摘要
【目的】揭示突发公共卫生事件各阶段微博话题与用户各种行为之间的相关关系。【方法】使用基于Relevance公式改进的LDA话题模型提取微博话题,计算话题分别与用户转发数、评论数、点赞数以及两两之间的标准化差的余弦相似度,分析不同话题间和同一话题下的行为规律。【结果】在突发公共卫生事件中,用户转发、评论、点赞这三种行为的演化趋势大致相同,转发数与评论数、评论数与点赞数、转发数与点赞数之间均存在显著的相关关系,相关系数分别为0.390、0.274、0.180,与事件进展、政府回应和知识普及等主题相关的微博更倾向于被评论,而与群众意见和事件措施等主题相关的微博则更倾向于被转发。【局限】由于仅以"山东问题疫苗事件"和新浪微博作为研究案例和数据来源,研究结论仍需其他案例和数据源的验证。【结论】用户行为有明显的倾向性,对不同类型与同一类型的话题会产生不同的行为选择。
[Objective] This paper aims to reveal the relationship between topics of microblog and user behaviors at different stages of public health emergencies.[Methods] We analyzed the behavioral patterns among different topics and within a specific topic.The LDA topic model improved by the relevance formula was employed to extract the topics of microblog entries on public health emergencies.The cosine distances between microblog topics and the numbers of retweets,comments,favorites,as well as those between each pair of behavior counts,were calculated to explore users' behavior patterns towards the same or different topics.[Results] During public health emergencies,the evolutionary trends of users' behaviors of retweets,comments,favorites are roughly similar.Significant correlations exist between the counts of three behaviors.The correlation coefficients between the counts of retweets and comments,those of comments and favorites,and those of retweets and favorites are 0.390,0.274,0.180 respectively.Microblogs related to the topics of event progress,government responses and knowledge dissemination are more likely to be commented on,while those related to the topics of public opinions and event measures are more likely to be retweeted.[Limitations] The universality of the conclusion is subject to the examination of other cases.[Conclusions] The tendency of user behaviors towards different types of topics is obviously unequal,which means different behaviors may happen among different topics and within a specific topic.
作者
安璐
梁艳平
An Lu;Liang Yanping(Center for Studies of Information Resources,Wuhan University,Wuhan 430072,China;School of Information Management,Wuhan University,Wuhan 430072,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2019年第4期33-41,共9页
Data Analysis and Knowledge Discovery
基金
教育部哲学社会科学研究重大课题攻关项目"提高反恐怖主义情报信息工作能力对策研究"(项目编号:17JZD034)
国家自然科学基金青年项目"突发公共卫生事件社交媒体信息主题演化与影响力建模"(项目编号:71603189)
国家自然科学基金重大课题"国家安全大数据综合信息集成与分析方法"(项目编号:71790612)的研究成果之一