Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning (P19-1)
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| Challenge: | Social media platforms do not always pose authentic information, and rumors spread fear or hate. |
| Approach: | They propose a new multi-task learning approach for rumor detection and stance classification tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art rumor detection approaches on two datasets. |
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| Challenge: | Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline . previous work focused on rumor detection, rumou tracking and stance classification as separate components . |
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| Challenge: | Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness . |
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| Challenge: | Existing research on rumor detection challenges the expressive power of text encoding sequences, and insufficient mining of semantic structural information. |
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Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor (2020.aacl-main)
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| Challenge: | Existing models to classify rumors have low precision and are time consuming. |
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Adversary-Aware Rumor Detection (2021.findings-acl)
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| Challenge: | Existing rumor detection models do not detect malicious attacks, e.g., framing. |
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Rumor Detection on Social Media: Datasets, Methods and Opportunities (D19-50)
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| Challenge: | Social media platforms are used for information gathering, but they also lead to the spreading of rumors and fake news. |
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Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning (2025.coling-main)
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| Challenge: | Existing methods for rumor detection are limited in labeled data, but social media data exhibits an imbalanced distribution with a minority of rumors among massive regular posts. |
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