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. |
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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. |
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| Challenge: | stance detection studies focus on evaluating stances within individual instances, hindering progress of conversational stance analysis. |
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| Challenge: | Existing benchmarks for political user-level stance detection rely on noisy heuristics or distant supervision. |
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(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. |
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| Challenge: | Existing methods for stance detection for pure texts have limited results to multi-modal content. |
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| Challenge: | Political stance detection is an increasingly relevant part of analyzing the flow of ideas in online environments where discourse is informal and implicitly expressed. |
| Approach: | They evaluate large language models for political stance detection in informal online discourse by analyzing user profiles derived from historical posts. |
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-Stance: A Large-Scale Real World Dataset of Stances in Legal Argumentation (2025.acl-long)
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| Challenge: | Current tools for legal argument reasoning do not support this task. |
| Approach: | They propose to use a large-scale dataset to facilitate work on the legal argument stance classification task by evaluating whether a case summary strengthens or weakens a legal argument. |
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CoFE: A New Dataset of Intra-Multilingual Multi-target Stance Classification from an Online European Participatory Democracy Platform (2022.aacl-short)
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| Challenge: | Stance Recognition is a useful tool for many real-life applications, from misinformation detection to poll verification. |
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| Challenge: | Existing datasets for the ID task only label a text as ideologically left- or right-leaning as a whole, regardless whether the text containing one or more different issues. |
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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. |
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