@InProceedings{ma-gao-wong:2017:Long,
  author    = {Ma, Jing  and  Gao, Wei  and  Wong, Kam-Fai},
  title     = {Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {708--717},
  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.},
  url       = {http://aclweb.org/anthology/P17-1066}
}