Article(id=1315372108784452425, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1315372022658613552, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1736006400000, receivedDateStr=2025-01-05, revisedDate=1742745600000, revisedDateStr=2025-03-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1791538749166, onlineDateStr=2026-10-09, pubDate=1773504000000, pubDateStr=2026-03-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791538749166, onlineIssueDateStr=2026-10-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791538749166, creator=13701087609, updateTime=1791538749166, updator=13701087609, issue=Issue{id=1315372022658613552, tenantId=1146029695717560320, journalId=1308397381743640680, year='2026', volume='44', issue='3', pageStart='449', pageEnd='672', issueExtLink='null', onlineDate='null', pubDate='1773504000000', pubDateStr='2026-03-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791538728624, creator='13701087609', updateTime=1791538856865, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1315372560565523080, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1315372022658613552, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1315372560569717385, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1315372022658613552, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=661, endPage=672, ext={EN=ArticleExt(id=1315372110709637962, articleId=1315372108784452425, tenantId=1146029695717560320, journalId=1308397381743640680, language=EN, title=Analysis of the construction of the Metaverse "user base" from the agenda setting perspective: Research based on deep learning text analysis, columnId=1309226375089840648, journalTitle=Studies in Science of Science, columnName=Frontiers and Opinions, runingTitle=null, highlight=null, articleAbstract=

Based on the agenda setting theory and technology acceptance model, this research utilizes deep learning text analysis method to explore the state, causes and effective road to construct metaverse public cognition from the two levels of information transmission and reception. By analyzing the metaverse news and user’s comments data from 2021-2023, this research reveals the meta-cosmic agenda communication is a game involving multiple agents, and the secondary communication of information by opinion leaders dominates the formation of public emotional cognition. The content of the media agenda does not coincide with the focus of public comment and contrary to the news, the sentiment of user comments is negative, which is due to the interaction of multiple external variables such as policy culture, which affect the perception of technological usefulness and ease of use. This paper analyzes the “user element” of meta-universe from multiple angles and at a deep level, which provides reference for the new quality productivity forces integration of meta-universe innovation and development, meta-universe policy formulation, product innovation and content publicity.

, authors=Xu-sen CHENG, Xiao-ling ZHANG, Tong XUE, Si-yu LEI, Bo YANG, authorsList=Xu-sen CHENG, Xiao-ling ZHANG, Tong XUE, Si-yu LEI, Bo YANG, authorCompany=null, correspAuthors=Bo YANG, 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=1315372115419841376, articleId=1315372108784452425, tenantId=1146029695717560320, journalId=1308397381743640680, language=CN, title=议程设置视角下元宇宙“用户基础”建设研究——基于深度学习文本分析的研究, columnId=1309226375303750154, journalTitle=科学学研究, columnName=前沿与观点, runingTitle=null, highlight=null, articleAbstract=

基于议程设置理论和技术接受模型,利用深度学习文本分析方法,分析2021-2023年元宇宙新闻报道和用户评论数据,从信息传播和接收两个层面探究元宇宙公众认知的现状、成因和有效建设链路。研究发现:元宇宙议程传播是多主体参与的博弈,意见领袖对信息的二次传播主导着公众情感认知的形成;内容上媒体议程与公众评论话题聚焦点并不一致;情感上二者相悖,用户评论呈负面消极情绪,原因在于政策文化等多外部变量互相作用,影响了技术有用性和易用性的感知。研究多角度、深层次地对元宇宙用户要素进行分析,为新质生产力融合元宇宙创新发展、元宇宙政策制定、产品创新及内容宣传提供参考启示。

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程絮森(1984-),男,教授、博士生导师。

张晓玲(1999-),女,硕士研究生。

薛童(2000-),女,硕士研究生。

雷思羽(2004-),女,本科生。

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杨波(1968-),男,教授、博士生导师, E-mail: 。
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AI ethical risk perception, trust and public participation[J]. 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tableContent=null), ArticleFig(id=1315372121593856912, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=EN, label=Figure 5, caption=LDA Consistency test result for news report texts, figureFileSmall=XVXN+zvok/Y6IyrR2DavCw==, figureFileBig=SOpB7OYQa/j/gLZfi9YOFg==, tableContent=null), ArticleFig(id=1315372121669354385, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=CN, label=图5, caption=新闻报道文本LDA一致性检验结果, figureFileSmall=XVXN+zvok/Y6IyrR2DavCw==, figureFileBig=SOpB7OYQa/j/gLZfi9YOFg==, tableContent=null), ArticleFig(id=1315372121736463250, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=EN, label=Figure 6, caption=Topic extraction results, figureFileSmall=Q6N9s0c0kf09VjahxWxXPw==, figureFileBig=W/0I+IOUaQAQQzNn03sE0w==, tableContent=null), ArticleFig(id=1315372121807766419, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=CN, label=图6, caption=主题提取结果

