Detect rumors in microblog posts using propagation structure via kernel learning

Jing Ma, Wei Gao, Kam Fai Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
PublisherAssociation for Computational Linguistics (ACL)
Pages708-717
Number of pages10
Volume1
ISBN (Electronic)9781945626753
DOIs
Publication statusPublished - 1 Jan 2017
Event55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, Canada
Duration: 30 Jul 20174 Aug 2017

Other

Other55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
CountryCanada
CityVancouver
Period30/7/174/8/17

Fingerprint

rumor
learning
social media
news
art
Kernel
Rumor
Fake

ASJC Scopus subject areas

  • Language and Linguistics
  • Artificial Intelligence
  • Software
  • Linguistics and Language

Cite this

Ma, J., Gao, W., & Wong, K. F. (2017). Detect rumors in microblog posts using propagation structure via kernel learning. In ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) (Vol. 1, pp. 708-717). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/P17-1066

Detect rumors in microblog posts using propagation structure via kernel learning. / Ma, Jing; Gao, Wei; Wong, Kam Fai.

ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). Vol. 1 Association for Computational Linguistics (ACL), 2017. p. 708-717.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ma, J, Gao, W & Wong, KF 2017, Detect rumors in microblog posts using propagation structure via kernel learning. in ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). vol. 1, Association for Computational Linguistics (ACL), pp. 708-717, 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30/7/17. https://doi.org/10.18653/v1/P17-1066
Ma J, Gao W, Wong KF. Detect rumors in microblog posts using propagation structure via kernel learning. In ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). Vol. 1. Association for Computational Linguistics (ACL). 2017. p. 708-717 https://doi.org/10.18653/v1/P17-1066
Ma, Jing ; Gao, Wei ; Wong, Kam Fai. / Detect rumors in microblog posts using propagation structure via kernel learning. ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). Vol. 1 Association for Computational Linguistics (ACL), 2017. pp. 708-717
@inproceedings{2c16b6ad1569495fac0bd5e212852b9d,
title = "Detect rumors in microblog posts using propagation structure via kernel learning",
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.",
author = "Jing Ma and Wei Gao and Wong, {Kam Fai}",
year = "2017",
month = "1",
day = "1",
doi = "10.18653/v1/P17-1066",
language = "English",
volume = "1",
pages = "708--717",
booktitle = "ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)",
publisher = "Association for Computational Linguistics (ACL)",

}

TY - GEN

T1 - Detect rumors in microblog posts using propagation structure via kernel learning

AU - Ma, Jing

AU - Gao, Wei

AU - Wong, Kam Fai

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85040238063&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85040238063&partnerID=8YFLogxK

U2 - 10.18653/v1/P17-1066

DO - 10.18653/v1/P17-1066

M3 - Conference contribution

VL - 1

SP - 708

EP - 717

BT - ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)

PB - Association for Computational Linguistics (ACL)

ER -