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        <title>Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection</title>
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    <abstract>The widespread of fake news has detrimental societal effects. Recent works model information propagation as graph structure and aggregate structural features from user interactions for fake news detection. However, they usually neglect a broader propagation uncertainty issue, caused by some missing and unreliable interactions during actual spreading, and suffer from learning accurate and diverse structural properties. In this paper, we propose a novel dual graph-based model, Uncertainty-aware Propagation Structure Reconstruction (UPSR) for improving fake news detection. Specifically, after the original propagation modeling, we introduce propagation structure reconstruction to fully explore latent interactions in the actual propagation. We design a novel Gaussian Propagation Estimation to refine the original deterministic node representation by multiple Gaussian distributions and arise latent interactions with KL divergence between distributions in a multi-facet manner. Extensive experiments on two real-world datasets demonstrate the effectiveness and superiority of our model.</abstract>
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%0 Conference Proceedings
%T Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection
%A Wei, Lingwei
%A Hu, Dou
%A Zhou, Wei
%A Hu, Songlin
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F wei-etal-2022-uncertainty
%X The widespread of fake news has detrimental societal effects. Recent works model information propagation as graph structure and aggregate structural features from user interactions for fake news detection. However, they usually neglect a broader propagation uncertainty issue, caused by some missing and unreliable interactions during actual spreading, and suffer from learning accurate and diverse structural properties. In this paper, we propose a novel dual graph-based model, Uncertainty-aware Propagation Structure Reconstruction (UPSR) for improving fake news detection. Specifically, after the original propagation modeling, we introduce propagation structure reconstruction to fully explore latent interactions in the actual propagation. We design a novel Gaussian Propagation Estimation to refine the original deterministic node representation by multiple Gaussian distributions and arise latent interactions with KL divergence between distributions in a multi-facet manner. Extensive experiments on two real-world datasets demonstrate the effectiveness and superiority of our model.
%U https://aclanthology.org/2022.coling-1.243
%P 2759-2768

Markdown (Informal)

[Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection](https://aclanthology.org/2022.coling-1.243) (Wei et al., COLING 2022)

• Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection (Wei et al., COLING 2022)

ACL

• Lingwei Wei, Dou Hu, Wei Zhou, and Songlin Hu. 2022. Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2759–2768, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.