注:图中每个圆圈表示一个主题,圆圈大小表示该主题在所有文档中出现的频率,原输出为交互图(可点击显示每个圆圈对应的主题和关键词),此处仅对整体的话题分布结果作静态展示,在交互中可操作如中间图所示的展开流程,对某一簇话题进行展开查看,可以进一步判断话题的重叠度,从而确定话题数,如中间图展开后,可将众多话题划归两个高阶话题。

, figureFileSmall=Q6N9s0c0kf09VjahxWxXPw==, figureFileBig=W/0I+IOUaQAQQzNn03sE0w==, tableContent=null), ArticleFig(id=1315372121879069588, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=EN, label=Figure 7, caption=Topic evolution results across datasets, figureFileSmall=IvbEFwSWA1n5xCWTz0Nscg==, figureFileBig=rhFdUj15FDYfYkXP9eNBnQ==, tableContent=null), ArticleFig(id=1315372121941984149, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=CN, label=图7, caption=各数据集话题演化结果

注:上图中三个数据集单独分析,各图中每条曲线代表一个话题,但不同图中相同颜色曲线并不对应同一话题。对上图不同数据集演化结果中,主要话题曲线的具体含义和内容将在后文详细解释。

, figureFileSmall=IvbEFwSWA1n5xCWTz0Nscg==, figureFileBig=rhFdUj15FDYfYkXP9eNBnQ==, tableContent=null), ArticleFig(id=1315372122004898710, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=EN, label=Figure 8, caption=Comparison of sentiment analysis results across different text categories, figureFileSmall=7pd0uL1lbXDQHfoXd4lm+w==, figureFileBig=7tvRJoo8y7ST3Qat4L+GvQ==, tableContent=null), ArticleFig(id=1315372122067813271, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=CN, label=图8, caption=各类文本情感分析结果对比, figureFileSmall=7pd0uL1lbXDQHfoXd4lm+w==, figureFileBig=7tvRJoo8y7ST3Qat4L+GvQ==, tableContent=null), ArticleFig(id=1315372122139116440, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=EN, label=Figure 9, caption=Technology acceptance model, figureFileSmall=8y5EEr0z1VW8FZwbNlQ6cA==, figureFileBig=TG661UNIOiqB+sUS5r83+w==, tableContent=null), ArticleFig(id=1315372123766506393, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=CN, label=图9, caption=技术接受模型, figureFileSmall=8y5EEr0z1VW8FZwbNlQ6cA==, figureFileBig=TG661UNIOiqB+sUS5r83+w==, tableContent=null), ArticleFig(id=1315372123850392474, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=EN, label=Figure 10, caption=Summary of research results, figureFileSmall=7K8jBSGzY3Vbdicis5hMdw==, figureFileBig=xdRZAKJPqG1AxyyidLV4HQ==, tableContent=null), ArticleFig(id=1315372123946861467, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=CN, label=图10, caption=研究结论一览图, figureFileSmall=7K8jBSGzY3Vbdicis5hMdw==, figureFileBig=xdRZAKJPqG1AxyyidLV4HQ==, tableContent=null), ArticleFig(id=1315372124018164636, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=EN, label=Table 1, caption=

Thematic classification framework for metaverse texts

, figureFileSmall=null, figureFileBig=null, tableContent=
话题名称 话题关键词
技术支持 区块链、VR、脑机接口、画面渲染、去中心化、人工智能
应用场景 游戏、广告、个性化推荐、虚拟人、运动会、博物馆
发展风险 骗局、虚假、沉迷、圈钱、非法、防范、消保、盗笔、性侵
企业新闻 meta、裁员、微软、扎克伯格
资本市场 个股、基金、行情、比特币、加密货币、指数、数字藏品、nft、下跌
官方平台 产业、发展、长三角、创新、四川、上海、新区、两会
产业前景 超导、服务业、材料、文化产业
概念起源 文学作品、电影、斯蒂芬森、小说、平行宇宙、雪崩
学术聚焦 复旦大学、北京大学、学者、教授、会议、讲座
愿景探讨 人类、记忆、虚拟、社会、虚拟世界、虚拟现实
), ArticleFig(id=1315372124097856413, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315372108784452425, language=CN, label=表1, caption=

元宇宙文本主题分类表

, figureFileSmall=null, figureFileBig=null, tableContent=
话题名称 话题关键词
技术支持 区块链、VR、脑机接口、画面渲染、去中心化、人工智能
应用场景 游戏、广告、个性化推荐、虚拟人、运动会、博物馆
发展风险 骗局、虚假、沉迷、圈钱、非法、防范、消保、盗笔、性侵
企业新闻 meta、裁员、微软、扎克伯格
资本市场 个股、基金、行情、比特币、加密货币、指数、数字藏品、nft、下跌
官方平台 产业、发展、长三角、创新、四川、上海、新区、两会
产业前景 超导、服务业、材料、文化产业
概念起源 文学作品、电影、斯蒂芬森、小说、平行宇宙、雪崩
学术聚焦 复旦大学、北京大学、学者、教授、会议、讲座
愿景探讨 人类、记忆、虚拟、社会、虚拟世界、虚拟现实
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议程设置视角下元宇宙“用户基础”建设研究——基于深度学习文本分析的研究
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程絮森 , 张晓玲 , 薛童 , 雷思羽 , 杨波
科学学研究 | 前沿与观点 2026,44(3): 661-672
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科学学研究 |前沿与观点 2026 , 44 (3) : 661 -672
议程设置视角下元宇宙“用户基础”建设研究——基于深度学习文本分析的研究
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程絮森, 张晓玲, 薛童, 雷思羽, 杨波
作者信息
  • 中国人民大学信息学院, 北京 100872
通讯作者:
杨波(1968-),男,教授、博士生导师, E-mail: 。
作者简介:

程絮森(1984-),男,教授、博士生导师。

张晓玲(1999-),女,硕士研究生。

薛童(2000-),女,硕士研究生。

雷思羽(2004-),女,本科生。

Analysis of the construction of the Metaverse "user base" from the agenda setting perspective: Research based on deep learning text analysis
Xu-sen CHENG, Xiao-ling ZHANG, Tong XUE, Si-yu LEI, Bo YANG
Affiliations
  • School of Information, Renmin University of China, Beijing 100872, China
出版时间: 2026-03-15
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基于议程设置理论和技术接受模型,利用深度学习文本分析方法,分析2021-2023年元宇宙新闻报道和用户评论数据,从信息传播和接收两个层面探究元宇宙公众认知的现状、成因和有效建设链路。研究发现:元宇宙议程传播是多主体参与的博弈,意见领袖对信息的二次传播主导着公众情感认知的形成;内容上媒体议程与公众评论话题聚焦点并不一致;情感上二者相悖,用户评论呈负面消极情绪,原因在于政策文化等多外部变量互相作用,影响了技术有用性和易用性的感知。研究多角度、深层次地对元宇宙用户要素进行分析,为新质生产力融合元宇宙创新发展、元宇宙政策制定、产品创新及内容宣传提供参考启示。

新质生产力  /  元宇宙  /  人智交互  /  公众认知  /  议程设置

Based on the agenda setting theory and technology acceptance model, this research utilizes deep learning text analysis method to explore the state, causes and effective road to construct metaverse public cognition from the two levels of information transmission and reception. By analyzing the metaverse news and user’s comments data from 2021-2023, this research reveals the meta-cosmic agenda communication is a game involving multiple agents, and the secondary communication of information by opinion leaders dominates the formation of public emotional cognition. The content of the media agenda does not coincide with the focus of public comment and contrary to the news, the sentiment of user comments is negative, which is due to the interaction of multiple external variables such as policy culture, which affect the perception of technological usefulness and ease of use. This paper analyzes the “user element” of meta-universe from multiple angles and at a deep level, which provides reference for the new quality productivity forces integration of meta-universe innovation and development, meta-universe policy formulation, product innovation and content publicity.

