Social Media Prediction Challenge

News

• [2020-06-20]:Congratulations! The leaderboard of SMP Challenge 2020 is available now. To Top-6 teams: please prepare to submit codes to social.media.prediction@gmail.com for final checking before June 23; the paper submission due is July 13.

• [2020-05-15]:The test dataset is available now, and you can evaluate your model here.

• [2020-05-05]: Please check the new deadlines (caused by COVID-19) in the following Important Dates.

• [2020-04-01]: Evaluation Server has been released.

• [2020-02-24]: We are hosting Social Media Prediction Challenge 2020.

Overview

Continuing the series of Social Media Prediction (SMP) Challenges, the 2020 edition is seeking excellent research teams to provide new ways of forecasting problems and meaningfully improve people’s social lives and business scenarios. Temporal Popularity Prediction is a massive-scale, multimodal and time-series forecasting problem that is central to various scenarios, such as online advertising, social recommendation, demand forecasting, etc. We released a large-scale Social Media Prediction Dataset (SMPD) with over 486K posts, 70K users and rich information including user profiles, images, texts, times, locations, and categories. This year the challenge will be again hosted by the joint team from multiple research organizations.

Dataset Statistics

The SMPD (Social Media Prediction Dataset) contains 486K social multimedia posts from 70K users and various social media information including anonymized photo-sharing records, user profile, web image, text, time, location, category, etc. SMPD is a multi-faced, large-scale, temporal web data collection, which collected from Flickr (one of the largest photo-sharing platforms). For the time-series forecasting task, we split training/testing data into chronological sets (commonly, by date and time). The tables below show the statistics of the dataset.

Important Dates

Citation

If you intend to publish results that use the information and resources provided by this challenge, please include the following references:

@inproceedings{Wu2019SMP,
  title={SMP Challenge: An Overview of Social Media Prediction Challenge 2019},
  author={Wu, Bo and Cheng, Wen-Huang and Liu, Peiye and Liu, Bei and Zeng,   Zhaoyang and Luo,
  Jiebo},
  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
  year={2019}
}
@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},
  location = {Melbourne, Australia}
}
@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},
  location = {Phoenix, Arizona}
            

Our Teams

Bo Wu

Columbia University

bo.wu@columbia.edu

Wen-Huang Cheng

National Chiao Tung University

whcheng@nctu.edu.tw

Bei Liu

Microsoft Research Asia

bei.liu@microsoft.com

Jiebo Luo

University of Rochester

jluo@cs.rochester.edu

Zhaoyang Zeng

Sun Yat-sen University Microsoft Research Asia

zengzhy5@mail2.sysu.edu.cn

Peiye Liu

Beijing University of Posts and Telecommunications

liupeiye@bupt.edu.cn

Jia Wang

National Chiao Tung University

jiawang169@outlook.com