Multimodal Content Presentation Characteristics and Algorithmic Reconstruction of AIGC-Generated Science Popularization Short Videos: A Case Study of the Douyin Platform
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Keywords
AIGC-generated science popularization short videos, multimodal presentation, algorithmic reconstruction, cross-modal synergy, communication efficiency and depth of understanding
Abstract
Generative artificial intelligence is deeply involved in the production of popular science short videos and promoting the dissemination of scientific knowledge into the platform in a multimodal form. However, AIGC not only improves the efficiency of content production, but also may change the way knowledge is organized and the path of understanding. This study takes the AIGC-generated science popularization short video on the Douyin platform as the object of observation, and analyzes its multimodal expression characteristics from the visual, linguistic, and auditory dimensions. The study found that AIGC popular science short videos have three main characteristics: visual spectacle, linguistic templatization and auditory style differentiation. Under the mutual influence of technical logic, platform logic and attention logic, the expression of scientific knowledge undergoes algorithmic reconstruction. This process enhances the perceptibility of scientific knowledge and accelerates its dissemination, but it also causes scientific authenticity to be aestheticized and poses the risk that deep understanding will be replaced by sensory experience. The optimization method lies in taking the promotion of understanding as the anchor point and re-forming the integrity of knowledge expression in the multimodal joint action.
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Lebing Xu, School of Journalism and Communication, Hebei Normal University, Shijiazhuang 050024, China
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