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        <title>A Unified Propagation Forest-based Framework for Fake News Detection</title>
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    <name type="personal">
        <namePart type="given">Songlin</namePart>
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    <abstract>Fake news’s quick propagation on social media brings severe social ramifications and economic damage. Previous fake news detection usually learn semantic and structural patterns within a single target propagation tree. However, they are usually limited in narrow signals since they do not consider latent information cross other propagation trees. Motivated by a common phenomenon that most fake news is published around a specific hot event/topic, this paper develops a new concept of propagation forest to naturally combine propagation trees in a semantic-aware clustering. We propose a novel Unified Propagation Forest-based framework (UniPF) to fully explore latent correlations between propagation trees to improve fake news detection. Besides, we design a root-induced training strategy, which encourages representations of propagation trees to be closer to their prototypical root nodes. Extensive experiments on four benchmarks consistently suggest the effectiveness and scalability of UniPF.</abstract>
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%0 Conference Proceedings
%T A Unified Propagation Forest-based Framework for Fake News Detection
%A Wei, Lingwei
%A Hu, Dou
%A Lai, Yantong
%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-unified
%X Fake news’s quick propagation on social media brings severe social ramifications and economic damage. Previous fake news detection usually learn semantic and structural patterns within a single target propagation tree. However, they are usually limited in narrow signals since they do not consider latent information cross other propagation trees. Motivated by a common phenomenon that most fake news is published around a specific hot event/topic, this paper develops a new concept of propagation forest to naturally combine propagation trees in a semantic-aware clustering. We propose a novel Unified Propagation Forest-based framework (UniPF) to fully explore latent correlations between propagation trees to improve fake news detection. Besides, we design a root-induced training strategy, which encourages representations of propagation trees to be closer to their prototypical root nodes. Extensive experiments on four benchmarks consistently suggest the effectiveness and scalability of UniPF.
%U https://aclanthology.org/2022.coling-1.244
%P 2769-2779

Markdown (Informal)

[A Unified Propagation Forest-based Framework for Fake News Detection](https://aclanthology.org/2022.coling-1.244) (Wei et al., COLING 2022)

• A Unified Propagation Forest-based Framework for Fake News Detection (Wei et al., COLING 2022)

ACL

• Lingwei Wei, Dou Hu, Yantong Lai, Wei Zhou, and Songlin Hu. 2022. A Unified Propagation Forest-based Framework for Fake News Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2769–2779, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.