News Verification by Exploiting Conflicting Social Viewpoints in Microblogs

Authors

  • Zhiwei Jin Institute of Computing Technology, Chinese Academy of Sciences
  • Juan Cao Institute of Computing Technology, Chinese Academy of Sciences
  • Yongdong Zhang Institute of Computing Technology, Chinese Academy of Sciences
  • Jiebo Luo University of Rochester

DOI:

https://doi.org/10.1609/aaai.v30i1.10382

Abstract

Fake news spreading in social media severely jeopardizes the veracity of online content. Fortunately, with the interactive and open features of microblogs, skeptical and opposing voices against fake news always arise along with it. The conflicting information, ignored by existing studies, is crucial for news verification. In this paper, we take advantage of this "wisdom of crowds" information to improve news verification by mining conflicting viewpoints in microblogs. First, we discover conflicting viewpoints in news tweets with a topic model method. Based on identified tweets' viewpoints, we then build a credibility propagation network of tweets linked with supporting or opposing relations. Finally, with iterative deduction, the credibility propagation on the network generates the final evaluation result for news. Experiments conducted on a real-world data set show that the news verification performance of our approach significantly outperforms those of the baseline approaches.

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Published

2016-03-05

How to Cite

Jin, Z., Cao, J., Zhang, Y., & Luo, J. (2016). News Verification by Exploiting Conflicting Social Viewpoints in Microblogs. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10382

Issue

Section

Technical Papers: NLP and Text Mining