new quality productivity forces  /  Metaverse  /  technological innovation  /  public cognition  /  agenda setting
程絮森, 张晓玲, 薛童, 雷思羽, 杨波. 议程设置视角下元宇宙“用户基础”建设研究——基于深度学习文本分析的研究. 科学学研究, 2026 , 44 (3) : 661 -672 .
Xu-sen CHENG, Xiao-ling ZHANG, Tong XUE, Si-yu LEI, Bo YANG. Analysis of the construction of the Metaverse "user base" from the agenda setting perspective: Research based on deep learning text analysis[J]. Studies in Science of Science, 2026 , 44 (3) : 661 -672 .
党的二十大报告提出,要“推动战略性新兴产业融合集群发展,构建新一代信息技术、人工智能等一批新的增长引擎”。“十四五”规划进一步明确,要推动数字技术创新和新业态发展,并把“虚拟现实和增强现实”列为“数字经济重点产业”。作为新一代信息技术集成创新和应用的未来产业,元宇宙是数字经济发展的重要增长引擎。基于数字技术搭建的元宇宙,是一种虚实融合且人能够以数字身份参与的三元世界数字社会[1],其市场涵盖了数字经济的各个领域。据Statista统计数据,预计到2030年,中国元宇宙相关产业市场规模将达871.5亿美元,产业增长态势迅猛,将持续拉动数字经济增长。
宏观来看(如图1),政策战略、经济效益、社会需求赋予了元宇宙建设的持续动力,而发展则需要底层技术和上层内容生态的协同构建[2]。建立良好的元宇宙内容生态,在其中推动数字经济进一步发展,是各领域面对的数字化转型新挑战,而这一过程依赖于良好的用户参与和互动。如在市场营销领域,元宇宙虚拟世界中的人际互动和社会分享等机制极大地影响着消费者决策和企业营销效果[3]。总而言之,用户是元宇宙内容生态建设和参与的主体,是元宇宙中一切活动的核心[4],用户体验和用户内容创作更是是衡量元宇宙发展水平的关键指标[2],而用户参与是所有上述活动的前提。因此,本文聚焦于探究“元宇宙‘用户参与’这一基础前提的发展现状和建设链路”。
现有元宇宙研究多集中在宏观的概念探讨、场景应用及发展模式与风险分析,学者们普遍认同元宇宙生态构建和用户参与的重要性,并强调元宇宙技术科普过程中要加强情感传播,通过“共情”加强公众与科学家平等对话的重要性[5]。但现有研究少有以用户为主要研究对象,对其关于元宇宙的认知规律和情感进行系统梳理并探析成因的深入研究。从用户角度来看,一方面是政府与企业的积极推动,另一方面又是难辨真假的媒体宣传。公众能否在信息泡沫中形成理智客观的判断,接受元宇宙并参与建设,对元宇宙信息生态系统构建及后续数字产业体系发展具有重要影响。鉴于媒体报道是塑造用户认知和情感的重要路径,也是用户接触元宇宙信息的主要方式,本文基于议程设置理论,从信息传播和接收两方面,采用深度学习方法对元宇宙内容宣传和公众认知规律进行全面归纳,结合技术接受模型,深入探析公众认知成因,为元宇宙媒体信息宣传、政府政策制定及企业产品创新提供实践启示,并为新质生产力与元宇宙建设的融合创新发展提供理论参考。
元宇宙概念由科幻作品中演化而来,自2021年起引起社会各界广泛关注,它依托数字孪生等技术生成的现实世界镜像,并通过数字化身份表达连接现实和虚拟世界,融合形成一个综合性的数字社会[1]。元宇宙的构建依赖于多种底层技术的支持,由虚实孪生、虚实相生和虚实融生三个阶段构成[6]。虚实孪生需要基于物联网、云计算及人工智能等综合技术实现对现实世界的复刻[7],借助VR、3D渲染等技术完成用户身份建模、社会计算等,使用户以数字分身进入虚拟世界,并依托脑机接口(BCI)等物联网设备实现人机交互及信息互动[8]。技术进步是实现元宇宙繁荣的必要条件,而元宇宙的最终价值体现在终端应用及内容的用户体验[2]。元宇宙发展是重构商业形态和生活方式的全新风口,能够实现产业形态和组织形式的创新,带动多领域商业发展和生活方式的升级[9]。与此同时,元宇宙潜在的新型治理问题和法律挑战不容忽视。如伦理风险[10],经济发展风险[11]等。
综合来看,学者们以底层技术为基础,开展了元宇宙赋能各行各业的发展链路分析,但少有研究充分关注到“用户基础”这一发展前提。部分研究者通过分析不同社交平台的评论,对公众认知展开讨论,但多分析视角单一且仅停留在对舆论现象的解释,缺乏深入的原因探析。本文将基于议程设置理论,从媒体信息传播和公众信息接收两方面对公众认知现状进行解读,并结合公众心理认知规律,对其成因进行探析,从而提出切实有效的发展建议。
