摘要
为了深入探究网络舆情演化的特点与规律,需要在内容层面上有效地从网络舆情的大量数据中发现高价值的舆情主题,并结合时间维度将各个时间点孤立的舆情内容整合起来。文章将情报学理论、生命周期理论、舆情传播理论、社会网络分析方法与文本分析方法相结合,提出了基于社区发现与关键节点识别的包括网络舆情生命周期阶段划分、网络舆情社区发现与关键节点识别、舆情事件主题发现三个部分的网络舆情主题发现研究设计。最后,选取新浪微博“上海踩踏”事件作为研究对象进行实证分析。研究发现:在主题发现研究中加入用户属性和用户行为等非文本特征要素的设计,弥补了用户关系的缺失,提高主题发现的效率;提出的主题发现方法在一定程度上降低了微博文本稀疏性的影响;研究发现了舆情事件在整个生命周期中主题内容变化的状态,所提方案可为相关决策提供有效的方法支撑,研究结论具有情报参考价值。
In order to explore the characteristics and laws of the evolution of network public opinion,it is necessary to effectively identify high-value public opinion topics from a large number of data of network public opinion at the content level,and integrate the public opinion content isolated at different time points with the time dimension.This paper combines information science theory,life cycle theory,public opinion communication theory,social network analysis method and text analysis method,proposes the research design based on community detection and key node identification.Finally,the paper takes the"Shanghai stampede"incident of Sina Weibo as the research object for empirical analysis.The results show that:the addition of non-text feature elements such as user attributes and user behavior to the topic discovery makes up for the lack of user relationship and improves the efficiency of topic discovery;the proposed topic discovery method reduces the impact of sparsity of micro-blog text;the research finds out the changing state of the subject content of public opinion events in the whole life cycle.The proposed research design can provide effective methodological support for relevant decision-making,and the research conclusion has information reference value.
作者
王曰芬
王一山
杨洁
Wang Yuefen;Wang Yishan;Yang Jie
出处
《图书与情报》
CSSCI
北大核心
2020年第5期48-58,共11页
Library & Information
基金
国家社会科学基金重点项目“大数据环境下社会舆情与决策支持方法体系研究”(项目编号:14AZD084)
江苏高校哲学社会科学重点研究基地“社会计算与舆情分析”(培育点)研究成果之一。
关键词
网络舆情
主题发现
社区发现
关键节点识别
非文本特征
network public opinion
topic discovery
community detection
key node identification
Non-textual features
