Topic analysis and optimization strategy of new media posts of laboratory medical journals
[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.
Laboratory medical journalNew mediaTopic of postLatent Dirichlet Allocation modelReadership
NETL
NSTL
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