Papers with rumor propagation

3 papers
Rumor Detection on Social Media with Temporal Propagation Structure Optimization (2025.coling-main)

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Challenge: Existing methods for detecting rumors on social media neglect the temporal aspect of rumor propagation.
Approach: They propose a method that incorporates temporal information by building a weighted propagation tree and a coding tree.
Outcome: The proposed approach preserves essential structure of rumor propagation while reducing noise.
SWAM: Adaptive Sliding Window and Memory-Augmented Attention Model for Rumor Detection (2025.emnlp-main)

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Challenge: Existing methods for rumor detection on social media focus on static graphs, ignoring dynamic and incremental propagation . rumour detection on the social media platform is crucial to mitigating harmful effects of rumors.
Approach: They propose a sliding window and memory-augmented attention model for rumor detection . they use a dynamic propagation graph and memory to capture the long-term dependency .
Outcome: The proposed model is compared with the state-of-the-art models on two public datasets.
GCML: Gradient Coherence Guided Meta-Learning for Cross-Domain Emerging Topic Rumor Detection (2025.emnlp-main)

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Challenge: Existing domain adaptation rumor detection methods ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation.
Approach: They propose a Gradient Coherence guided Meta-Learning approach for emerging topics rumor detection that selectively learns more "generalizable" tasks that are more beneficial in adapting to the target domain.
Outcome: The proposed method outperforms baselines on real-world datasets and significantly outperformed traditional methods on the in-domain condition.

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