Papers with stance detection
Integrating Stance Detection and Fact Checking in a Unified Corpus (N18-2)
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| Challenge: | Existing methods for fact checking are not supported by existing datasets, which treat fact checking, document retrieval, source credibility, stance detection and rationale extraction as independent tasks. |
| Approach: | They propose to implement automatic fact checking on an Arabic fact checking corpus, which is the first of its kind. |
| Outcome: | The proposed approach is based on an Arabic fact checking corpus, the first of its kind. |
Computational Analysis of Political Texts: Bridging Research Efforts Across Communities (P19-4)
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| Challenge: | Political scientists have developed and adopted natural language processing (NLP) methods to exploit text as an additional source of data in their analyses. |
| Approach: | This tutorial aims to provide a gentle introduction to methods and tasks related to computational analysis of political texts from both communities. |
| Outcome: | The main goal of this tutorial is to bring the two research communities closer to each other and contribute to faster and more significant developments in this interdisciplinary area. |
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. |
Political Ideology and Polarization: A Multi-dimensional Approach (2022.naacl-main)
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| Challenge: | Recent research has made great strides towards understanding the ideological bias (i.e., stance) of news media along the left-right spectrum. |
| Approach: | They propose a novel approach for the study of ideology based on its left or right positions on the issue being discussed. |
| Outcome: | The proposed method allows for the quantitative and temporal measurement and analysis of polarization as a multidimensional ideological distance. |
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. |
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. |
Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection (2022.findings-emnlp)
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| Challenge: | Stance Detection Tasks require background knowledge especially when there is no explicit target mentioned in text. |
| Approach: | They propose a masked language prompt joint contrastive learning approach to stimulate the knowledge inherit from pre-trained models. |
| Outcome: | The proposed model is effective in stance detection on three benchmarks. |
Analyzing the Dynamics of Climate Change Discourse on Twitter: A New Annotated Corpus and Multi-Aspect Classification (2024.lrec-main)
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Shuvam Shiwakoti, Surendrabikram Thapa, Kritesh Rauniyar, Akshyat Shah, Aashish Bhandari, Usman Naseem
| Challenge: | a lack of data on climate change discourse has highlighted the need for further advancement . a new study examines the discourse on social media platforms that ignores climate change . |
| Approach: | They analyze climate change discourse on Twitter using a meticulously annotated dataset . they find relevance, stance, hate speech, direction of hate, humor and humor are key aspects . |
| Outcome: | The proposed method combines annotated tweets with a dataset of 15,309 tweets . it reveals tweet distribution patterns, stance prevalence, and hate speech trends . |
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. |
PairScale: Analyzing Attitude Change with Pairwise Comparisons (2025.findings-naacl)
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| Challenge: | a text-based framework for measuring attitudes in communities is proposed . the framework uses both implicit and explicit evidence in language to characterize attitudes . |
| Approach: | They propose a text-based framework for measuring attitudes in communities toward issues of interest using language. |
| Outcome: | The proposed framework is validated by examining attitudes on two high-profile issues in the u.s. |
Putting Context in Context: the Impact of Discussion Structure on Text Classification (2024.eacl-long)
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| Challenge: | Current text classification approaches focus on the content to be classified, but contextual information is neglected in many cases. |
| Approach: | They propose to integrate contextual information into a transformer-based model by feeding it as natural language input into . they also experiment with different amounts of training data and analyse local discussion networks in a privacy-compliant way. |
| Outcome: | The proposed model can be generalized to other datasets and is privacy-compliant. |
Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media (2022.naacl-main)
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| Challenge: | Existing approaches to detect vaccine attitudes on social media require abundant annotations and pre-defined aspect categories. |
| Approach: | They propose a semi-supervised approach to detect vaccine attitudes on social media . they use an autoencoding architecture to learn from unlabelled data the topical information of the domain . |
| Outcome: | The proposed model outperforms existing aspect-based models on stance detection and tweet clustering. |
Syntopical Graphs for Computational Argumentation Tasks (2021.acl-long)
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Joe Barrow, Rajiv Jain, Nedim Lipka, Franck Dernoncourt, Vlad Morariu, Varun Manjunatha, Douglas Oard, Philip Resnik, Henning Wachsmuth
| Challenge: | adler and van Doren (1940) proposed a formalized manual process for understanding a topic based on multiple viewpoints. |
| Approach: | They propose a syntopical reading process that emphasizes comparing and contrasting viewpoints to improve topic understanding. |
| Outcome: | The proposed method outperforms approaches that do not use collection-level information. |
Stance Detection in COVID-19 Tweets (2021.acl-long)
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| Challenge: | a global pandemic of COVID-19 has forced major changes in our daily lives . a new stance detection dataset is being used to track the stances of Twitter users . |
| Approach: | They use Twitter stance data to collect stances on topics related to the pandemic . they train models to take advantage of large amounts of unlabeled data . |
| Outcome: | The proposed model improves on existing stance detection datasets and unlabeled data. |
