Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2017

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-ofthe-art rumor detection models.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, Canada, 2017 July 30 - August 4

First Page

708

Last Page

717

Identifier

10.18653/v1/P17-1066

Publisher

Association for Computational Linguistics

City or Country

Vancouver, Canada

Additional URL

https://doi.org/10.18653/v1/P17-1066

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