SMP Challenge Summary: Social Media Prediction Challenge
Authors: 
Bo Wu, Peiye Liu, Qiushi Huang, Zhaoyang Zeng, Jia Wang, Bei Liu, 
Jiebo Luo, Wen-Huang ChengAuthors Info & Claims
Bo Wu, Peiye Liu, Qiushi Huang, Zhaoyang Zeng, Jia Wang, Bei Liu, Jiebo Luo, Wen-Huang ChengAuthors Info & ClaimsPages 11442 - 11444
Abstract
SMP Challenge is an annual challenge that seeks top research teams to develop innovative forecasting methods that can enhance social and business applications. We define and introduce the Social Media Popularity Prediction (SMPP) task that predicting the future popularity of a post made by a specific user at a given time on social media. This task is pivotal in various applications and scenarios, such as online advertising, social recommendations, post ranking, and demand forecasting, etc. To motivate diverse perspectives of social media prediction researches, we built a large-scale benchmark Social Media Prediction Dataset (SMPD) that includes approximately 500K posts, along with associated 756 tags, visual-language data, and spatial-temporal information, and sourced from around 70K users and their profiles.
With participation and contribution from top teams worldwide, the challenge has seen continuous performance improvements in recent years, driven by technological advancements. For the latest information, leaderboard or online evaluation, please visit the SMP Challenge Homepage: www.smp-challenge.com.
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Index Terms
- SMP Challenge Summary: Social Media Prediction Challenge
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Information & Contributors
Information
Published In
October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
- General Chairs:
- Jianfei Cai,
- Mohan Kankanhalli,
- Balakrishnan Prabhakaran,
- Susanne Boll,
- Program Chairs:
- Ramanathan Subramanian,
- Liang Zheng,
- Vivek K. Singh,
- Pablo Cesar,
- Lexing Xie,
- Dong Xu
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
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Published: 28 October 2024
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MM '24
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MM '24: The 32nd ACM International Conference on Multimedia
October 28 - November 1, 2024
Melbourne VIC, Australia
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MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%
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