摘要
【目的/意义】通过对微博数据的挖掘,了解公众的态度和需求,为新冠疫苗后续的推广或其他疫苗的推广提供一定的参考。【方法/过程】基于内容分析法对564条微博热搜词条进行分类编码,得出公众广泛关注的10大主题。将BTM主题模型和关联规则相结合,进一步对主题包含的热搜话题下的推文和评论进行挖掘,更详细地了解公众的态度和需求。【结果/结论】公众对我国新冠疫苗的态度总体上是积极的,公众最关注的问题为新冠疫苗的不良反应情况、接种禁忌、保护效果。【创新/局限】本研究的创新点在于通过微博平台热搜数据的主题发现研究,分析了公众对新冠疫苗的态度和需求。
【Purpose/significance】Through the mining of Weibo data,we can understand the public’s attitudes and needs,and provide a certain reference for the follow-up promotion of COVID-19 vaccines or the promotion of other vaccines.【Method/process】Based on the content analysis method,564 Weibo hot search terms were coded and classified,and 10 topics of widespread public concern were obtained.The BTM and association rules are combined to further mine the tweets and comments under the hot search terms contained in the topic,in order to understand the public’s attitude and needs in more detail.【Result/conclusion】The public’s attitude towards COVID-19 vaccines of China is generally positive.The public’s most concerned problems are the adverse reactions,vaccination taboos and protective effects of COVID-19 vaccines.【Innovation/limitation】The innovation of this research is to analyze the public’s attitude and demand for COVID-19 vaccines through the topic discovery research of hot search data on the Weibo platform.
作者
吴鹏
诗童
凌晨
WU Peng;SHI Tong;LING Chen(School of Intelligent Manufacturing,Nanjing University of Science and Technology,Nanjing 210094,China;School of Economic and Management,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《情报科学》
CSSCI
北大核心
2022年第7期12-18,26,共8页
Information Science
基金
国家自然科学基金项目“突发事件网民负面情感的模型检测研究”(71774084)
江苏省研究生科研与实践创新计划项目“图文结合的多模态知识库构建研究”(KYCX20_0410)
江苏省博士后基金项目“突发事件中网民情感状态演变规律研究”(2020Z193)。
