摘要
[目的/意义]通过梳理近十年我国图书情报学领域网络舆情研究方法应用情况,辅助研究人员准确认识和选择方法,为后续研究提供参考借鉴。[方法/过程]本文使用文献计量、聚类分析、网络分析等多种方式从“时间-方法”“主题-方法”“作者-方法”“方法-方法”4个维度展开分析;接着,总结了各研究方法的适用场景、应用条件和应用效果;最后,对方法使用过程中的问题作简要评述,并预测了未来舆情研究的发展趋势。[结果/结论]研究表明,网络舆情研究方法从以定性分析方法为主到以知识发现方法、仿真建模方法等智能方法为主;舆情传播演化、监测预警、治理引导三大研究主题各有其适用方法;高频作者主要使用人工智能、网络分析、仿真建模3类方法;研究中知识发现方法最常与其他方法共同使用;方法应用过程在数据收集、数据分析、结论应用三方面仍存在不足。
[Purpose/Significance]Combing the application of online public opinion research methods in the field of domestic library and information science in the last decade can assist researchers in accurately understanding and selecting methods,and provide reference for subsequent studies.[Method/Process]The paper used bibliometric method,cluster analysis,network analysis and other methods to carry out analysis from four dimensions of“time-method”“subject-method”“author-method”and“method-method”.Then,application scenarios,application conditions and application effects of each research method were summarized.Finally,the paper briefly reviewed some problems in the application of the method,and forecasted the development trend of public opinion research in the future.[Result/Conclusion]The results show that the research methods are gradually changed from qualitative analysis to intelligent methods;The three major research topics have their own applicable methods;High-frequency authors mainly use artificial intelligence,network analysis and simulation modeling;The knowledge discovery method is most often used together with other methods in the current research;The application process of the method still has shortcomings in data collection,data analysis and conclusion application.
作者
黄茜茜
杨建林
Huang Xixi;Yang Jianlin(School of Information Management,Nanjing University,Nanjing 210023,China;Jiangsu Key Laboratory of Data Engineering and Knowledge Service,Nanjing 210023,China)
出处
《现代情报》
CSSCI
2022年第7期167-177,共11页
Journal of Modern Information
关键词
网络舆情
研究方法
图书情报学领域
主题分析
聚类分析
共现分析
online public opinion
research methods
library and information science
topic analysis
cluster analysis
co-occurrence analysis