“新质生产力是由未来产业和战略性新兴产业所催生的具有高效能、高质量的生产力”,其代表了一种新型、高质量生产力的跃升[12]。习近平总书记在对新质生产力的科学内涵进行界定时曾强调,新质生产力是由技术革命性突破、生产要素创新性配置、产业深度转型升级催生。元宇宙以集大成的数字技术为驱动力,构建虚实相融的新型社会形态,其发展全面涵盖新质生产力构建的三项重要催生要素。推动元宇宙和新质生产力的融合发展,一方面有助于以元宇宙为助力提供新质生产力的多元发展方向,另一方面有助于新质生产力强化元宇宙发展新动能[12]。
新质生产力的发展需要新型生产关系的适应,元宇宙通过虚拟协作等为个人和企业提供了多样化的生产方式和更广阔的生产平台,使其能以内容创作、提供服务等方式参与到元宇宙的经济活动中。其中参与者的角色被重塑,生产者和消费者之间的角色边界被打破,新的协作与竞争模式也为全球经济发展带来了新的可能性。
媒体传播是影响公众认知最直接和高频的方式,是其他要素影响公众认知的作用路径。媒体报道通过议程设置,影响公众对议题的感知和反应[13],如在金融领域,媒体的情绪会显著影响分析师对盈利预测的乐观偏差度[14],对于新技术的传播亦是同理。新媒体时代,议程设置是媒体、公民、政府等多主体动态博弈的过程。社交媒体使得公众能够更直接地参与到议程设置的过程中,通过分享、评论等互动形式影响议题的可见性和重要性[15],过程中“意见领袖”的中介作用也不断强化,他们会首先从媒体获取信息,然后影响周围人的观点和行为[16]。
综上,在元宇宙这一新技术领域,媒体的议题设置和情绪表达极大影响着公众的认知和进一步的行为,而公众通过社交媒体的表达又反过来影响着媒体的报道,过程中还涉及其他社会主体(如政府、企业)参与的信息传播的博弈。因此,本文基于议程设置博弈的理论,从信息的传播和接收两个层面,对元宇宙的媒体报道和公众社交媒体表达进行分析。不同于传统的问卷访谈的方法,本文基于大数据全面挖掘公众认知规律并进一步深入探析成因。
技术的创新和应用逐渐成为推动社会进步的重要力量,然而技术的成功推广不仅取决于其自身的创新性和功能性,还受到公众接受程度和使用意愿的影响[17]。公众对新兴技术的接受度不仅会影响技术应用的成效,也会改变技术发展的路径,甚至限制技术的创新、应用与发展[18]。技术接受模型探讨了诸多外部因素对用户信念、态度及意向的影响,并指出影响用户对技术接受程度的两个关键因素:感知有用性和感知易用性[19]。以技术接受模型为基础,研究者们基于不同的应用背景进行改进和扩展,进一步构建出UTAUT[20]等模型。如今,技术接受模型已被广泛应用于多个领域[21,22],且不同领域和文化背景下,有用性和易用性感知对技术的接受度有着不同的影响。本文将借用技术接受模型的视角,对归纳得出的公众认知规律进行深入的原因探析。
如图2所示,为了探究元宇宙发展的“用户基础”现状,本文基于议程设置理论和技术接受模型,从元宇宙内容宣传和公众情感认知两个角度展开分析。内容宣传方面,科技新闻报道作为公众了解新技术概念、接受新领域知识的关键路径,深刻影响着社会舆论导向;公众情感认知方面,社交平台作为普通互联网用户进行表达的主要载体,其中的评论内容对探究公众对某一话题的认知具有重要价值,也是除传统的问卷、采访等手段外,借助大数据分析方法收集用户评价和看法的关键手段,其研究结果更加客观真实。
本文将通过文本分析实验分别探究如下三个问题:媒体议程中元宇宙的主题是什么,情感如何?公众议程中元宇宙的主题是什么,情感如何?二者是否一致,若不一致,原因是什么?本文将先基于深度学习文本分析回答前两个问题,而后基于议程设置理论解释公众认知现状,基于技术接受模型探析成因。
图3展示了对媒体报道和公众评论进行主题演化和情感分析的方法设计。
对六大网站进行检索,采集元宇宙新闻文本,这些网站基本覆盖大部分主流媒体和自媒体报道,数据全面,可信度高;通过两大社交媒体平台收集用户评论文本数据,其中知乎更偏向以精英群体为代表的问答社区,热衷于知识分享,B站则是国内最大的年轻人文化社区,互动性强[23],这两大平台于公众而言更具知识科普价值,因而其中的讨论也更具研究价值。
在具体的文本分析方法上,LDA模型作为传统有效的方法被广泛应用于多个领域[24,25]。考虑到元宇宙作为新兴技术领域,其发展给用户能够体验的时间并不长,虽然产生了较多的文本评论数据,但对相关话题提取的超参数设计还缺乏相关专家经验,因此本文采用BertTopic[26]模型首先进行文本主题建模。BERTopic是一种完全基于BERT预训练模型的文本聚类工具,借助深度学习模型,它通过对大量文本数据进行训练,学习丰富的语言特征和语义信息,有较强的语言理解能力。同时考虑到元宇宙“新”的特征,有必要进行人工干预以优化模型应用,所以本文结合模型在数据集上的运行效果,针对性地对原始BERTopic模型的聚类环节进行了优化设计,对文本进行主题提取。主题提取之后,本文对评论文本进一步做了情感分析,从内容和情感两方面,更全面地挖掘用户心智认知规律,探究其影响因素与作用机制。
