The 7th Social Media Prediction Challenge

News

• [2024-07-01]:The paper submission due will be June 29 July 5.
• [2024-06-03]:We will notify the paper submission change to top teams.
• [2024-05-25]:We announce the leaderboard this year.
• [2024-05-08]:The submission due is extended to 23:59:59 on May 15.
• [2024-03-15]:The online evaluation for the development phase is open.
• [2024-03-10]:The SMP Challenge website is available now.

Overview

SMP Challenge is an annual challenge that seeks excellent research teams on new ways of forecasting problems and meaningfully improving people’s social lives and business scenarios. The enormous amounts of online content lead to overconsumption, online word-of-mouth helps us to efficiently discover interesting news, emerging topics, the latest stories, or amazing products from the information ocean. Therefore, predicting online popularity became an emerging and significant task for online media, brand marketing, social influencers, or our individuals. We formulated this task as the Social Media Popularity Prediction. It focuses on predicting the impact of online post sharing on social media. It is central to various scenarios, such as online advertising, social recommendation, demand forecasting, etc.

Dataset

Social Media Prediction Dataset (SMPD): a massive-scale, multimodal, and temporal dataset with over 486K social multimedia posts from 70K users and various social media information including anonymized photo-sharing records, user profiles, images, texts, times, or other metadata. SMPD is collected from Flickr's online streams. For keeping temporal properties, we conducted a chronological split for training and testing data (commonly, by date and time).

Important Dates

Citation

If you like or use the resources from the challenge, please read and cite the following references:

@inproceedings{SMP2023,
  title={SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge},
  author={Wu, Bo and Liu, Peiye and Cheng, Wen-Huang and Liu, Bei and Zeng, Zhaoyang and Wang, Jia and Huang, Qiushi and Luo, Jiebo},
  booktitle={Proceedings of the 31th ACM International Conference on Multimedia},
  year={2023}}
@inproceedings{Wu2017DTCN,
    title={Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks},
    author={Wu, Bo and Cheng, Wen-Huang and Zhang, Yongdong and Qiushi, Huang and Jintao, Li and Mei, Tao},
    booktitle={International Joint Conference on Artificial Intelligence (IJCAI)},
    year={2017}}
@inproceedings{Wu2016TemporalPrediction,
    author = {Wu, Bo and Mei, Tao and Cheng, Wen-Huang and Zhang, Yongdong},
    title = {Unfolding Temporal Dynamics: Predicting Social Media Popularity Using Multi-scale Temporal Decomposition},
    booktitle = {Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI)}
    year = {2016}}
@inproceedings{wu2016time,
  title={Time matters: Multi-scale temporalization of social media popularity},
  author={Wu, Bo and Cheng, Wen-Huang and Zhang, Yongdong and Mei, Tao},
  booktitle={Proceedings of the 24th ACM international conference on Multimedia},
  year={2016}}
            

Our Teams

Bo Wu

MIT-IBM Watson AI Lab

Wen-Huang Cheng

National Taiwan University

Bei Liu

Microsoft Research Asia

Jiebo Luo

University of Rochester

Jia Wang

National Yang Ming Chiao Tung University

Zhaoyang Zeng

International Digital Economy Academy

Peiye Liu

Alibaba Group

Qiushi Huang

University of Surrey