Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

Jing Ma, Wei Gao, Kam-Fai Wong


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.
Anthology ID:
P17-1066
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
708–717
Language:
URL:
https://aclanthology.org/P17-1066
DOI:
10.18653/v1/P17-1066
Bibkey:
Cite (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.
Cite (Informal):
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (Ma et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1066.pdf
Presentation:
 P17-1066.Presentation.pdf
Video:
 https://vimeo.com/234955713