Target-Aware Data Augmentation for Stance Detection (2021.naacl-main)
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| Challenge: | Existing methods for stance detection are not diversified or inconsistent with the given target and label information. |
| Approach: | They propose to augment a text with a conditional masked word prediction task . they propose to replace a target mention with 'target-aware' sentences by replacing a reference word with . |
| Outcome: | The proposed method outperforms existing methods on 11 targets. |
Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter (2020.acl-main)
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Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
| Challenge: | stance detection is a key component of fake news detection, fact-checking and rumor verification. |
| Approach: | They propose to use a large dataset of English tweets for stance detection for a rumor verification task. |
| Outcome: | The proposed dataset contains 51,284 tweets in English, making it the largest available dataset of the type. |
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. |
A Unified Feature Representation for Lexical Connotations (2021.eacl-main)
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| Challenge: | ideological attitudes and stance are often expressed through subtle meanings of words and phrases. |
| Approach: | They propose a method for lexical representations that capture connotations within the embedding space . they define six new fine-grained connotation aspects for nouns and adjectives . |
| Outcome: | The proposed method improves stance detection when data is limited. |
Pro-Woman, Anti-Man? Identifying Gender Bias in Stance Detection (2024.findings-acl)
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| Challenge: | Gender bias has been widely observed in NLP models, which can perpetuate harmful stereotypes and discrimination. |
| Approach: | They construct a dataset to measure gender bias in stance detection using 36k samples . they find that all models are gender-biased and prone to classify sentences that contain male nouns as Against and those with female noun as Favor . |
| Outcome: | The proposed dataset shows that all models are gender-biased and prone to classify sentences that contain male nouns as Against and those with female noun as Favor. |
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. |
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. |
A Few Topical Tweets are Enough for Effective User Stance Detection (2021.eacl-main)
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| Challenge: | Recent work that employs unsupervised classification has shown that user stance detection is highly accurate on vocal Twitter users, but fails for less vocal users, who may have only authored a few tweets about a target. |
| Approach: | They propose to perform user stance detection on vocal Twitter users using contextualized embeddings to represent tweets and then perform unsupervised classification of the user. |
| Outcome: | The proposed method outperforms two baseline methods and achieves 89.6% accuracy and 91.3% macro F-measure on eight controversial topics. |
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection (2021.findings-emnlp)
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| Challenge: | Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. |
| Approach: | They propose a hierarchical message-encoder pre-trained over Twitter for stance prediction task. |
| Outcome: | The proposed model achieves 67% performance on stance prediction task using a pre-trained message-encoder over Twitter. |
Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph (2021.findings-acl)
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| Challenge: | Existing methods for stance detection are not applicable to zero-shot and few-shot scenarios. |
| Approach: | They propose a model that integrates commonsense knowledge into a stance detection model. |
| Outcome: | The proposed model outperforms state-of-the-art methods on zero-shot and few-shot stance detection tasks. |
Incorporating Stock Market Signals for Twitter Stance Detection (2022.acl-long)
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Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
| Challenge: | stance detection is the task of automatically classifying the writer's opinion expressed in a text towards a particular target. |
| Approach: | They propose a robust multi-task neural architecture that combines textual input with high-frequency intra-day time series from stock market prices. |
| Outcome: | The proposed system achieves state-of-the-art on the wt–wt dataset. |
Predicting Stances from Social Media Posts using Factorization Machines (C18-1)
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| Challenge: | Social media provide platforms to express, discuss, and shape opinions about events and issues in the real world. |
| Approach: | They propose to use factorization machines to model user preferences toward topics from social media data to predict whether a given text/user is in favor (agree), against (disagreer), or neutral toward a target topic. |
| Outcome: | The proposed method can predict stances of silent users based on their stance toward other topics and the social media posts of the user. |
What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse (2025.findings-emnlp)
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| Challenge: | Media framing is a method of shaping public perceptions of issues, but the interaction between stance and media frame remains unexplored. |
| Approach: | They propose to use a dataset of climate-change memes annotated with stance and media frames to conceptualize and computationally explore this interaction. |
| Outcome: | The proposed dataset includes 1,184 climate-change memes sourced from 47 subreddits and enables analysis of frame prominence over time and communities. |
Toxicity, Morality, and Speech Act Guided Stance Detection (2023.findings-emnlp)
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| Challenge: | Existing studies that focus on stance detection ignore the speech act, toxic, and moral features of tweets or lack an efficient architecture to detect the attitudes across targets. |
| Approach: | They propose a multitasking model that extracts valence, arousal, and dominance aspects hidden in tweets and injects the emotional sense into the embedded text followed by an efficient attention framework to correctly detect the tweet’s stance. |
