ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (12): 2490-2500.doi: 10.7544/issn1000-1239.2020.20200725

所属专题: 2020人机混合增强智能的典型应用专题

• 人工智能 • 上一篇    下一篇

基于排序学习的网络舆情演化趋势评估方法研究

秦涛1,2,沈壮1,2,刘欢1,2,陈周国3   

  1. 1(智能网络与网络安全教育部重点实验室(西安交通大学) 西安 710049);2(西安交通大学电子与信息工程学部 西安 710049);3(中国电子科技集团第三十研究所 成都 610093) (qin.tao@mail.xjtu.edu.cn)
  • 出版日期: 2020-12-01
  • 基金资助: 
    国家重点研发计划项目(2016YFE0206700);国家自然科学基金创新研究群体(61721002);教育部创新团队(IRT_17R86);国家自然科学基金项目(61772411);陕西省自然科学基金项目(2020JQ-646);中央高校基本科研业务费专项资金(xjh012019026)

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).

摘要: 社交网络中的舆情事件关乎社会的和谐与稳定,分析事件的演化趋势并进行管控能够有效降低恶性舆情事件的影响.但是,高效的舆情管控却面临标注数据少、管控资源有限的难题,采用人机混合增强技术,充分利用少量标注样本中的专家知识,是建立舆情演化态势评估模型的可行思路之一.据此,提出一种基于排序学习的舆情事件演化趋势重要性评估算法,在模型训练过程中,充分利用标注数据中的专家知识以及有标签数据和无标签数据的关联关系,筛选重要舆情事件进行管控,提升管控资源利用效能.首先,结合舆情管控经验和需求,从“人”“事”“势”等三要素出发,构建易获取、可量化、有含义的舆情事件演化态势评估指标体系;其次,基于图卷积神经网络构建舆情演化趋势评估模型,利用局部敏感Hash算法挖掘数据的空间结构信息,并利用图卷积求取数据及其邻域的混合特征;最后,针对有标签数据和无标签数据设计相应的损失函数,实现标注数据中专家知识和无标注数据中空间结构信息的综合利用.在公开数据集MQ2007-semi和MQ2008-semi上验证了算法的有效性,在自主构建的舆情数据集上验证了算法的实用性和泛化性.实验结果表明,所提算法可以根据少量的专家知识或标注数据,实现网络舆情事件演化态势的评估,为资源有限条件下的舆情事件管控提供决策支撑.

关键词: 舆情事件管控, 演化趋势评估, 标签数据缺失, 排序学习, 图卷积网络

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

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