcome: | The proposed model can detect stance of documents on two datasets. |
P-Stance: A Large Dataset for Stance Detection in Political Domain (2021.findings-acl)
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| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
| Approach: | They propose to use a large stance detection dataset in the political domain to detect stances on twitter. |
| Outcome: | The proposed model achieves a macro-average F1-score of 80.53% and can be used to improve cross-domain stance detection. |
Guiding Computational Stance Detection with Expanded Stance Triangle Framework (2023.acl-long)
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| 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. |
Modeling Human-Like Cognition for Stance Detection: Integrating Intuitive Judgment and Analytical Reasoning (2026.acl-long)
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| 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. |
Chain-of-Thought Embeddings for Stance Detection on Social Media (2023.findings-emnlp)
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| Challenge: | Stance detection on social media platforms like Twitter is challenging for Large Language Models (LLMs), as emerging slang and colloquial language in online conversations often contain deeply implicit stance labels. |
| Approach: | They propose to embed COT reasonings into a traditional RoBERTa-based stance detection pipeline by embedding COT stance reasonings and integrating them into slang-based models. |
| Outcome: | The proposed model achieves SOTA performance on multiple stance detection datasets collected from social media. |
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge (2020.acl-main)
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| Challenge: | Existing methods for stance detection are struggling to cope with the data across targets. |
| Approach: | They propose a model that uses external knowledge as a bridge to enable knowledge transfer across different targets. |
| Outcome: | The proposed model outperforms existing methods on a large real-world dataset. |
LLM-Driven Implicit Target Augmentation and Fine-Grained Contextual Modeling for Zero-Shot and Few-Shot Stance Detection (2025.emnlp-main)
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| Challenge: | Recent studies on zero-shot and few-shot stance detection neglect implicit yet semantically important targets. |
| Approach: | They propose a framework that uses Large Language Models to annotate implicit targets . they also propose 'DyMCA' to dynamically adjust text-target contributions based on context . |
| Outcome: | The proposed framework achieves state-of-the-art on a benchmark dataset. |
Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration (2025.naacl-long)
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Ang Li, Jingqian Zhao, Bin Liang, Lin Gui, Hui Wang, Xi Zeng, Xingwei Liang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Large language models generate biased stances due to spurious correlations and preference towards certain individuals and topics. |
| Approach: | They propose a counterfactual Augmented Calibration Network to calibrate potential bias in stance detection of large language models. |
| Outcome: | The proposed calibration network can mitigate biases of large language models, achieving state-of-the-art results. |
Adversarial Learning for Zero-Shot Stance Detection on Social Media (2021.naacl-main)
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| Challenge: | a new model for zero-shot stance detection on Twitter uses adversarial learning to generalize across topics . previous work on zero- shot stance detector on English social media focuses on cross-target stances . |
| Approach: | They propose a model that uses adversarial learning to generalize across topics on Twitter . their model achieves state-of-the-art performance on unseen test topics . |
| Outcome: | The proposed model achieves state-of-the-art performance on unseen topics with minimal computational costs. |
Distilling Calibrated Knowledge for Stance Detection (2023.findings-acl)
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| Challenge: | Existing methods for stance detection ignore meaningful signals among categories offered by hard labels. |
| Approach: | They propose to use knowledge distillation to calibrate teacher predictions in each generation step. |
| Outcome: | The proposed method can calibrate teacher predictions in each generation step and improves stance detection accuracy. |
Target-Oriented Relation Alignment for Cross-Lingual Stance Detection (2023.findings-acl)
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| Challenge: | Existing work on cross-lingual stance detection has ignored the inconsistency in the occurrences and distributions of targets between languages, which consequently degrades the performance of stance detector in low-resource languages. |
| Approach: | They propose a fine-grained method which considers both target-level associations and language-level alignments to learn the in-language and cross-language associations. |
| Outcome: | The proposed method is compared with competing methods under variant settings and shows that it performs better in low-resource languages. |
Dynamic Stance: Modeling Discussions by Labeling the Interactions (2023.findings-emnlp)
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| Challenge: | Stance detection is a popular task that has been modeled as a static task, but its limitations are strong topic-dependent. |
| Approach: | They propose to model stance as a dynamic task by focusing on interactions between a message and their replies. |
| Outcome: | The proposed model shows portability across topics and languages. |
Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings (2025.coling-main)
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| Challenge: | Prior work has demonstrated the importance of the conversational context in stance detection. |
| Approach: | They propose a multimodal architecture for stance detection that fuses transformer-based content embedding with unsupervised structural embeddment. |
| Outcome: | The proposed model outperforms strong baselines on common benchmarks and outperformed existing models on common frameworks. |
Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection (2022.emnlp-main)
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| Challenge: | Existing methods for zero-shot stance detection are labor-intensive to train for each new target. |
| Approach: | They propose a generative data augmentation approach to generate training samples containing unseen and seen targets and map them into the same embedding space with contrastive learning. |
| Outcome: | The proposed model achieves state-of-the-art on most topics in the task of zero-shot stance detection. |
EDDA: An Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection (2024.lrec-main)
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| Challenge: | Existing methods for enhancing text or data are limited by lack of logical connections between generated texts and training data. |
| Approach: | They propose an encoder-decoder data augmentation framework that combines large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships. |
| Outcome: | The proposed framework significantly improves over state-of-the-art methods on benchmark datasets while enabling interpretable rationale-based learning. |
Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation (2021.emnlp-main)
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| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
| Approach: | They propose a method that applies instance-specific temperature scaling to the teacher and student predictions. |
| Outcome: | The proposed method outperforms the state-of-the-art on all datasets and on multiple datasets. |
A New Direction in Stance Detection: Target-Stance Extraction in the Wild (2023.acl-long)
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| Challenge: | Existing methods for stance detection assume that the target is known in advance . Existing tasks use implicit mentions in the source text and are infeasible to have manual annotations at a large scale. |
| Approach: | They propose a task Target-Stance Extraction that aims to extract the (target, stance) pair from social media texts. |
| Outcome: | The proposed task can facilitate future research in the field of stance detection. |
ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarization (2023.emnlp-main)
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| Challenge: | Recent development of large language models (LLMs) have boosted interest on dialogue agents . however, research on these tasks is limited by the insufficiency of public datasets . stance detection and debate summarization are key for engaging argumentative dialogues - but are not available for non-English languages. |
| Approach: | They propose to use ORCHID to benchmark stance detection and debate summarization in Chinese debates. |
| Outcome: | The proposed task is based on 1,218 real-world debates conducted in Chinese on 476 unique topics. |
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection (2022.coling-1)
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| Challenge: | stance detection is a task to identify attitudes from opinions towards certain targets, but it is expensive and time-consuming . stance detector is based on labeled data, but unlabeled data can be collected easier . |
| Approach: | They propose a semi-supervised framework for few-shot stance detection that uses unlabeled data to learn more distinguishable representations for different targets. |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiple benchmark datasets. |
GunStance: Stance Detection for Gun Control and Gun Regulation (2024.acl-long)
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Nikesh Gyawali, Iustin Sirbu, Tiberiu Sosea, Sarthak Khanal, Doina Caragea, Traian Rebedea, Cornelia Caragea
| Challenge: | Social media, especially Twitter, has been a melting pot for such debates. |
| Approach: | They propose to annotate tweets relevant to shooting events into three classes: In-Favor, Against, and Neutral. |
| Outcome: | The proposed approach outperforms supervised, semi-supervised, and LLM-based zero-shot models on the dataset. |
Multi-Task Stance Detection with Sentiment and Stance Lexicons (D19-1)
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| 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. |
Incorporating Label Dependencies in Multilabel Stance Detection (D19-1)
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| Challenge: | Stance detection is a well-studied task in social media, but previous work focused on multiclass versions of the problem where labels are mutually exclusive. |
| Approach: | They propose a method that explicitly incorporates label dependencies in the training objective and reduces multilabel to multiclass learning. |
| Outcome: | The proposed method improves on two out of three datasets and reduces multilabel to multiclass learning. |
Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework (2023.emnlp-main)
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| Challenge: | Existing studies on stance detection were conducted mainly in English due to the low-resource problem in most non-English languages. |
| Approach: | They propose to use a cross-lingual teacher and a teacher to transfer knowledge from source to target language to bridge the discrepancy between languages. |
| Outcome: | The proposed framework bridges the discrepancy between languages and generalizes the knowledge to unseen targets in target language. |
Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation (2022.emnlp-main)
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| Challenge: | a new task is needed to understand the interaction between entities when inferring stances. |
| Approach: | They propose a task that primes models to identify entities in their canonical names and discern stances jointly. |
| Outcome: | The proposed model outperforms strong comparisons by large margins. |
TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings (2023.emnlp-main)
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| Challenge: | Recent studies have focused on topic-specific stance classifiers that fail to generalize to unseen topics. |
| Approach: | They propose to use contrastive learning and an unlabeled dataset to train topic-agnostic/TAG and topic-aligned/TAW embeddings for use in downstream stance detection. |
| Outcome: | The proposed model achieves state-of-the-art performance across several public stance detection datasets (0.771 F1-score on the Zero-shot VAST dataset). |
Cross-Domain Label-Adaptive Stance Detection (2021.emnlp-main)
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| Challenge: | Stance detection is a task that focuses on the classification of a writer’s viewpoint towards a target. |
| Approach: | They propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels. |
| Outcome: | The proposed framework shows that it can be used to predict unseen labels over strong baselines. |
An LLM-Enabled Knowledge Elicitation and Retrieval Framework for Zero-Shot Cross-Lingual Stance Identification (2024.findings-emnlp)
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| Challenge: | Existing research is conducted in monolingual setting on English datasets, whereas in other low-resource languages, it lacks sufficient data for training quality stance detection models. |
| Approach: | They propose a knowledge elicitation and retrieval framework that leverages the capability of large language models for stance knowledge acquisition and matches the target language input to the most relevant stance information. |
| Outcome: | The proposed framework improves on multilingual datasets and competitive baselines. |
Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations (2020.emnlp-main)
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| Challenge: | Existing methods for stance detection are topic-specific and cross-target stance. |
| Approach: | They propose a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. |
| Outcome: | The proposed model improves performance on a number of challenging linguistic phenomena. |
Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)
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| Challenge: | Existing methods for stance detection for pure texts have limited results to multi-modal content. |
| Approach: | They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities. |
| Outcome: | The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter . |
Journalism-Guided Agentic In-context Learning for News Stance Detection (2025.emnlp-main)
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| Challenge: | Existing stance detection research on news content is limited to short texts and high-resource languages. |
| Approach: | They propose a dataset for article-level stance detection that integrates viewpoints into recommendation algorithms and a framework that employs a language model agent to predict the stances of key structural segments. |
| Outcome: | The proposed framework outperforms existing methods in identifying article stances and uncovering patterns of media bias. |
Using Convolution Neural Network with BERT for Stance Detection in Vietnamese (2022.lrec-1)
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| Challenge: | Stance detection is a task of automatically eliciting stance information towards a specific claim made by a primary author. |
| Approach: | They propose an architecture using transformers to detect stances in Vietnamese claims . they exploit BERT to extract contextual word embeddings instead of traditional word2vec models . |
| Outcome: | The proposed model outperforms the previous methods on a public dataset. |
Exploring Artificial Image Generation for Stance Detection (2025.emnlp-main)
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| Challenge: | Existing approaches to stance detection focus on textual content, which may not capture the implicit stance conveyed by the author. |
| Approach: | They propose a novel approach that transforms original texts into artificially generated images and uses the visual representation to enhance stance detection. |
| Outcome: | The proposed model is able to detect author's stance from a set of artificially generated images and then leverages both the original textual content and the generated image to identify the author' stance. |
PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labels (2025.findings-emnlp)
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| Challenge: | Stance detection is a method of identifying the viewpoint expressed in text toward a specific target, such as a political figure. |
| Approach: | They present a dataset for the 2024 U.S. presidential election that includes 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories. |
| Outcome: | The proposed dataset comprises 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories. |
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning (2024.lrec-main)
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| Challenge: | Stance Reasoner is a model for zero-shot stance detection on social media platforms that can be used to extract opinions from opinionated content. |
| Approach: | They propose a method that leverages explicit reasoning over background knowledge to guide the model’s inference about the document’s stance on a target. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on 3 Twitter datasets, including fully supervised models. |
Target-Adaptive Consistency Enhanced Prompt-Tuning for Multi-Domain Stance Detection (2024.lrec-main)
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| Challenge: | Stance detection is a fundamental task in natural language processing, but it is challenging due to diverse expressions and topics related to the targets from multiple domains. |
| Approach: | They propose a prompt-tuning method that incorporates target knowledge and prior knowledge to construct target-adaptive verbalizers for diverse domains. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on nine stance detection datasets from multiple domains. |
StanceAttack: Adversarial Attack for Stance Detection (2026.findings-acl)
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| Challenge: | pretrained language models (PLMs) have greatly enhanced stance detection, but they remain vulnerable to adversarial attacks. |
| Approach: | They propose an adversarial attack method that uses ChatGPT to create adversarials that can mislead well-trained stance detection models. |
| Outcome: | The proposed method outperforms existing adversarial methods with higher success rates and fewer retries on two benchmark datasets. |