本文基于BERTopic进行动态主题建模。如图4模型处理流程所示。原始的BERTopic采用HDBScan算法,它可以有效地处理高维数据,并且能够识别不同密度的聚类,但本文通过实验发现,其聚类出的主题仍旧较为分散,主题重合度较高,有必要进行人工干预实现更好的聚类效果。因此,本文在HDBScan聚类结果的基础上,结合LDA一致性检验,对话题数进行约束,再次采用KMeans进行聚类,而后再进行人工整合,逐层凝练文档主题,得到最终聚类结果;聚类完成后,BERTopic会通过构建词语词频矩阵,而后借助矩阵分解技术,对矩阵进行分解,从而得到每个词的潜在主题分布,进一步构建文档的主题分布,从而得到每个文档的的主题标签。
基于上述步骤完成文档的主题提取后,可对文档的主题进行动态建模,只需要先为每个主题生成一个全局表示,然后为每个时间段生成一个局部表示。全局表示的构建采用了基于类的TF-IDF (c-TF-IDF)方法,其计算如公式(1)所示:
${W}_{t,c}=t{f}_{t,c}\times log(1+\frac{A}{t{f}_{t}})$
其中,tft,c为单词t在类别c中出现的频率,tft为单词t在所有类别中出现的频率;A为每个类的平均字数。进一步地,实现主题的局部表示,只需将其修改tft,c为时间段i时段文档的词频即可。获取每条文本的主题后,使用与主题相关的文本的归一化频率作为度量,结合时间戳,计算每个时间片(天)的主题热度,通过可视化工具直观地分析其演化规律。
本文以“元宇宙”为关键词在六大网站进行检索,通过爬虫对新闻的发布时间、标题、正文内容进行爬取,去除异常值和重复数据后,共收集从2021年11月至2023年12月的4290篇新闻报道。对用户评论数据,本文在知乎“元宇宙(Metaverse)”话题下265个“精华”讨论中,依据时间分布和讨论热度等特征选取了具有代表性的8个问题,分别爬取其对应的评论内容,处理后共收集评论文本数据5700条;在哔哩哔哩以“元宇宙”为关键词检索视频内容,依据视频与元宇宙的相关性、发布时间、讨论热度等,选取了10个具有代表性的视频,分别爬取其评论内容并进行处理,共获取评论数据6137条。对所有文本数据进行清洗等预处理工作。
对所有预处理后的文档(新闻报道及评论),本文采用优化后的“paraphrase-multilingual-MiniLM -L12-v2”句向量模型对进行嵌入表示。在聚类环节,原HDBScan的优势在于无需人工提前指定主题数,但从其聚类的效果来看,其簇分离程度较低,话题重复度较高,且产生了较多无关话题。尽管如此,其聚类结果为本文人工干预确定最佳主题数提供了重要指导,所以参考HDBScan聚类结果,同时结合LDA模型对不同主题数下主题一致性的计算(图5),本文加以人工干预,对三个数据集分别确立最佳主题数,优化BERTopic在本文研究场景下的应用,采用KMeans模型再次进行聚类,最终结果如图6所示(从左至右依次为新闻文本、哔哩哔哩评论、知乎评论的主题提取结果)。
可以看到,各数据集簇间的区分均已比较明显,但观察结果仍旧存在重复话题,为了更好地表达文本规律,本文对算法提取出的主题进一步进行人工整合,最终主题提取结果如表1所示。
完成算法对全局的主题提取后,结合时间戳数据,本文进一步对人工合并前,算法分析出的主题在不同时间段内的表现进行了动态展示,旨在分析不同数据集中话题的数量、分布、波动等规律,结果如图7所示。
从图中可以看出,各类文本中均存在部分随时间的推移并无明显变化的主题,查看具体内容发现这些话题包含大量的媒体名称、地名等和元宇宙舆论并无直接关系的信息,不具分析价值,因而本文在人工整合时对这类话题做了删减。
如图所示的演化规律,相比于新闻报道,评论文本的话题变化更加集中。不同于新闻文本主题的持续波动,评论文本的主题热度可能一段时间沉寂,而后突然短时爆发,对应于公众在某些特殊时间点如政策发布、重大事件发生等,被外部事件触发集中的讨论,其讨论内容多由媒体发布的信息而来,然而也可以发现,公众关注焦点的波动和媒体报道重点的波动并不总是一致的,这说明媒体的议程设置并不总是奏效的,媒体议程推动了公众议程的形成,但后者的走向并不受前者的限制,甚至会反向作用于前者。
进一步地,结合具体内容对各数据集主要话题曲线进行分析,本文发现新闻文本所涉及的话题类别更多,涵盖了表1归纳出的全部话题,而两个平台的评论文本则集中在新闻话题的子话题集中,各自的具体侧重又有所不同。对于表1中总结出的全部话题,知乎评论更多集中在对发展风险、企业新闻、官方平台、概念起源的探讨;哔哩哔哩依托音视频多模态及场景化的表达,能够更形象地展示一些科幻内容,所以其评论更多聚焦在应用场景、技术支持、发展愿景、发展风险以及资本市场的探讨。其中,两个社交平台对发展风险的探讨均占据了过半的比重。
为了明确各类文本的情感倾向,本文调用了百度情感分析API进行文本情感分析,百度情感分析模型基于自建中文数据库进行训练,其对于中文文本的情感判断效果表现更佳。
图8展示了各类文本的情感分析结果,可以看到,新闻文本中绝大多数为积极和中立的情感,但两类评论文本中消极情感文本均占了绝大多数。仔细查看评论内容,诸如“骗局”“技术达不到”“概念骗钱”等言论高频出现,对于元宇宙的发展前景,公众的评论内容整体持较为消极的态度。
