Papers with rumor detection models
Exploring Hyperbolic Hierarchical Structure for Multimodal Rumor Detection (2025.findings-emnlp)
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| Challenge: | rumor detection models often assume a simplistic one-to-one alignment between modalities . authors present a method that preserves hierarchical, non-linear relationships . |
| Approach: | They propose a method that uses hyperbolic geometry to preserve hierarchical relationships . it decomposes image and text content into three levels and embeds them in hyperbolical space . |
| Outcome: | The proposed method preserves hierarchical relationships rather than representing them at a flat semantic level. |
It’s about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits (2023.findings-eacl)
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| Challenge: | Current rumor detection benchmarks use random splits as training, development and test sets which results in topical overlaps. |
| Approach: | They propose to use chronological rather than random splits for rumor classification . they propose to always use chronological splits to minimize topical overlaps . |
| Outcome: | The proposed model overestimates performance on four popular rumor detection benchmarks considering chronological instead of random splits. |
Adversary-Aware Rumor Detection (2021.findings-acl)
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| Challenge: | Existing rumor detection models do not detect malicious attacks, e.g., framing. |
| Approach: | They propose a weighted-edge transformer-graph network and position-aware Adversarial Response Generator to improve the vulnerability of detection models. |
| Outcome: | The proposed framework achieves state-of-the-art on various rumor detection tasks and maintains performance under adversarial learning. |
Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection (2021.findings-emnlp)
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| Challenge: | Existing rumor detection models focus on textual data to extract distinctive features, but they fail to capture the inconsistency information among the content and background knowledge. |
| Approach: | They propose to capture inconsistency semantics and content-knowledge level in a unified framework to detect rumors with multimedia content. |
| Outcome: | Extensive experiments on two public real-world datasets show that the proposed network outperforms the state-of-the-art models. |
FGDGNN: Fine-Grained Dynamic Graph Neural Network for Rumor Detection on Social Media (2025.findings-acl)
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| Challenge: | Existing methods for detecting rumors on social media focus on coarse-grained temporal information and ignore fine-grain temporal dynamics. |
| Approach: | They propose a fine-grained dynamic graph neural network model which incorporates fine-grain temporal information into a unified framework for rumor detection. |
| Outcome: | The proposed model improves on three public real-world datasets. |
Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets (2024.lrec-main)
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| Challenge: | Past research has indicated that content-based rumor detection models perform less effectively on unseen rumors. |
| Approach: | They propose to use data split strategies to minimize the effects of temporal concept drift in static datasets during the training of rumor detection methods. |
| Outcome: | The proposed model over-relys on the information derived from the rumors’ source post and overlooks the significant role that contextual information can play. |
Leveraging Contrastive Learning and Knowledge Distillation for Incomplete Modality Rumor Detection (2023.findings-emnlp)
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| Challenge: | Existing rumor detection models neglect the semantic coherence between text and image components in multimodal posts . Existing models neglect incomplete modalities in single modal posts, such as missing text or images . |
| Approach: | They propose a framework for incomplete modality rumor detection that captures semantic consistency between text and image pairs while enhancing model generalization to incomplete modalities within individual posts. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media. |
MONTROSE: LLM-driven Monte Carlo Tree Search Self-Refinement for Cross-Domain Rumor Detection (2025.findings-acl)
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| Challenge: | Existing feature alignment methods are susceptible to task interference during training. |
| Approach: | MONTROSE is a cross-domain rumor detection method that generates high-quality synthetic data for the target domain and a domain-sharpness-aware approach to train models with these synthetic data. |
| Outcome: | Experiments show that MONTROSE improves in cross-domain rumor detection. |