Article(id=1289539482269630485, tenantId=1146029695717560320, journalId=1149652312894775300, issueId=1289539472731775707, articleNumber=null, orderNo=null, doi=10.19293/j.cnki.1673-8357.2021.05.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1607270400000, receivedDateStr=2020-12-07, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1785379771058, onlineDateStr=2026-07-30, pubDate=1635436800000, pubDateStr=2021-10-29, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1785379771058, onlineIssueDateStr=2026-07-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1785379771058, creator=13701087609, updateTime=1785379771058, 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=33, endPage=41, ext={EN=ArticleExt(id=1289539482496122902, articleId=1289539482269630485, tenantId=1146029695717560320, journalId=1149652312894775300, language=EN, title=The Public Cognition and Attitude to Artificial Intelligence during the COVID-19 Epidemic: Based on the Text Mining of Weibo Content, columnId=1289539473524499164, journalTitle=Studies on Science Popularization, columnName=Special on the Science Popularization Study of COVID-19, runingTitle=null, highlight=null, articleAbstract=

This article is based on Gsdata.cn’s public opinion monitoring data on the discussion about artificial intelligence (AI) under the COVID-19 epidemic in Weibo. Text mining combined with qualitative analysis were used to analyze the public cognition and attitude to AI. AI in the areas of work resumption, epidemic monitoring, and medical applications received the most attentions. A relatively positive overall attitude was shown, mainly reflected people’s acceptance and recognition of effectiveness, accuracy and safety of AI in fighting against the epidemic. During this period, enterprises and governments are the main sources of information, publishers and objects of attention on AI issues. Discussions focused on issues such as economics, policies, and technology applications. There was relatively less content on AI ethics, governance, risks, and related scientific popularization. Therefore, we propose to encourage multiple subjects to conduct open dialogues, focusing on the issues of AI ethics, governance and risks. Science popularizers can conduct in-depth and systematic scientific dissemination around the public’s attention and personal experience during the epidemic to AI.

, authors=Nan Huang¹, Jun Xiao², Zengyi Zhang¹, authorsList=Nan Huang, Jun Xiao, Zengyi Zhang, 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=1289539482848444439, articleId=1289539482269630485, tenantId=1146029695717560320, journalId=1149652312894775300, language=CN, title=新冠肺炎疫情期间公众对人工智能的认知与态度——基于新浪微博内容的文本挖掘, columnId=1289539473688077022, journalTitle=科普研究, columnName=疫情科普研究专题, runingTitle=null, highlight=null, articleAbstract=

基于清博大数据在新冠肺炎疫情期间的微博舆情监测数据，采用文本挖掘结合质性分析的方法对参与人工智能话题讨论的微博内容进行研究。研究发现，人工智能在复工复产、疫情监测，以及医疗场景下的应用最受公众关注，讨论中整体呈积极正向的态度，主要体现在公众接受和认可人工智能的准确性、有效性和安全性。企业和政府既是人工智能话题的发起者、信息来源，也是受公众关注的对象，重点关注了人工智能与经济、政策、技术应用等层面的问题，而人工智能伦理、治理、风险和相关科学普及方面的内容相对较少。提出应鼓励多元主体展开公开对话，聚焦人工智能伦理、治理与风险问题，科普工作者可以借助公众对人工智能的关注与疫情期间的切身经历，进行深入、系统和有针对性的科普。

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张增一，中国科学院大学人文学院教授，研究方向：科学传播、科技与社会，E-mail：zhzy@ucas.ac.cn。

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张增一，中国科学院大学人文学院教授，研究方向：科学传播、科技与社会，E-mail：zhzy@ucas.ac.cn。

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