基于特征融合的媒体流行度预测方法综述

IF 3.8 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Visual Informatics Pub Date : 2022-12-01 DOI:10.1016/j.visinf.2022.07.003
An-An Liu , Xiaowen Wang , Ning Xu , Junbo Guo , Guoqing Jin , Quan Zhang , Yejun Tang , Shenyuan Zhang
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引用次数: 2

摘要

随着社交媒体的普及,信息传播方式发生了变化,基于社交媒体平台的信息流行度预测受到了广泛关注。基于特征融合的媒体流行度预测方法关注社交媒体的多模态特征,旨在探索影响媒体流行度的关键因素。同时,该方法弥补了传统基于信息传播过程的特征利用方法的不足。本文从特征提取和预测模型构建两方面综述了基于特征融合的媒体热度预测方法。在此之前,我们分析了媒体受欢迎程度的影响因素,以提供直观的理解。我们进一步讨论了现有方法和数据集的优缺点,以突出未来的方向。最后,讨论了人气预测的应用。据我们所知,这是第一个基于调查报告特征融合的媒体人气预测方法。
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A review of feature fusion-based media popularity prediction methods

With the popularization of social media, the way of information transmission has changed, and the prediction of information popularity based on social media platforms has attracted extensive attention. Feature fusion-based media popularity prediction methods focus on the multi-modal features of social media, which aim at exploring the key factors affecting media popularity. Meanwhile, the methods make up for the deficiency in feature utilization of traditional methods based on information propagation processes. In this paper, we review feature fusion-based media popularity prediction methods from the perspective of feature extraction and predictive model construction. Before that, we analyze the influencing factors of media popularity to provide intuitive understanding. We further argue about the advantages and disadvantages of existing methods and datasets to highlight the future directions. Finally, we discuss the applications of popularity prediction. To the best of our knowledge, this is the first survey reporting feature fusion-based media popularity prediction methods.

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来源期刊
Visual Informatics
Visual Informatics Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.70
自引率
3.30%
发文量
33
审稿时长
79 days
期刊最新文献
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