元宇宙作为科技传播的一个重要事例,其内容传播的过程充分体现了在新媒体的复杂环境下议程设置中的博弈变化。
第一,媒体作为公众了解元宇宙讯息的主要途径,其议程极大地影响着公众元宇宙议程,如在元宇宙概念初期,媒体对元宇宙发展前景的展望使得公众对元宇宙情绪高涨,元宇宙在资本市场获得了极高的热度;对元宇宙性侵等风险案例的报道,密集地引发了公众对元宇宙发展的伦理、道德等风险的思考和担忧。第二,媒体虽是主要路径,但其议程信息并不全部触达公众,意见领袖的二次传播发挥着关键作用。从情感分析结果可以看到,媒体对元宇宙内容的报道多为积极正向的,对相关负向风险等内容仅限于客观提及,并未突出强调,但经由二次传播形成的公共讨论中,公众议程却更多地关注在这些媒体并未突出报道的方面,形成了整体偏负向消极的情感倾向。
第三,公众议程或高热度的公众讨论反向议程设置影响着媒体报道的内容选择。从主题演化结果中可以看到,部分话题在新闻报道中的热度上升较评论具有一定的滞后,如对资本市场泡沫、新式消费骗局等发展风险的讨论,一定程度上公众议程对媒体议程起了推动作用。深入观察,可以发现,反向议程设置多发生在负向内容话题,公众对于此类内容感知更强烈、讨论热度更高、情绪也更高涨,因而引发媒体更多的关注和跟进报道。
第四,元宇宙内容传播是一个多主体参与、动态变化的博弈过程。政府、企业及各类组织机构,经由媒体进行政策宣传、信息发布等,媒体结合自身价值观和利益追求进行信息加工和传播;公众意见整合各类信息形成个人议程,在社交平台进行传播;其他公众则对上述所有信息结合自身经验以及熟人社交进行整合,参与公众讨论,最终形成公众议程。而后,政府、企业、媒体、意见领袖等各参与主体依据公众议程的动态,实时调整自己的信息传播内容及策略。各主体之间同样存在互相影响的作用机制,如企业及时根据政府政策和市场动态调整业务内容和信息传播策略;政府依据业界发展及时跟进政策制定、环境搭建及技术落地应用等。因而元宇宙内容生产和传播实际是一个多主体参与的动态博弈过程。
元宇宙作为一项新兴的科学技术概念,其公众认知的成因可借助技术接受模型进行分析。
技术接受模型指出,用户面对新技术时,有用性和易用性的感知是对技术接受与否的两个主要决定因素。元宇宙概念发展至今,以元宇宙为标签的各类虚拟场景体验都给用户带来了“使用感知”,其中,有用性强调用户对元宇宙带来工作及生活效率提升的感知价值,易用性指用户对元宇宙进入及使用的简便性的感知水平。外部变量方面,系统特性如系统界面设计等、公众的自我效能感知、组织与文化环境如政策激励等、社会影响如意见领袖的引导等都会影响公众对技术的有用性和易用性感知,进而影响其态度和行为[27,28]。
从感知有用性上讲,元宇宙概念兴起两年以来,各类标榜为元宇宙应用的虚拟体验,仍旧停留在对少数场景娱乐化体验的改善,和公众的日常生活接触甚少,距离关乎公众切身利益的生活、工作等场景仍旧遥远,降低了公众的有用性感知;从感知易用性角度看,元宇宙概念的宣传中反复提到各类高深的底层技术和硬件支持,如VR设备、区块链、云计算等,而反观这些技术和设备的发展现状,远达不到能够支撑元宇宙打造流畅体验的程度,技术发展的较大差距使得用户对于进入元宇宙的成本和技术门槛认知过高,易用性感知降低,同时对相关技术(如人工智能等)的伦理、法律等风险感知,也深刻影响着用户的参与[29]。
感知的有用性和易用性由多个外部变量互相作用影响,有的变量正向促进二者,有的则负向削弱,同一变量的作用效果在互相作用的情境下可能有所不同。比如政策激励原本可正向促进感知有用性和易用性,但在发展风险、技术门槛等负向变量的综合作用下,政策激励可能适得其反地降低感知的有用性和易用性,即一些负面的信息的出现,如资本市场泡沫、新式诈骗、性侵等违法犯罪行为,在引起公众关注、谈论,进而形成负面认知后,再进行的正向宣传可能反过来加剧公众的质疑。例如,在知乎“上海提出到 2025 年「元宇宙」相关产业规模达 3500 亿元,将带动哪些相关产业发展?”这一话题下,评论未见政策激励下本应有的正向憧憬,反而充斥着“割韭菜”“泡沫”“非常遥远”等消极内容。
本文基于议程设置理论和技术接受模型,利用深度学习方法,通过分析元宇宙媒体新闻报道和社交平台用户评论文本,从信息传播和接收两个层面探究了元宇宙公众认知的现状和成因。基于研究结论(图10),本文给出了元宇宙建设的理论研究新方向和应用实践的新参考。
第一,赋能新质生产力发展融合元宇宙建设的理论研究。本文全面梳理了元宇宙的建设链路和各发展要素间的关系。以生成式人工智能(AIGC)为代表的新科技突破,既是元宇宙的建设引擎,也是新质生产力的发展动力。在元宇宙底层技术生态不断繁荣的基础上,充分把握用户认知规律,有效引导用户参与内容生态构建,创新生产要素配置,推动产业转型升级,既是元宇宙价值的根本所在,也是新质生产力发展的动力来源,并不断推动生产关系的革新。因而元宇宙建设与新质生产力发展是“你中有我,我中有你”的“共同体”,在进行理论研究时应把握好二者之间的关联关系进行融合创新,以新质生产力发展为“风向标”指导元宇宙建设,以元宇宙建设为“设计图”解读新质生产力发展。
