| Challenge: | Recent work has shown that stance classification is a critical step for information credibility and automated fact-checking. |
| Approach: | They propose a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. |
| Outcome: | The proposed model outperforms existing methods on a Perspectrum dataset and shows that it is more accurate than existing methods. |
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Tribrid: Stance Classification with Neural Inconsistency Detection (2021.emnlp-main)
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| Challenge: | a new neural architecture can be used to classify stances on social media without relying on linguistic features. |
| Approach: | They propose a neural architecture where the input also includes automatically generated negated perspectives over a given claim. |
| Outcome: | The proposed model improves on the original input and removes doubtful predictions over the retained information. |
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. |
STEntConv: Predicting Disagreement between Reddit Users with Stance Detection and a Signed Graph Convolutional Network (2024.lrec-main)
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| Challenge: | Existing methods to detect disagreements on social media platforms have focused on supplementing textual information with user network information, such as Twitter's following system, retweets and hashtags. |
| Approach: | They propose a method which builds a graph of users and named entities and trains a Signed Graph Convolutional Network to detect disagreement between comment and reply posts. |
| Outcome: | The proposed model builds a graph of users and named entities weighted by stance and trains a Signed Graph Convolutional Network (SGCN) to detect disagreement between comment and reply posts. |
Unsupervised stance detection for social media discussions: A generic baseline (2024.eacl-long)
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| Challenge: | stance detection methods are designed for specific network types, either homophilic or heterophilic, and fail to generalize to both. |
| Approach: | They propose to generalize a graph neural network based on text embeddings to homophilic and homophilic networks. |
| Outcome: | The proposed model outperforms state-of-the-art methods across heterophilic and homophilic networks. |
-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). |
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. |
Unsupervised stance detection for arguments from consequences (2020.emnlp-main)
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| Challenge: | Social media platforms are becoming an essential venue for online deliberation . stance detection is a task to determine whether a text is in favor of, against, or unrelated to a given topic. |
| Approach: | They propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. |
| Outcome: | The proposed method outperforms BERT and can be comparable to other methods. |
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. |
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. |
Recognising Agreement and Disagreement between Stances with Reason Comparing Networks (P19-1)
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| Challenge: | Existing methods for (dis)agreement detection focus on conversational settings . however, non-dialogic stance-bearing utterances are common in real-world scenarios . |
| Approach: | They propose a reason comparing network to leverage reason information for stance comparison. |
| Outcome: | The proposed method outperforms baselines on a well-known stance corpus. |