
Purpose This study aims to examine how the Chinese climate nongovernmental organization “Chinese Weather Enthusiasts” engaged youth through video strategies. Design/methodology/approach The research proposed a framework grounded in the 5W model and message sensation value (MSV) to analyze the relationship between video content and user interaction. It categorized Bilibili videos into outer and inner features and introduced rhetorical strategies as content elements. A hybrid video coding framework was used, combining machine learning and deep learning (computer vision) for analyzing formal features, while manual coding was used for content features. Findings The results revealed that video length, long shots and the number of scenes positively influenced coins and favorites, whereas personification had a negative impact. In addition, tone and language intensity were positively correlated with user engagement. Originality/value This study offers insights regarding video production for climate communication, broadening the focus from text and images to video content and providing evidence-based guidance for practitioners.
User engagement, Nongovernmental organization, Environmental sciences, Video production, Meteorology. Climatology, Climate change, Communication strategy, GE1-350, QC851-999, Bilibili
User engagement, Nongovernmental organization, Environmental sciences, Video production, Meteorology. Climatology, Climate change, Communication strategy, GE1-350, QC851-999, Bilibili
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
