ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (12): 2490-2500.doi: 10.7544/issn1000-1239.2020.20200725

Special Issue: 2020人机混合增强智能的典型应用专题

Previous Articles     Next Articles

Learning to Rank for Evolution Trend Evaluation of Online Public Opinion Events

Qin Tao1,2, Shen Zhuang1,2, Liu Huan1,2, Chen Zhouguo3   

  1. 1(Key Laboratory for Intelligent Networks and Network Security (Xi’an Jiaotong University), Ministry of Education, Xi’an 710049);2(School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049);3(30th Research Institute of China Electronics Technology Group Corporation, Chengdu 610093)
  • Online:2020-12-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2016YFE0206700), the Innovative Research Group of the National Natural Science Foundation of China (61721002), the Innovation Research Team of Ministry of Education (IRT_17R86), the National Natural Science Foundation of China (61772411), the Natural Science Foundation of Shaanxi Province (2020JQ-646), and the Fundamental Research Funds for the Central Universities (xjh012019026).

Abstract: Public opinion events in social networks have a bearing on social harmony and stability. Analyzing the evolution trend of events so as to manage and control them is able to reduce the impact of malignant online public opinion. However, the lack of labelled data and the limited relevant resources makes the effective management of online public opinion challenging and complicated. To solve those difficulties, we propose a learning-to-rank algorithm for the events evolution trend evaluation, which makes full use of the expert knowledge in the labelled data and the correlation between labelled and unlabelled data to select important public opinion for management, in turn, improves the management efficiency. Firstly, based on the experiences and demands of public opinion management, we design a measurable, accessible and meaningful hierarchical index system, which is derived from the three most important factors of events, for evolution trend evaluation. Secondly, we build an evaluation model for evolution trend evaluation based on the graph convolutional network. Specifically, our model uses the local sensitive Hash algorithm to mine the structural information from the data node’s neighborhood and generates the mixed feature of the data node and its neighbor. Finally, we design different loss functions for the labelled and unlabelled data respectively, in order to realize the comprehensive utilization of the expert knowledge in the labelled data and the spatial structure information in the unlabelled data. We verify the efficiency of the proposed model on public datasets MQ 2007-semi and MQ 2008-semi. We also build a real-world public opinion event dataset to verify the practicability and generalization of the proposed algorithm. The experimental results show that the proposed model can evaluate the public opinion event evolution trend with limited expert knowledge, and provide decision support for public opinion event management with limited resources.

Key words: online public opinion management and control, evolution trend evaluation, limited labelled data, learning to rank, graph convolutional network

CLC Number: