Beyond virtual boundaries: the intersection of metaverse technologies, tourism, and lifelong learning in China’s digital discourse

The metaverse, originally coined by Neal Stephenson (1992), has become a transformative concept for education and tourism as extended reality (XR) technologies such as AR and VR converge and mature, a shift accelerated by COVID-19. In lifelong learning, the metaverse offers immersive, interactive, and experiential environments that can enhance cognitive development and support student-centered, constructivist pedagogies. In tourism education, it enables realistic simulations of complex scenarios, providing practical, context-rich experiences that bridge theory and practice. However, adoption faces challenges related to infrastructure, connectivity, relevance, and perceived usefulness. The extended Technology Acceptance Model (TAM) provides a useful lens to understand motivations and attitudes toward such technologies. The metaverse also holds potential for inclusion across dimensions such as social care, gender, and disability, and shows promise in domains like nursing education through immersive VR. This study examines the interplay among social, educational, and tourism technologies to identify determinants of adoption and integration, and to contribute to discourse on digital transformation in China’s education and tourism sectors. It explores public perceptions, technological advancements, and policy needs, using Weibo discourse as a lens for understanding the metaverse’s role in lifelong learning and tourism education. Research question: Assess the social and technological concerns of metaverse tourism in the public view and its educational implications to develop public discourse that responds to these concerns.

The metaverse and lifelong learning: The metaverse—reframed from science fiction into a composite of AR/VR, blockchain, and related digital infrastructures—extends lifelong learning beyond traditional institutions toward personalized, experiential, scenario-based activities. Active, social, exploratory learning aligns well with virtual environments, with studies showing VR/AR can boost engagement and outcomes while underscoring the need for digital literacy. Persistent issues include equity and access to required technologies. Metaverse in tourism education: In tourism education, the metaverse affords interdisciplinary, realistic simulations of tourist environments, enabling pragmatic experiences difficult to stage in classrooms and fostering cultural heritage learning and language practice. Immersive technologies can close the theory–practice gap and prepare students for evolving demands in marketing and management. Barriers include high-performance computing requirements, cybersecurity, potential distraction, and dependence on instructional design quality and educator capacity to integrate technologies. Analytical frameworks in social media discourse: Semantic Network Analysis (SNA) models keywords as nodes and associations as links to reveal thematic clusters and relational importance, using centrality metrics (degree, closeness, betweenness, eigenvector) to identify focal and bridging concepts. Comparative discourse analysis situates Weibo-derived dynamics within broader global contexts, highlighting how public dialog evolves with digital advancements. Public sentiment and cultural-technological context: Benefits discussed include accessibility (e.g., for disabilities or remote users), immersive experiences, and environmental advantages, while concerns center on privacy/security, surveillance, and potential addiction. Chinese cultural factors (collectivism, guanxi), rapid mobile adoption, and platform-specific behaviors shape Weibo discourse. Addressing privacy and ethical issues within China’s context is essential to drive inclusive, culturally sensitive educational tourism experiences. Research gaps: Prior literature seldom centers public perceptions of the metaverse in lifelong learning within China’s digital milieu, nor does it sufficiently guide policy and practice for educators and policymakers. This study addresses these gaps by analyzing Weibo discourse to elucidate social and ethical issues relevant to integrating emergent technologies into tourism and education.

Design: Mixed-method computational content analysis combining Semantic Network Analysis (SNA) and automated sentiment analysis of Weibo posts about metaverse tourism and education. Data source and period: Weibo posts collected over 1269 days (January 20, 2020–June 23, 2023), spanning pre-, during, and post-key developments in metaverse technologies and their integration into tourism/education. Keywords and collection: Four Chinese search terms were used: “元宇宙” (metaverse), “元宇宙旅游” (metaverse tourism), “元宇宙平台” (metaverse platform), and a related term for events/environments. Data were collected using SocialSensor, an automated web crawler capable of handling large datasets and filtering non-original content; only original posts were retained to avoid redundancy. Preprocessing: Text normalized to lowercase; URLs, stopwords, punctuation, special characters, user mentions, emojis, and celebrity-related filler were removed; synonyms were merged (e.g., tourism/sightseeing/visiting), traditional characters converted to simplified; duplicates removed; irrelevant tech terms and unspecified words eliminated. Tokenization and lemmatization performed using jieba. Words with frequency above corpus mean were retained. An additional pass reported retention of tokens with frequency >22.37, yielding 24,202 valid tokens for analysis. Semantic network construction: Co-occurrence matrices were built using a five-word sliding window (inspired by cognitive chunking limits). Undirected, weighted graphs were generated where edge weights equal co-occurrence counts. Networks were visualized and analyzed in Gephi. Analyses included modularity detection (semantic clusters), network density, and centrality metrics (Degree and Eigenvector centrality primarily; also degree/betweenness noted as key indicators). For visualization, the top 100 words by frequency/degree were included; edge thickness represented co-occurrence strength and node size reflected degree. Sentiment analysis: Conducted with TextMind (Chinese Academy of Sciences) to classify posts as positive/negative/neutral; additional modeling referenced StructBERT-based sentiment classification trained on multiple Chinese datasets (bdc, dianping, jd, wanjia, waimai-10k), with scores normalized for overall sentiment estimation. A decision threshold of 0.9 was applied for polarity separation. Outputs and metrics: The full corpus produced 9,053,734 word-pair associations. In the focused subgraph (top 100 nodes by degree), density ≈0.99 indicating high interconnectedness; nodes exhibited an average degree ≈421. Key nodes and eigenvector centralities were tabulated; modularity identified five thematic clusters: technology-related, conceptual, market and product, digital assets, and culture and development.

