README.md

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  SMP CHALLENGE COMMUNITY LICENSE AGREEMENT

  Last Updated: March 17, 2025

  1. INTRODUCTION

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  4. GENERAL TERMS

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  5. DEFINITIONS

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task_categories:
  - image-classification
language:
  - en
tags:
  - social-media-popularity
  - image-popularity-prediction
  - flickr
  - smp-challenge
pretty_name: Social Media Prediction - Image
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: test
        path: data/test-*.parquet
    features:
      - name: image
        dtype: image
      - name: Uid
        dtype: string
      - name: Pid
        dtype: string
      - name: img_filepath
        dtype: string
      - name: label
        dtype: float32
      - name: Postdate
        dtype: string
      - name: Longitude
        dtype: string
      - name: Latitude
        dtype: string
      - name: Geoaccuracy
        dtype: string
      - name: Category
        dtype: string
      - name: Subcategory
        dtype: string
      - name: Concept
        dtype: string
      - name: Alltags
        dtype: string
      - name: Title
        dtype: string
      - name: Mediatype
        dtype: string
      - name: Mediastatus
        dtype: string
      - name: Pathalias
        dtype: string
      - name: Ispublic
        dtype: string
      - name: photo_count
        dtype: float32
      - name: ispro
        dtype: float32
      - name: timezone_offset
        dtype: string
      - name: photo_firstdate
        dtype: string
      - name: photo_firstdatetaken
        dtype: string
      - name: timezone_id
        dtype: string
      - name: user_description
        dtype: string
      - name: location_description
        dtype: string

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:

• Challenge Homepage
• Task
• Dataset

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}}