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Social Media Prediction Challenge - Image

Part of SMP Challenge: https://smp-challenge.com

Dataset Overview

The SMP-Image dataset supports social media popularity prediction using Flickr images and rich metadata. Popularity is defined as the normalised view-count an image receives within a fixed time window after posting.

Split	Samples	Labels
train	305,613	popularity score (float)
test	180,581	held-out (-1)

How to Use the Dataset

from datasets import load_dataset

# Load both splits
ds = load_dataset("smpchallenge/SMP-Image")

# Or a single split
train = load_dataset("smpchallenge/SMP-Image", split="train")
test  = load_dataset("smpchallenge/SMP-Image", split="test")

Each example contains:

{
  "image":        <PIL.Image>,          # JPEG image
  "img_filepath": "train/uid/pid.jpg",  # relative path
  "label":        11.18,                # popularity score (-1 for test)
  "Uid":          "59@N75",
  "Pid":          "775",
  "Category":     "Fashion",
  "Subcategory":  "Fashion",
  "Concept":      "glam",
  "Postdate":     "1446016778",
  # ... 26 columns total
}

Columns

Column	Type	Description
`image`	Image	JPEG image (PIL format via datasets)
`Uid`	string	Flickr user ID
`Pid`	string	Flickr photo ID
`img_filepath`	string	Relative image path (e.g. `train/uid/pid.jpg`)
`label`	float	Popularity score (train); -1 for test
`Category`	string	Broad category (Fashion, Travel, …)
`Subcategory`	string	Sub-category
`Concept`	string	Fine-grained concept tag
`Postdate`	string	Unix timestamp of posting
`Longitude`	string	Longitude (empty if unknown)
`Latitude`	string	Latitude
`Geoaccuracy`	string	Geo accuracy level
`Alltags`	string	Space-separated photo tags
`Title`	string	Photo title
`Mediatype`	string	Media type (photo)
`Mediastatus`	string	Media status (ready)
`Pathalias`	string	User path alias on Flickr
`Ispublic`	string	Whether photo is public
`photo_count`	float	Number of photos the user has
`ispro`	float	Whether user is Flickr Pro
`timezone_offset`	string	User timezone offset
`photo_firstdate`	string	Unix timestamp of user's first photo
`photo_firstdatetaken`	string	Date of user's first photo taken
`timezone_id`	string	User timezone ID
`user_description`	string	User profile description
`location_description`	string	User location description

About SMP Challenge

Social Media Prediction Challenge (SMP Challenge) is an annual international competition dedicated to advancing research in social multimodal forecasting. It aims to identify outstanding research teams and innovative solutions that can contribute to understanding and predicting user behavior, content virality, and engagement trends across social platforms.

How to Join

For detailed information about the SMP Challenge, including tasks, evaluation criteria, and ongoing updates, please visit:

Citation

If you use this dataset, please cite:

@inproceedings{SMPanalysis2023,
  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 31st ACM International Conference on Multimedia},
  year={2023}}
@inproceedings{SMPdataset2019,
  author = {Wu, Bo and Cheng, Wen-Huang and Liu, Peiye and Liu, Bei and Zeng, Zhaoyang and Luo, Jiebo},
  title = {SMP Challenge: An Overview of Social Media Prediction Challenge 2019},
  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
  year = {2019}}
@inproceedings{Wu2017TemporalContext,
  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{Wu2016Prediction,
  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}}

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