Abstract
This work aims to predict the popularity of short videos using the videos themselves and their related features. Popularity is measured by four key engagement metrics: view count, like count, comment count, and share count. This study employs video classification models with different architectures and training methods as backbone networks to extract video modality features. Meanwhile, the cleaned video captions are incorporated into a carefully designed prompt framework, along with the video, as input for video-to-text generation models, which generate detailed text-based video content understanding. These texts are then encoded into vectors using a pre-trained BERT model. Based on the six sets of vectors mentioned above, a neural network is trained for each of the four prediction metrics. Moreover, the study conducts data mining and feature engineering based on the video and tabular data, constructing practical features such as the total frequency of hashtag appearances, the total frequency of mention appearances, video duration, frame count, frame rate, and total time online. Multiple machine learning models are trained, and the most stable model, XGBoost, is selected. Finally, the predictions from the neural network and XGBoost models are averaged to obtain the final result.
Abstract (translated)
这项工作旨在通过短视频本身及其相关特征来预测短视频的受欢迎程度。受欢迎程度通过四个关键参与指标进行衡量：观看次数、点赞数、评论数和分享数。本研究采用不同架构和训练方法的视频分类模型作为骨干网络，以提取视频模态特征。同时，对清理过的视频字幕在精心设计的提示框架中与视频一起输入视频到文本生成模型，该模型生成详细的基于文本的视频内容理解。随后使用预训练的BERT模型将这些文本编码成向量。根据上述六组向量，为四个预测指标中的每一个训练一个神经网络。 此外，本研究还对视频和表格数据进行了数据分析和特征工程，构建了诸如标签总出现频率、提及总出现频率、视频时长、帧数、帧率和在线总时间等实用特征。训练了多个机器学习模型，并选择了最稳定的XGBoost模型。最后，通过平均神经网络和XGBoost模型的预测结果来获得最终结果。
URL
https://arxiv.org/abs/2501.01422
