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GDELT大数据下的COVID-19事件关注度时空演变特征分析

Spatio-temporal Evolution Characteristics of Global Attention toCOVID-19 Supported by GDELT Big Data
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摘要 基于GDELT事件报道,将全球248个国家新闻报道数据作为研究对象,首先建立分类模型评估在合作和冲突事件类型下的网络关注度变化,然后通过空间自相关和空间核密度分析并结合网络化挖掘方法对疫情事件进行时空可视化,揭示了疫情下世界各国网络关注度变化特征。结果表明:本文提出的方法有助于掌握各国新冠肺炎疫情网络关注度的发展状况和趋势,发现全球疫情网络关注度的变化规律和特点,对于判断疫情下的舆情变化具有重要意义。同时完善了以大数据为支撑的国际视角下网络关注度的研究,可为日后的世界舆情事件研究提供借鉴。 The Global Database of Events,Language,and Tone(GDELT)Database collects hundreds of thousands of news media reports around the world and observes world dynamics in real time.Based on GDELT event reports,classification model is established by the assessment under the cooperation and conflict event types of network awareness,making the news report data of 248 countries around the world as the research object,and then it is spatio-temporal visualization of outbreak events through the analysis of the spatial autocorrelation and kernel density combined with network mining method.It reveals the changing characteristics of online attention in countries around the world under the epidemic.The results show that:the method proposed in this paper is helpful to understand the development status and trend of online attention to COVID-19 in various countries,and to discover the changing rules and characteristics of online attention to COVID-19 in the world,which is of great significance to judge the change of public opinion under the epidemic situation.At the same time,the research on network attention from the international perspective supported by big data is improved,and it provides reference for the future research on world public opinion events.
作者 孙长青 SUN Changqing(School of Civil and Surveying Mapping Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China)
出处 《测绘与空间地理信息》 2023年第1期56-59,64,共5页 Geomatics & Spatial Information Technology
基金 省级创新专项资金项目(YC2020-S437)资助。
关键词 GDELT 新冠肺炎疫情 网络关注度 网络化 时空演变 GDELT COVID-19 online attention networking spatio-temporal evolution
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