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. |
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| 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. |
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Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity (2020.acl-main)
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| Challenge: | Existing methods for predicting a post's stance polarity and intensity don't take into account the stance's degree of intensity. |
| Approach: | They propose to use a new problem to train models for predicting a post's stance polarity and intensity in cyber argumentation. |
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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 . |
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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. |
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| Challenge: | Existing models fail to learn target-specific representations and are prone to overfitting. |
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| Challenge: | stance detection methods are designed for specific network types, either homophilic or heterophilic, and fail to generalize to both. |
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| Challenge: | Stance detection is a task that focuses on the classification of a writer’s viewpoint towards a target. |
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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. |
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TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection (2026.acl-long)
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| Challenge: | Existing benchmarks for political user-level stance detection rely on noisy heuristics or distant supervision. |
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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. |
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| Outcome: | The proposed framework achieves state-of-the-art on two benchmark datasets. |