Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (11): 3139-3144.DOI: 10.11772/j.issn.1001-9081.2021030451

• Artificial intelligence • Previous Articles     Next Articles

Storyline extraction method from Weibo news based on graph convolutional network

Xujian ZHAO(), Chongwei WANG   

  1. School of Computer Science and Technology,Southwest University of Science and Technology,Mianyang Sichuan 621010,China
  • Received:2021-03-24 Revised:2021-06-03 Accepted:2021-06-03 Online:2021-11-29 Published:2021-11-10
  • Contact: Xujian ZHAO
  • About author:ZHAO Xujian,born in 1984,Ph. D.,associate professor. His research interests include text mining,natural language processing,Web information processing
    WANG Chongwei,born in 1995,M. S. candidate. His research interests include information extraction,machine learning.
  • Supported by:
    the Humanities and Social Sciences Foundation of the Ministry of Education(17YJCZH260);the Key Project of Science and Technology Department of Sichuan Province(2020YFS0057);the CERNET Innovation Project(NGII20180403)


赵旭剑(), 王崇伟   

  1. 西南科技大学 计算机科学与技术学院,四川 绵阳 621010
  • 通讯作者: 赵旭剑
  • 作者简介:赵旭剑(1984—),男,四川绵阳人,副教授,博士,CCF 会员,主要研究方向:文本挖掘、自然语言处理、Web 信息处理
  • 基金资助:


As a key platform for people to acquire and disseminate news events, Weibo hides rich event information. Extracting storylines from Weibo data provides users with an intuitive way to accurately understand event evolution. However, the data sparseness and lack of context make it difficult to extract storylines from Weibo data. Therefore, two consecutive tasks for extracting storylines automatically from Weibo data were introduced: 1) events were modeled by propagation impact of Weibo, and the primary events were extracted; 2) the heterogeneous event graph was built based on the event features, and an Event Graph Convolution Network (E-GCN) model was proposed to improve the learning ability of implicit relations between events, so as to predict story branches of the events and link the events. The proposed method was evaluated from the perspectives of story branch and storyline on real datasets. In story branch generation evaluation, the results show that compared with Bayesian model, Steiner tree and Story forest, the proposed method has the F1 value higher by 28 percentage points, 20 percentage points and 27 percentage points on Dataset1 respectively, and higher by 19 percentage points, 12 percentage points and 22 percentage points on Dataset2 respectively. In storyline extraction evaluation, the results show that compared with Story timeline, Steiner tree and Story forest, the proposed method has the correct edge accuracy higher by 33 percentage points, 23 percentage points and 17 percentage points on Dataset1 respectively, and higher by 12 percentage points, 3 percentage points and 9 percentage points on Dataset2 respectively.

Key words: social network, Weibo, primary event, storyline, Graph Convolutional Network (GCN)



关键词: 社交网络, 微博, 首要事件, 故事线, 图卷积网络

CLC Number: