摘要
【目的】分析检验医学期刊新媒体高阅读量推文主题,以揭示用户关注的热点和偏好,指导制定推文主题优化策略,旨在提升检验医学期刊新媒体传播力和影响力。【方法】以“检验医学”微信公众号(以下简称“检验医学”)为例,采用Python编程筛选出在2018年1月1日至2023年5月31日投放的阅读量排名前100的推文,基于隐含狄利克雷分布模型对100篇推文主题识别与分析,进而提出针对性的检验医学期刊新媒体推文主题优化策略。【结果】“检验医学”高阅读量推文主题为“新冠疫情防控核酸检测政策文件”主题、“医疗行业行为规范”主题、“职业资格考试和继续教育”主题。依据主题识别和分析结果提出多声部复调式(系列化子主题开发、新视角主题演绎及案例主题集萃)推文主题优化策略,以拉伸推文主题的叙事张力,从文本述说到述说文本,从而提升检验医学期刊新媒体推文主题内容的深度与广度。【结论】识别和分析“检验医学”高阅读量推文主题,有助于制定更精准的推文主题优化策略,为检验医学期刊新媒体高阅读量推文未来主题的研究创新与空间拓展提供依据。
[Purposes]This paper analyzes the topics of the highly read posts in new media of laboratory medical journals,to reveal the hotspots and preferences of users,and guide the formulation of post topic optimization strategy,aiming at improving the dissemination power and influence of new media of laboratory medical journals.[Methods]Taking the WeChat official account of"Laboratory Medicine"(referred to as"Laboratory Medicine")as an example,we used Python programming to screen out the top 100 read posts published on its platform from January 1,2018 to May 31,2023.Based on the Latent Dirichlet Allocation model,we identified the topics of the 100 posts and analyzed them,and then proposed a corresponding post topic optimization strategy for new media of laboratory medical journals.[Findings]The topics of the highly read posts of"Laboratory Medicine"are"Policy document on nucleic acid detection for prevention and control of COVID-19","Code of conduct in the medical industry",and"Professional qualification examination and continuing education".Based on the topic identification and analysis,the topic optimization strategy of multi-voice polyphony(serialized sub-topic development,topic deduction from new perspectives,and case topic collection)is proposed.This strategy is to stretch the narrative tension of the topic of posts,from text telling to retelling text,to enhance the depth and breadth of the topic of new media posts of laboratory medicine journals.[Conclusions]By identifying and analyzing the research topic of the highly read posts on"Laboratory Medicine",it is helpful to formulate more accurate optimization strategies for the topic of posts and provides a basis for the research innovation and spatial expansion of the future topic of the highly read posts of new media of laboratory medical journals.
作者
周丽
方琪
何金龙
曾蕴林
卜梦婵
陈丞
伍胤志
张耀元
ZHOU Li;FANG Qi;HE Jinlong;ZENG Yunlin;BU Mengchan;CHEN Cheng;WU Yinzhi;ZHANG Yaoyuan(Guangxi Normal University,1 Yanzhong Road,Yanshan District,Guilin 541006,China;Chongqing Health Statistics Information Center,420 Baohuan Road,Yubei District,Chongqing 401120,China;Baidu.com Times Technology(Beijing)Co.,Ltd.,10 Shangdi 10th Street,Haidian District,Beijing 100089,China;Yangtze Normal University,16 Juxian Avenue,Fuling District,Chongqing 408100,China)
出处
《中国科技期刊研究》
北大核心
2024年第7期932-940,共9页
Chinese Journal of Scientific and Technical Periodicals
基金
中国科学技术期刊编辑学会2023-2024年度基金项目“科技期刊文章利用微信公众号进行二次内容转化促进其传播的模式研究”(CESSP-2023-C21)
关键词
检验医学期刊
新媒体
推文主题
隐含狄利克雷分布模型
阅读量
Laboratory medical journal
New media
Topic of post
Latent Dirichlet Allocation model
Readership
