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        <title>Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning</title>
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        <namePart type="given">Wei</namePart>
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        <namePart type="given">Kam-Fai</namePart>
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    <abstract>How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted and developed over time. We then propose a kernel-based method called Propagation Tree Kernel, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures. Experimental results on two real-world datasets demonstrate that the proposed kernel-based approach can detect rumors more quickly and accurately than state-of-the-art rumor detection models.</abstract>
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%0 Conference Proceedings
%T Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning
%A Ma, Jing
%A Gao, Wei
%A Wong, Kam-Fai
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F ma-etal-2017-detect
%X How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted and developed over time. We then propose a kernel-based method called Propagation Tree Kernel, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures. Experimental results on two real-world datasets demonstrate that the proposed kernel-based approach can detect rumors more quickly and accurately than state-of-the-art rumor detection models.
%R 10.18653/v1/P17-1066
%U https://aclanthology.org/P17-1066
%U https://doi.org/10.18653/v1/P17-1066
%P 708-717

Markdown (Informal)

[Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning](https://aclanthology.org/P17-1066) (Ma et al., ACL 2017)

• Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (Ma et al., ACL 2017)

ACL

• Jing Ma, Wei Gao, and Kam-Fai Wong. 2017. Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 708–717, Vancouver, Canada. Association for Computational Linguistics.