- Semantic network structure: The metaverse (元宇宙) is the most central node (Degree 20,334; Eigenvector 0.064), linked strongly with ‘world’ (世界; Degree 13,117), ‘learning’ (学习; 12,897), ‘digital’ (数字; 12,141), ‘China’ (中国; 11,993), ‘sci-tech/technology/development’ (科技/技术/开发), and ‘tourism’ (旅游). Overall, 9,053,734 word-pair co-occurrences were observed; in the top-100-node subgraph, network density ≈0.99 and average node degree ≈421 indicate a highly interconnected discourse. - Cluster themes (modularity): Five robust clusters emerged: (1) Technology-related (VR, AR, blockchain, platforms, applications, innovation); (2) Conceptual (virtual vs. physical, virtual worlds, authenticity); (3) Market and product (brands, companies, video/music, global, VR shopping); (4) Digital assets (digital economy, NFTs, artists, collections); (5) Culture and development (industry, development, culture/tradition, tourism, nation). - Representative associations: High-frequency links included reality–world, virtual–virtual world, Weibo–music, video–music, brand–global, digital–economy, industry–development, tradition–culture, tourism–culture. - Example cases referenced within clusters: Educational AR by Nreal/Xreal; Palace Museum virtual tours; Alibaba’s VR shopping; Baidu’s Xuperchain for NFTs; Tencent’s digital heritage restoration. These illustrate application domains relevant to education/tourism rather than being direct quantitative outcomes. - Sentiment distribution: 56.78% positive (n=81,311) vs. 43.21% negative (n=61,871) posts, reflecting a balanced yet polarized public stance. Positive sentiments emphasize excitement about immersive learning, accessibility, innovation, and cultural exploration; negative sentiments focus on data privacy, security, digital addiction, and ethical concerns. - Policy-relevant insights: The centrality of technology terms underscores infrastructure and capability as key drivers; conceptual and cultural clusters highlight the need for public education on benefits/risks and culturally sensitive design; digital assets raise IP and monetization considerations; market cluster signals opportunities for branding and content creation in educational tourism.

The study’s findings address the research question by empirically mapping how social (ethical, cultural) and technological concerns about metaverse tourism are articulated on Weibo and by quantifying their salience and interconnections. The strong centrality of technology-related terms indicates that infrastructure, VR/AR capability, and platform readiness are pivotal to adoption in educational tourism. Concurrently, the presence of culture/humanity and conceptual clusters shows that users weigh authenticity, cultural value, and humanistic outcomes, aligning with lifelong learning aims. The near-balanced sentiment split (≈57% positive vs. ≈43% negative) reveals a nuanced public stance: enthusiasm for immersive, accessible, and innovative learning experiences is tempered by significant concerns about privacy, security, addiction, and ethics. This duality suggests that successful integration into tourism education requires a balanced, risk-aware approach encompassing robust data governance, ethical standards, and digital wellbeing. In China’s context—marked by rapid digitization, collectivist norms, and strong educational aspirations—the discourse points toward opportunities for cultural competence building and global citizenship through virtual cultural heritage and simulated tourism experiences. The results substantiate theoretical implications for experiential learning in virtual settings, digital pedagogy, and culturally responsive curriculum design. Practically, the findings support policies to enhance digital infrastructure, promote digital literacy, establish ethical content and privacy standards, incentivize industry–education collaborations, and invest in R&D for metaverse applications. Collectively, these measures can help convert public enthusiasm into sustainable educational value while addressing articulated risks.

This study analyzes Weibo discourse using semantic network and sentiment analyses to understand how the metaverse intersects with tourism education and lifelong learning in China. Results show a highly interconnected discussion centered on technology, concept, market/product, digital assets, and culture/development themes, alongside a polarized but slightly positive overall sentiment. The metaverse is positioned as a promising platform for immersive, experiential learning that can enhance tourism education, foster cultural understanding, and support lifelong learning goals. However, public concerns—privacy, security, addiction, and ethics—underscore the need for cautious, equitable, and ethical integration. Key contributions include: (1) empirical mapping of discourse structures and sentiments on metaverse tourism; (2) identification of thematic clusters guiding educational, cultural, and economic implications; and (3) policy-oriented insights for infrastructure, literacy, ethics, collaboration, and R&D. Future research could incorporate multi-platform and cross-cultural comparisons, triangulate social media insights with interviews/field experiments, and evaluate learning outcomes from implemented metaverse curricula to strengthen causal inferences and practical guidance.