基于多模态特征的短视频热度预测研究OA

[Objective]Short videos have become a crucial medium for online public opinion dissemina-tion,making accurate popularity prediction vital for content moderation and public sentiment analysis.However,existing studies exhibit limitations in feature extraction and temporal model-ing:First,the unidimensional feature analysis fails to fully leverage multimodal data sources.Second,conventional linear approaches prove inadequate in characterizing the nonlinear popu-larity dynamics of short videos,particularly the distinctive"cold-start-explosion-decay"lifecy-cle patterns.To address these gaps,this study proposes a multimodal feature-based approach for short video popularity prediction.[Methods]First,a multidimensional feature system is constructed,encompassing user influence,author influence,audiovisual quality and content fea-tures,comment features,and interaction features.Second,the Random Forest model is employed for nonlinear modeling to capture complex feature interactions and improve the ability to predict video heat.[Results]Experi-mental results demonstrate the superior performance of the proposed method in short video popularity predic-tion tasks,achieving an F1-score of 69.3%,representing a 13.7 percentage point improvement over the baseline model.The AUC value reaches 71.3%,showing a 16 percentage point enhancement compared to the baseline.[Conclusions]The multimodal feature-based approach significantly improves prediction accuracy,offering a ro-bust technical solution for online public opinion analysis and content governance..