TY - GEN
T1 - Social Media Popularity Prediction
T2 - 27th ACM International Conference on Multimedia (MM '19)
AU - Ding, Keyan
AU - Wang, Ronggang
AU - Wang, Shiqi
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2019/10
Y1 - 2019/10
N2 - Social media popularity prediction (SMPD) aims to predict the popularity of the post shared on online social media platforms. This task is crucial for content providers and consumers in a wide range of real-world applications, including multimedia advertising, recommendation system and trend analysis. In this paper, we propose to fuse features from multiple sources by deep neural networks (DNNs) for popularity prediction. Specifically, high-level image and text features are extracted by the advanced pretrained DNN, and numerical features are captured from the metadata of the posts. All of the features are concatenated and fed into a regressor with multiple dense layers. Experiments have demonstrated the effectiveness of the proposed model on the ACM Multimedia Challenge SMPD2019 dataset. We also verify the importance of each feature via univariate test and ablation study, and provide the insights of feature combination for social media popularity prediction.
AB - Social media popularity prediction (SMPD) aims to predict the popularity of the post shared on online social media platforms. This task is crucial for content providers and consumers in a wide range of real-world applications, including multimedia advertising, recommendation system and trend analysis. In this paper, we propose to fuse features from multiple sources by deep neural networks (DNNs) for popularity prediction. Specifically, high-level image and text features are extracted by the advanced pretrained DNN, and numerical features are captured from the metadata of the posts. All of the features are concatenated and fed into a regressor with multiple dense layers. Experiments have demonstrated the effectiveness of the proposed model on the ACM Multimedia Challenge SMPD2019 dataset. We also verify the importance of each feature via univariate test and ablation study, and provide the insights of feature combination for social media popularity prediction.
KW - Deep neural networks
KW - Features fusion
KW - Image popularity
KW - Social media
UR - https://www.scopus.com/pages/publications/85074869568
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85074869568&origin=recordpage
U2 - 10.1145/3343031.3356062
DO - 10.1145/3343031.3356062
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781450368896
T3 - MM - Proceedings of the ACM International Conference on Multimedia
SP - 2682
EP - 2686
BT - MM '19: Proceedings of the 27th ACM International Conference on Multimedia
PB - Association for Computing Machinery
Y2 - 21 October 2019 through 25 October 2019
ER -