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The emergence of artificial intelligence-generated content (AIGC) technology has markedly enhanced the capabilities of digital human content generation and natural language processing, thus further advancing the development of digital humans. To enable enterprises and governments to effectively address the challenges and opportunities arising from the rapid development of digital humans, it is imperative to understand the public opinion and discussion dynamics of digital humans.

This study initially analyzed the trends and distribution patterns of public attention to digital humans. By utilizing word cloud technology, we explored the primary focal points of public interest and conducted a topic analysis using latent Dirichlet allocation (LDA) techniques. Subsequently, content analysis was conducted on the popular application domains of digital humans. Finally, this study examined the influence of user characteristics on emotional scores toward digital humans and the presence of differences in focus across user groups.

The results indicate a sustained increase in public attention toward digital humans, accompanied by notable geographic disparities in the distribution of discussions. Discussions on Weibo are primarily focused on four domains, whereas areas within the digital human application domain that provoke widespread discussion include live streaming, service, cultural entertainment and digital avatars. Significant impacts of user characteristics on sentiment scores were observed, revealing divergent focal points of interest among different user groups toward digital humans.

Through the deep analysis of Weibo data, this study offers new insights into the digital human industry, enabling governments and businesses to understand industry trends and develop targeted digital human customization strategies based on customer characteristics.

Shifting to OLSL classes during the pandemic can bring learners ambivalent experiences: negative, positive or both appraisals toward the technologies. However, few studies have examined how ambivalent experiences can influence students' learning behaviors, specifically cyberslacking and active participation. Using the challenge-hindrance stressor framework, this study investigates the impact of challenge and hindrance appraisals on these learning behaviors.

This study uses a mixed methods approach to answer research questions. An interview was conducted to identify the key components of ambivalent appraisals, and a survey was conducted to empirically examine the impact of challenge and hindrance appraisals on learners' behaviors in online live streaming learning (OLSL) contexts. The data of 675 university students were analyzed using structural equation modeling.

This study found that hindrance appraisal leads to cyberslacking while challenge appraisal leads to active participation, but it can also cause cyberslacking. Social presence has a double-edged effect, acting as both a facilitator and inhibitor, strengthening the effect of hindrance appraisal on cyberslacking and the impact of challenge appraisal on active participation.

Prior studies have primarily focused on the negative side (techno-distress) of technology appraisals. This study simultaneously examines the positive side, techno-eustress, on learners' behaviors in OLSL contexts, and explores the moderating effects of social presence. This study contributes to the technostress and technology adaptation literature by revealing how technology-induced ambivalent appraisals impact behavioral responses. It offers important theoretical and practical implications for education tool designers.