Article(id=1289539473579033566, tenantId=1146029695717560320, journalId=1149652312894775300, issueId=1289539472731775707, articleNumber=null, orderNo=null, doi=10.19293/j.cnki.1673-8357.2021.05.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1624291200000, receivedDateStr=2021-06-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1785379768986, onlineDateStr=2026-07-30, pubDate=1635436800000, pubDateStr=2021-10-29, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1785379768986, onlineIssueDateStr=2026-07-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1785379768985, creator=13701087609, updateTime=1785379768985, updator=13701087609, issue=Issue{id=1289539472731775707, tenantId=1146029695717560320, journalId=1149652312894775300, year='2021', volume='16', issue='5', pageStart='5', pageEnd='98', issueExtLink='null', onlineDate='null', pubDate='1635436800000', pubDateStr='2021-10-29', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1785379768784, creator='13701087609', updateTime=1785380839568, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1289543963996701432, tenantId=1146029695717560320, journalId=1149652312894775300, issueId=1289539472731775707, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1289543963996701433, tenantId=1146029695717560320, journalId=1149652312894775300, issueId=1289539472731775707, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=23, endPage=32, ext={EN=ArticleExt(id=1289539474233344991, articleId=1289539473579033566, tenantId=1146029695717560320, journalId=1149652312894775300, language=EN, title=Content Analysis on Themes of COVID-19 Epidemic in Popular Science Journals and Its Optimazation: Take Typical Popular Science Journals as Examples, columnId=1289539473524499164, journalTitle=Studies on Science Popularization, columnName=Special on the Science Popularization Study of COVID-19, runingTitle=null, highlight=null, articleAbstract=

In order to understand the topic selection scheme of domestic popular science journals in response to covid-19 pandemic, establish the pattern of selected topics during the pandemic, and provide some references, this paper uses the computational language method LDA topic model to compare the topic categories and contents of typical popular science journals on COVID-19 from January 2020 to January 2021, and then analyses the characteristics as well as shortcomings of the topic settings. This paper suggests that popular science journals need to enhance the innovation and depth of thematic content, systematically develop science thematic strategies based on the life cycle of an epidemic, increase the proportion of science news themes to promote the timeliness of science.

, authors=Meihuan Han¹, Jin Cao², Bin Xu³, authorsList=Meihuan Han, Jin Cao, Bin Xu, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1289539474522751968, articleId=1289539473579033566, tenantId=1146029695717560320, journalId=1149652312894775300, language=CN, title=科普期刊新冠肺炎疫情主题内容分析及优化路径探索——以典型科普期刊为对象, columnId=1289539473688077022, journalTitle=科普研究, columnName=疫情科普研究专题, runingTitle=null, highlight=null, articleAbstract=

为了解国内科普期刊应对新冠肺炎疫情的选题策划情况，厘清疫情周期中的选题规律，对科普期刊未来的相关主题策划提供借鉴，本文采用计算语言方法LDA主题模型，比较了国内典型综合性科普期刊2020年1月至2021年1月一年内的主题类别及关键内容，并分析了主题设置的特点以及不足。对科普期刊未来类似主题的策划提出优化建议：增强主题的创新性和内容的深度；基于事件生命周期，系统制定科普主题策略；增加科学新闻主题比重，促进科普时效性。

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许斌，清华大学计算机科学与技术系副教授，研究方向：自然语言处理、知识图谱，E-mail：xubin@tsinghua.edu.cn。

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许斌，清华大学计算机科学与技术系副教授，研究方向：自然语言处理、知识图谱，E-mail：xubin@tsinghua.edu.cn。

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