Utilizing Multidimensional Features to Predict the Dissemination-Force of Emergency Short Videos
Yuqi Yuan, Yang Li, Honglei Lia Sun · 2024
Short video platforms are increasingly critical to information dissemination, especially during emergencies, where high dissemination-force videos rapidly shape online public opinion and exert significant social impact. Predicting the dissemination-force of emergency short videos can enhance public opinion management and improve decision-making foresight. This study aims to develop a predictive system for the dissemination-force of emergency short videos using machine learning and to identify key factors influencing dissemination-force through feature contribution analysis. Consequently, the study quantified dissemination force through metrics such as likes, comments, and retweets, and developed a multidimensional feature system encompassing user features, title features, audio & video (AV) features, AV & title features, and time features. Machine learning algorithms were applied for prediction, with XGBoost identified as the optimal model, achieving 96.39% accuracy, and recall and F1 scores exceeding 95%. Feature contribution analysis highlighted the total_favorited metric as the most significant predictor, followed by posting date and dubbing, with user features being the most important dimension in prediction outcomes. This research enriches the theoretical understanding of short video dissemination-force and offers practical models for effective public opinion management, thereby enhancing control, accuracy, and predictability during emergencies.