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license: cc-by-4.0
language:
- en
configs:
- config_name: benchmark
data_files:
- split: posts
path: bluesky/thread_metadata_updated_added.parquet
MMG-Pop Benchmark Dataset
This dataset supports the MMG-Pop (Multimodal Graph-based Popularity Prediction) Benchmark.
Directory Overview
metadata/
Contains the full cascade files for each dataset. These files are the main source for labels, post content, and complete conversation structure.
thread_metadata_updated_added.parquet or reddit_*_metadata.parquet
One row per discussion tree or thread. Includes cascade-level metadata and final prediction targets, such as:
- width
- depth
- virality
- size (given as num_posts)
- unique users
- like score (given as score in Reddit datasets or like_count in Bluesky)
thread_posts_with_all_labels2.parquet or reddit_*_posts.parquet
One row per post or comment in the full cascade. Contains raw post text and post-level fields used to build node features and reconstruct the conversation tree.
user_degrees.csv
Bluesky-only auxiliary file mapping users to follower counts. Used to derive root-user follower features.
snapshots/
Contains early-window versions of the cascade graph. These are the forecasting inputs: what the model observes after only the first few minutes of cascade growth.
edges_{window}min.parquet
Reply edges observed within a specific early window. Defines the partial graph structure at that time.
meta_{window}min.parquet
Cascade-level snapshot records for the same window. Used to identify valid trees at that early stage and align them with the full metadata and post files.
splits/
Optional saved train, validation, and test splits for reproducibility.
train_ids_*.npy, val_ids_*.npy, test_ids_*.npy
Lists of cascade IDs assigned to each split for a given observation window.
future_horizons/
Optional files for future-horizon prediction beyond the default final-state prediction setting.
gt_4h.parquet, gt_8h.parquet, gt_16h.parquet, gt_24h.parquet
Ground-truth cascade outcomes measured at intermediate future times.
valid_mask.parquet
Marks which cascade/horizon pairs are valid and should be used for training or evaluation.
File Structure
bluesky/
metadata/
thread_metadata_updated_added.parquet
thread_posts_with_all_labels2.parquet
user_degrees.csv
snapshots/
edges_2min.parquet
meta_2min.parquet
edges_10min.parquet
meta_10min.parquet
splits/
train_ids_2min.npy
val_ids_2min.npy
test_ids_2min.npy
train_ids_10min.npy
val_ids_10min.npy
test_ids_10min.npy
future_horizons/
gt_4h.parquet
gt_8h.parquet
gt_16h.parquet
gt_24h.parquet
valid_mask.parquet
reddit/
gaming/
metadata/
reddit_gaming_metadata.parquet
reddit_gaming_posts.parquet
snapshots/
edges_20min.parquet
meta_20min.parquet
edges_50min.parquet
meta_50min.parquet
splits/
train_ids_20min.npy
val_ids_20min.npy
test_ids_20min.npy
train_ids_50min.npy
val_ids_50min.npy
test_ids_50min.npy
future_horizons/
gt_4h.parquet
gt_8h.parquet
gt_16h.parquet
gt_24h.parquet
valid_mask.parquet
futurology/
metadata/
reddit_futurology_metadata.parquet
reddit_futurology_posts.parquet
snapshots/
edges_90min.parquet
meta_90min.parquet
splits/
train_ids_90min.npy
val_ids_90min.npy
test_ids_90min.npy
future_horizons/
gt_4h.parquet
gt_8h.parquet
gt_16h.parquet
gt_24h.parquet
valid_mask.parquet
ama/
metadata/
reddit_ama_metadata.parquet
reddit_ama_posts.parquet
snapshots/
edges_30min.parquet
meta_30min.parquet
splits/
train_ids_15min.npy
val_ids_15min.npy
test_ids_15min.npy
train_ids_30min.npy
val_ids_30min.npy
test_ids_30min.npy
future_horizons/
gt_4h.parquet
gt_8h.parquet
gt_16h.parquet
gt_24h.parquet
valid_mask.parquet