摘要
【目的】从舆论热点话题与信息传播模式等视角理解自然灾害期间微博平台的舆情特征与演变模式。【方法】采用机器学习方法对微博有效数据进行提取,采用深度学习方法对微博文本进行聚类分析,采用复杂网络分析方法对微博信息传播模式进行研究。【结果】微博分类器准确率达到0.82,有效提取出灾害相关数据;文本聚类结果能较好地体现出舆情热点演变情况;发现信息传播网络的“幂律性”和“时间不变性”,信息传播网络结构稀疏,社团规模稳定扩张但分布模式不变;灾害期间用户之间的交互加强,不同地区用户对信息来源的偏好有所差异。【局限】缺乏对不同社交平台数据的实验验证和分析比较。【结论】所提研究方法和结果可以为灾害期间的舆情发现和管理提供一定启发。
[Objective]This study reveals patterns and evolution of public opinion on Weibo during natural disasters from the perspectives of trending topics and information dissemination.[Methods]We proposed a machine learning approach to extract the valid data of natural disasters from Weibo.Then,we employed a deep learning model to cluster these textual posts.Finally,we investigated the information dissemination patterns with complex network analysis.[Results]The accuracy of our extractor for valid disaster information reached 0.82.The clusters of textual posts indicated the changes of trending topics.The structure of information dissemination during disasters was sparse.The sizes of online communities expanded constantly while their distribution unchanged.Users in different regions had different preferences for information sources.[Limitations]We did not conduct experiment to examine data from different social platforms.[Conclusions]The proposed method could effectively identify public opinion events during natural disasters.
作者
马莹雪
赵吉昌
Ma Yingxue;Zhao Jichang(School of Economics and Management,Beihang University,Beijing 100191,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2021年第6期66-79,共14页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金面上项目(项目编号:71871006)的研究成果之一。
关键词
社交媒体
话题发现
社交网络
舆情管理
信息传播
Social Media
Topic Mining
Social Network
Public Opinion
Management
Information Dissemination
