| Autor: |
An-An Liu, Xiaowen Wang, Ning Xu, Junbo Guo, Guoqing Jin, Quan Zhang, Yejun Tang, Shenyuan Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Visual Informatics, Vol 6, Iss 4, Pp 78-89 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2468-502X |
| DOI: |
10.1016/j.visinf.2022.07.003 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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