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
Citation
MA, Jing; GAO, Wei; and WONG, Kam-Fai.
Detect rumors in microblog posts using propagation structure via kernel learning. (2017). Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, Canada, 2017 July 30 - August 4. 708-717.
Available at: https://ink.library.smu.edu.sg/sis_research/4563
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.18653/v1/P17-1066