| Challenge: | a global warming denier might frame an opinion of an untrustworthy source with a predicate connoting doubt. |
| Approach: | They propose a dataset of stance-labeled GW sentences and train a BERT classifier to study opinion-framing in the global warming debate. |
| Outcome: | The proposed dataset of stance-labeled GW sentences and a BERT classifier study opinion-framing in the global warming debate. |
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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. |
-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. |
| Outcome: | The proposed dataset is used to facilitate work on the legal argument stance classification task, which involves assessing whether a case summary strengthens or weakens a legal argument (polarity) and to what extent (intensity). |
Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language Models (2026.findings-eacl)
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| Challenge: | a new study examines how financial news media portrays climate change . financial news is the nervous system of the global economy . |
| Approach: | They propose a three-stage Actor–Frame–Argument pipeline that uses large language models to extract actors, stances, frames, and argumentative structures from a 980,061-article corpus. |
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Can We Identify Stance without Target Arguments? A Study for Rumour Stance Classification (2024.lrec-main)
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| Challenge: | Existing target-aware models underperform in cases where the context of the target is crucial. |
| Approach: | They propose a framework to enhance reasoning with the targets and propose 'target-aware' models without awareness of the target. |
| Outcome: | The proposed framework achieves state-of-the-art on two benchmark datasets. |
Towards Fine-grained Classification of Climate Change related Social Media Text (2022.acl-srw)
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| Challenge: | a new study examines the fine-grained classification and classification of climate change-related social media text. |
| Approach: | They propose to use two datasets to analyze climate change-related social media text and propose a fine-grained classification based on the proposed dataset. |
| Outcome: | The proposed datasets are compared with existing datasets and benchmarked using the best-performing model. |
Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset (N18-2)
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| Challenge: | Argument mining is a subset of NLP that deals with extracting arguments from user-based content. |
| Approach: | They propose to use weakly supervised and semi-supervised methods to automatically annotate reviews and provide large annotated datasets. |
| Outcome: | The proposed methods can be used to learn better models for implicit/explicit opinion classification. |
(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. |
Determining Relative Argument Specificity and Stance for Complex Argumentative Structures (P19-1)
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| Challenge: | Existing work on claim specificity and stance has been limited to shallow arguments . a system that can determine the stance of claims employed in argumentation is not sufficient . |
| Approach: | They propose to use a dataset of manually curated argument trees to study claim specificity and stance in argumentation. |
| Outcome: | The proposed dataset consists of manually curated argument trees for 741 controversial topics covering 95,312 unique claims. |
GerCCT: An Annotated Corpus for Mining Arguments in German Tweets on Climate Change (2022.lrec-1)
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| Challenge: | Recent work on annotated resources focused on single argument components, i.e., claim or evidence. |
| Approach: | They propose to annotate a German climate change argument corpus using sarcasm and toxic language to facilitate filtering out non-argumentative content. |
| Outcome: | The proposed corpus is the first to be annotated for argumentation, sarcasm and toxic language. |
The Impact of Stance Object Type on the Quality of Stance Detection (2024.lrec-main)
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| Challenge: | stance is defined by Biber and Finegan as the expression of an author's standpoint and judgment towards a given proposition. |
| Approach: | They analyze the implied knowledge and judgments required when deciding the stance of a text towards each possible stance object type. |
| Outcome: | The proposed models can infer the stance of a text towards any of the three stance object types, namely topics, claims, and frames of communication. |