research-article

A Hybrid Model Combining Convolutional Neural Network with XGBoost for Predicting Social Media Popularity

Authors:

Wenyin LiuAuthors Info & Claims

MM '17: Proceedings of the 25th ACM international conference on Multimedia

Pages 1912 - 1917

https://doi.org/10.1145/3123266.3127902

Published: 23 October 2017 Publication History

Abstract

A hybrid model for social media popularity prediction is proposed by combining Convolutional Neural Network (CNN) with XGBoost. The CNN model is exploited to learn high-level representations from the social cues of the data. These high-level representations are used in XGBoost to predict the popularity of the social posts. We evaluate our approach on a real-world Social Media Prediction (SMP) dataset, which consists of 432K Flickr images. The experimental results show that the proposed approach is effective, achieving the following performance: Spearman's Rho: 0.7406, MSE: 2.7293, MAE: 1.2475.

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Cited By

Liu S(2024)A Gold and Bitcoin Trading Model Based on XGBoost PredictionProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675419(7-11)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675419
Cheng ZZhang JXu XTrajcevski GZhong TZhou FBaeza-Yates RBonchi F(2024)Retrieval-Augmented Hypergraph for Multimodal Social Media Popularity PredictionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672041(445-455)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672041
Tas ISimsek M(2024)A Machine Learning Regression Analysis Application: Smart Device Price2024 IEEE 9th International Conference for Convergence in Technology (I2CT)10.1109/I2CT61223.2024.10544204(1-6)Online publication date: 5-Apr-2024
https://doi.org/10.1109/I2CT61223.2024.10544204
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Published In

cover image ACM Conferences

MM '17: Proceedings of the 25th ACM international conference on Multimedia

October 2017

2028 pages

ISBN:9781450349062

DOI:10.1145/3123266

General Chairs:
Qiong Liu
FXPAL, USA
,
Rainer Lienhart
Universität Augsburg, Germany
,
Haohong Wang
TCL America, USA
,
Program Chairs:
Sheng-Wei "Kuan-Ta" Chen
Academia Sinica, Taiwan
,
Susanne Boll
University of Oldenburg, Germany
,
Phoebe Chen
La Trobe University, Australia
,
Gerald Friedland
Lawrence Livermore National Lab, USA
,
Jia Li
Google, USA
,
Shuicheng Yan
Qihoo 360, China

Copyright © 2017 ACM.

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Publication History

Published: 23 October 2017

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Guangdong Innovative Research Team Program

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MM '17

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MM '17: ACM Multimedia Conference

October 23 - 27, 2017

California, Mountain View, USA

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MM '17 Paper Acceptance Rate 189 of 684 submissions, 28%;

Overall Acceptance Rate 995 of 4,171 submissions, 24%

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MM '24

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The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

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Cited By

Liu S(2024)A Gold and Bitcoin Trading Model Based on XGBoost PredictionProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675419(7-11)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675419
Cheng ZZhang JXu XTrajcevski GZhong TZhou FBaeza-Yates RBonchi F(2024)Retrieval-Augmented Hypergraph for Multimodal Social Media Popularity PredictionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672041(445-455)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672041
Tas ISimsek M(2024)A Machine Learning Regression Analysis Application: Smart Device Price2024 IEEE 9th International Conference for Convergence in Technology (I2CT)10.1109/I2CT61223.2024.10544204(1-6)Online publication date: 5-Apr-2024
https://doi.org/10.1109/I2CT61223.2024.10544204
Mu YNiu PBontcheva KAletras N(2024)Predicting and analyzing the popularity of false rumors in WeiboExpert Systems with Applications10.1016/j.eswa.2023.122791243(122791)Online publication date: Jun-2024
https://doi.org/10.1016/j.eswa.2023.122791
Ma HYang PWang FWang XYang DFeng B(2023)Short-Term Heavy Overload Forecasting of Public Transformers Based on Combined LSTM-XGBoost ModelEnergies10.3390/en1603150716:3(1507)Online publication date: 3-Feb-2023
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Mavaie PHolder LSkinner M(2023)Hybrid deep learning approach to improve classification of low-volume high-dimensional dataBMC Bioinformatics10.1186/s12859-023-05557-w24:1Online publication date: 7-Nov-2023
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Wang BWu DZhang KZhang HZhang C(2023)Prediction Model of Fault Block Reservoir Measure Index Based on 1DCNN-LightGBMScientific Programming10.1155/2023/85554232023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/8555423
Theophilo AGiot RRocha A(2023)Authorship Attribution of Social Media MessagesIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.312389510:1(10-23)Online publication date: Feb-2023
https://doi.org/10.1109/TCSS.2021.3123895
Sivakumar SPriyanka SSelvam P(2023)Enhancing Personality Type Prediction with Ensemble Models: A Robust Predictive Approach2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)10.1109/ICSES60034.2023.10465452(1-7)Online publication date: 14-Dec-2023
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Wang JYang SZhao HYang Y(2023)Social media popularity prediction with multimodal hierarchical fusion modelComputer Speech & Language10.1016/j.csl.2023.10149080(101490)Online publication date: May-2023
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