SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge

Fuente: arXiv

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Bibliographic Details
Main Authors:	Wu, Bo, Liu, Peiye, Cheng, Wen-Huang, Liu, Bei, Zeng, Zhaoyang, Wang, Jia, Huang, Qiushi, Luo, Jiebo
Format:	Preprint
Published:	2024
Subjects:	Multimedia Artificial Intelligence Computer Vision and Pattern Recognition Social and Information Networks
Online Access:	Acceder al recurso
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_version_	1866916250066616320
author	Wu, Bo Liu, Peiye Cheng, Wen-Huang Liu, Bei Zeng, Zhaoyang Wang, Jia Huang, Qiushi Luo, Jiebo
author_facet	Wu, Bo Liu, Peiye Cheng, Wen-Huang Liu, Bei Zeng, Zhaoyang Wang, Jia Huang, Qiushi Luo, Jiebo
contents	Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts, leveraging vast amounts of multimodal data available on social media platforms. Studying and investigating social media popularity becomes central to various online applications and requires novel methods of comprehensive analysis, multimodal comprehension, and accurate prediction. SMP Challenge is an annual research activity that has spurred academic exploration in this area. This paper summarizes the challenging task, data, and research progress. As a critical resource for evaluating and benchmarking predictive models, we have released a large-scale SMPD benchmark encompassing approximately half a million posts authored by around 70K users. The research progress analysis provides an overall analysis of the solutions and trends in recent years. The SMP Challenge website (www.smp-challenge.com) provides the latest information and news.
format	Preprint
id	arxiv_https___arxiv_org_abs_2405_10497
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge Wu, Bo Liu, Peiye Cheng, Wen-Huang Liu, Bei Zeng, Zhaoyang Wang, Jia Huang, Qiushi Luo, Jiebo Multimedia Artificial Intelligence Computer Vision and Pattern Recognition Social and Information Networks Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts, leveraging vast amounts of multimodal data available on social media platforms. Studying and investigating social media popularity becomes central to various online applications and requires novel methods of comprehensive analysis, multimodal comprehension, and accurate prediction. SMP Challenge is an annual research activity that has spurred academic exploration in this area. This paper summarizes the challenging task, data, and research progress. As a critical resource for evaluating and benchmarking predictive models, we have released a large-scale SMPD benchmark encompassing approximately half a million posts authored by around 70K users. The research progress analysis provides an overall analysis of the solutions and trends in recent years. The SMP Challenge website (www.smp-challenge.com) provides the latest information and news.
title	SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge
topic	Multimedia Artificial Intelligence Computer Vision and Pattern Recognition Social and Information Networks
url	https://arxiv.org/abs/2405.10497