| Outcome: | The proposed model exploits the toxicity, morality, and speech act features of the tweets to detect the public's stance. |
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination (2022.lrec-1)
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| Challenge: | Social media are integrated with our daily life and are used to circulate information. |
| Approach: | They develop and publicly release the first largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign. |
| Outcome: | The proposed dataset is the largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign, covering many countries in the Arab region. |
Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization (2022.coling-1)
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| Challenge: | Existing computational approaches to translate languages or creoles back to standard English are challenging . lexical level normalization, syntactic level editing, and semantic level rewriting are key to a successful translation task. |
| Approach: | They propose a computational task to parse Singlish into English using its dialects . they propose to use a dataset to normalize and edit the text to improve translation . |
| Outcome: | The proposed model can improve translation performance and improve stance detection. |
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. |
STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval (2020.findings-emnlp)
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Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
| Challenge: | a new news dataset targets both stance detection (SD) and fine-grained evidence retrieval (ER) . stance Detection (SD), which is a form of multitask learning, has gained increasing interest in recent work . |
| Approach: | They propose a news dataset that targets both stance detection (SD) and fine-grained evidence retrieval (ER) their dataset is an expert-annotated news dataset with 3,291 articles. |
| Outcome: | The proposed dataset is a high-quality benchmark for future research in stance detection and evidence retrieval. |
Knowledge Enhanced Masked Language Model for Stance Detection (2021.naacl-main)
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| Challenge: | Detecting stance on Twitter is difficult because of the short length of each tweet . Twitter content is dynamic, constantly coining new terminology and hashtags . |
| Approach: | They propose a BERT-based fine-tuning method that enhances stance detection models . they use weighted log-odds-ratio to identify words with high stance distinguishability . |
| Outcome: | The proposed method outperforms the state-of-the-art for stance detection on Twitter data about the 2020 US presidential election. |
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. |
DEEM: Dynamic Experienced Expert Modeling for Stance Detection (2024.lrec-main)
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| 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. |
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. |
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. |
Content Fuzzing for Escaping Information Cocoons on Digital Social Media (2026.findings-acl)
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| Challenge: | Information cocoons restrict users’ exposure to posts with diverse viewpoints . social media platforms restrict the range of viewpoints that users encounter . |
| Approach: | They propose a confidence-guided fuzzing framework that rewrites posts while preserving their human-interpreted intent and induces different machine-inferred stance labels. |
| Outcome: | The proposed framework rewrites posts while preserving human-interpreted intent and induces different machine-inferred stance labels while maintaining semantic integrity with respect to the original content. |
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News (2020.coling-main)
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| Challenge: | Using unsupervised representation learning, automated Fake News detection is a challenge for researchers. |
| Approach: | They examine pre-trained language models with respect to their performance on two Fake News related data sets. |
| Outcome: | The proposed models outperform the autoregression-based models on two Fake News related data sets. |
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. |
The Lou Dataset - Exploring the Impact of Gender-Fair Language in German Text Classification (2024.emnlp-main)
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| Challenge: | Gender-fair language fosters inclusion by addressing all genders or using neutral forms. |
| Approach: | They present a dataset that provides high-quality reformulations for German text classification . they find substantial label flips, reduced prediction certainty, and altered attention patterns . |
| Outcome: | The proposed dataset provides high-quality reformulations for German text classification . it finds label flips, reduced prediction certainty, and significantly altered attention patterns . |
Examining Temporalities on Stance Detection towards COVID-19 Vaccination (2024.lrec-main)
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| Challenge: | Existing studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. |
| Approach: | They evaluate a range of transformer-based models using chronological and random splits of social media data to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination. |
| Outcome: | The proposed models show that the models performed better with chronological and random splits than with random split models. |
Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets (2024.emnlp-main)
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| Challenge: | Topic-dependent argument mining is a task that requires expert knowledge to recognize retrieved arguments. |
| Approach: | They investigate the effect of TDAM dataset composition on model performance by using carefully composed training samples and reducing the training sample size by almost 90%. |
| Outcome: | The proposed model can achieve 95% of the maximum performance on three different datasets. |
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. |
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. |
FarExStance: Explainable Stance Detection for Farsi (2025.coling-main)
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Majid Zarharan, Maryam Hashemi, Malika Behroozrazegh, Sauleh Eetemadi, Mohammad Taher Pilehvar, Jennifer Foster