第二,弥补了元宇宙用户研究的理论空缺。本文多角度、深层次地探究了元宇宙“用户基础”的发展现状和成因,并对元宇宙信息传播链路及多主体间博弈演化关系进行了梳理分析。鉴于元宇宙“用户参与”的重要性,本文基于议程设置理论,从信息传播和接收两个角度,采用深度学习文本分析的方法对大数据进行规律挖掘,全面客观地对元宇宙公众认知现状进行了刻画和解读,并对信息传播过程中多主体间的博弈关系进行了分析,而后基于技术接受模型,深层剖析了公众认知现状的成因。
第三,以元宇宙生态建设驱动数字经济创新性发展。元宇宙生态建设能够重构商业形态和生活方式,数实融合推动产业形态和组织形式的转型升级,是数字经济创新性发展的重要实现路径。本文为从元宇宙生态建设的视角,进行数字经济创新性发展的研究提供了思路参考:应当充分把握用户认知规律,从公众切身利益相关的场景入手,引导企业及组织完成从数字化到数智化的转型升级,融入元宇宙生态建设,并引导用户参与共建,以此逐步打造和完善新的经济形态和运行机制。
紧抓用户认知的“木桶效应”,建立健全虚拟产业监管及风险防范机制。感知有用性和感知易用性是影响用户接受程度的两个核心要素,二者又受到多个外部变量间的相互作用,研究表明当负向变量引发公众不满或怀疑时,正向促进要素可能适得其反,因而应当紧抓用户认知的“木桶效应”,首要突出解决短板要素。相比于一味地正向宣传,政府、企业和媒体首先应合力解决用户的顾虑和担忧。
政府可积极推进相关法律法规和政策制度的完善,为元宇宙等虚拟产业的监管、新型违法犯罪的防范提供参考依据;企业在创新产品设计时,应当同步推进对产品应用过程风险的防范举措,依法依规落实好平台的监管责任,保障好用户的权益;媒体在报道和宣传中,着重解决公众对技术发展的负向感知,跟进新技术,塑造公众信心,加大对元宇宙监管、安全、风险防范等内容的报道力度,呼吁引导公众建立风险防范意识,增强公众对元宇宙发展的信任。
以生活场景为切入,融合新质生产力相关产业建设推动元宇宙落地研究。在元宇宙的落地方面,应当首先建设与公众生活息息相关的场景。同时,在进行元宇宙学术研究和企业产品创新的过程中,应当充分结合新质生产力产业建设方向,如以智慧医疗、教育、智慧城市建设等场景为切入,加强政策引导,加快推动算力等基础设施建设,政企共建推动元宇宙应用落地。
把握用户心智,建立有效的元宇宙宣传链路。在元宇宙宣传中,各类文学、影视及企业的科技“大V”具有强大的影响力和号召力,政府、企业及媒体等应当重视并充分发挥社交媒体平台的宣传影响力,邀请各界“大V”合作,通过多种形式对元宇宙的理论概念、政策制度、行业动态、技术进展等进行解读,客观真实地呈现元宇宙建设进展的同时,突出强调安全监管,合理引导愿景想象,通过设置信息传播的不同重点,分阶段合理分配用户对不用话题的重要性感知,逐步提升用户对元宇宙的心理认可。
本文从整体视角探究了公众对元宇宙的认知现状及成因,尚未进行更细化的公众分析和原因探究。因而未来工作有两个优化方向,一是通过结合问卷调查和访谈等手段,对人群进行分层(年龄、地域、经济状况、学历水平等)研究,从而针对不同类别的人群提出更有针对性的信息传播建议,更好地提升公众科技素养;二是对于公众认知的成因,对于技术接受模型的多个外部变量间的相互影响机制,可进行深入探究,从而明确元宇宙各个话题相关要素,如政策激励、发展风险、应用场景等是如何互相作用改变用户的有用性和易用性感知,从而更好地提升公众对元宇宙的接受度,推动元宇宙上层内容生态建设。
  • 国家自然科学基金重点项目(72434006)
  • 国家自然科学基金面上项目(72271236)
  • 教育部哲学社会科学研究重大课题攻关项目(23JZD022)
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2026年第44卷第3期
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  • 接收时间:2025-01-05
  • 首发时间:2026-10-09
  • 出版时间:2026-03-15
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  • 收稿日期:2025-01-05
  • 修回日期:2025-03-24
基金
国家自然科学基金重点项目(72434006)
国家自然科学基金面上项目(72271236)
教育部哲学社会科学研究重大课题攻关项目(23JZD022)
作者信息
    中国人民大学信息学院, 北京 100872

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杨波(1968-),男,教授、博士生导师, E-mail: 。
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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