| Challenge: | FarExStance is a new dataset for explainable stance detection in Farsi . it contains extractive explanations as evidence for stance labels and claims . |
| Approach: | They propose a dataset for explainable stance detection in Farsi with extractive explanations as evidence. |
| Outcome: | The proposed model is the most accurate on stance detection, while the best explanation is from few-shot Claude-3.5-Sonnet. |
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. |
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 . |
Is External Information Useful for Stance Detection with LLMs? (2025.findings-acl)
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| Challenge: | Existing studies suggest that external information, such as Wikipedia excerpts, can improve stance detection performance, but whether such information can benefit large language models remains an unanswered question. |
| Approach: | They conduct a systematic evaluation on how Wikipedia and web search external information can affect stance detection across eight LLMs and in three datasets with 12 targets. |
| Outcome: | The proposed model degrades performance in most cases, with macro F1 scores dropping by up to 27.9%, while fine-tuning mitigates but does not eliminate it. |
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study (2024.lrec-main)
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| Challenge: | Existing models for stance detection are not robust enough to be used in a viewpoint-diverse news recommender because the news constantly has new discussion topics. |
| Approach: | They propose to use two stance task definitions (Pro/Con versus Same Side Stance) and two LLM architectures (bi-encoding versus cross-encode) to test model performance. |
| Outcome: | The proposed models outperform the same side-stance definition and other models on stance across different topics. |
Beyond Testers’ Biases: Guiding Model Testing with Knowledge Bases using LLMs (2023.findings-emnlp)
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Chenyang Yang, Rishabh Rustogi, Rachel Brower-Sinning, Grace Lewis, Christian Kaestner, Tongshuang Wu
| Challenge: | Identifying what to test is a step that is largely ignored and poorly supported. |
| Approach: | They propose an interactive tool that supports requirements elicitation for guiding model testing. |
| Outcome: | The proposed tool can help practitioners test models in real-world settings . |
MARASTA: A Multi-dialectal Arabic Cross-domain Stance Corpus (2024.lrec-main)
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Anis Charfi, Mabrouka Ben-Sghaier, Andria Samy Raouf Atalla, Raghda Akasheh, Sara Al-Emadi, Wajdi Zaghouani
| Challenge: | Approximately half of the sentences are in Modern Standard Arabic (MSA) for each region, and the other half is in the region’s respective dialect. |
| Approach: | They propose a cross-domain and multi-dialectal stance corpus for Arabic that includes four regions in the Arab World and covers the main Arabic dialect groups. |
| Outcome: | The proposed corpus outperforms the state-of-the-art dataset in stance detection and dialect and dialect classes. |
Stance Detection on Social Media with Background Knowledge (2023.emnlp-main)
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| Challenge: | Existing studies of stance detection focus on learning stance information about specific targets from context, but in real-world scenarios, we usually have a certain understanding of a target when we express our stance on it. |
| Approach: | They propose to take the background knowledge of the target into account for better stance detection by categorizing it into episodic and discourse knowledge categories and a heuristic retrieval algorithm based on the topic to retrieve the Wikipedia documents relevant to the sample. |
| Outcome: | The proposed framework achieves state-of-the-art on four benchmark datasets showing that the proposed framework is able to detect stances in-target and zero-shot scenarios. |
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. |
Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding (2026.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection. |
| Approach: | They propose a large-scale model that combines large-level annotations with hyperbolic space to model human cognitive states. |
| Outcome: | The proposed model outperforms baseline models on cognitive dimensions on single dimension tasks while retaining strong hierarchical structure. |
A Reinforcement Learning Framework for Cross-Lingual Stance Detection Using Chain-of-Thought Alignment (2025.findings-acl)
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| Challenge: | Existing approaches to cross-lingual stance detection can't effectively perform cross-linguistic transfer of complex reasoning processes. |
| Approach: | They propose a framework to facilitate cross-lingual transfer of complex reasoning processes in stance detection by using cross-linguistic Chain-of-Thought alignment to obtain high-quality CoTs generated from target language inputs. |
| Outcome: | The proposed framework outperforms competing models on four multilingual datasets. |
I love pineapple on pizza != I hate pineapple on pizza: Stance-Aware Sentence Transformers for Opinion Mining (2024.emnlp-main)
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| Challenge: | Sentence transformers excel at grouping topically similar texts, but struggle to differentiate opposing viewpoints on the same topic. |
| Approach: | They propose to fine-tune sentence transformers with arguments for and against controversial claims to enhance their utility for social computing tasks. |
| Outcome: | The proposed model improves opinion mining and stance detection tasks by combining human-generated controversial claims with stance-aware sentences. |
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. |
Are Stereotypes Leading LLMs’ Zero-Shot Stance Detection ? (2025.emnlp-main)
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| Challenge: | Large Language Models inherit stereotypes from their pretraining data, leading to biased behavior toward certain social groups in many tasks. |
| Approach: | They propose to annotate posts in pre-existing stance detection datasets with dialect or vernacular of a specific group and text complexity/readability to investigate whether these attributes influence the model’s stance detect decisions. |
| Outcome: | The proposed model exhibits significant stereotypes when performing stance detection tasks in a zero-shot setting. |
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. |