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Datasets:
hayshn
/
ICAIF26-EventXbench

Tasks:
Text Classification
Tabular Classification
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1M<n<10M
Tags:
prediction-markets
social-media
multimodal
financial-nlp
twitter
polymarket
License:
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ICAIF26-EventXbench / README.md
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metadata 
license: cc-by-nc-4.0
language:
  - en
pretty_name: ICAIF26-EventXbench
size_categories:
  - 1M<n<10M
task_categories:
  - text-classification
  - tabular-classification
task_ids:
  - multi-class-classification
tags:
  - prediction-markets
  - social-media
  - multimodal
  - financial-nlp
  - twitter
  - polymarket

ICAIF26-EventXbench

EventX is a multimodal benchmark connecting ~9M Twitter/X posts from 1,152 KOL accounts to 11,952 Polymarket prediction markets (2021–2026). It defines seven tasks across two tiers: resolution (human-annotated ground truth) and forecast (deterministic labels from post-publication tick data).

This repository hosts the canonical KDD v2 release of the EventXBench data (tasks T1, T2, T4, T5, T6). The companion codebase is available at github.com/mlsys-io/EventXBench; the primary dataset mirror is mlsys-io/EventXBench.

Quick Start 

pip install datasets

from datasets import load_dataset

# T1: train + test (2-way temporal split)
ds = load_dataset("hayshn/ICAIF26-EventXbench", "t1")

# T4/T5/T6: train + validation + test
ds = load_dataset("hayshn/ICAIF26-EventXbench", "t4")

Alternatively, use the repository loader (eventxbench.loader) with local_dir= pointing at this dataset's data directory.

Benchmark Overview (v2 counts)

Task Name Tier Output Instances (v2) Primary Metrics
T1 Market Volume Prediction Forecast 3-class (high_interest/moderate_interest/low_interest) 984 Macro-F1
T2 Post-to-Market Linking Resolution Market ID or NONE 5,543 (5,000 human-gold eval) Accuracy@1, MRR
T4 Market Movement Prediction Forecast Direction × Magnitude 10,934 Dir-Acc, Mag-F1
T5 Volume & Price Impact Forecast Continuous + decay_class 3,342 Spearman ρ, Decay macro-F1
T6 Cross-Market Propagation Forecast 3-class (no_effect/primary_only/cross_market) 4,583 Macro-F1

T3 (Evidence Grading) is not included in this repository. See the primary mirror for the rebuilt T3 artifacts (train + gold).

Splits

Task Train Validation Test Split policy
T1 709 — 275 Event- and market-group atomic, temporal 60/0/40
T2 543 2,500 2,500 Train: silver + single-human; val/test: three-reviewer human gold, zero cross-split overlap
T4 2,875 2,268 5,791 Event-cluster atomic temporal (2025-05→2026-06)
T5 889 692 1,761 Same temporal policy as T4 (non-flat subset)
T6 766 1,225 2,592 Event-cluster atomic temporal × horizons {1,3,7}d

Task Descriptions

  • T1 – Market Volume Prediction: predict the creation-cohort volume percentile of a market from pre-market social signals (post links within 7 days, cosine+entity-verified).
  • T2 – Post-to-Market Linking: given a post and a frozen BGE-M3 candidate set (top-10), select the addressed market or NONE.
  • T4 – Market Movement Prediction: predict the YES-price direction (up/down/flat) and magnitude bucket at 1/3/7-day horizons for a market-day bundle.
  • T5 – Volume & Price Impact: predict continuous price_impact and volume_multiplier targets and the decay_class (transient/sustained/reversal).
  • T6 – Cross-Market Propagation: predict whether a bundle's move propagates to sibling markets (graph v2, 35,526 edges / 14,739 nodes), labeled no_effect/primary_only/cross_market at 1/3/7-day horizons.

Label Distributions

  • T1: high_interest 480, moderate_interest 387, low_interest 117
  • T2: train LINK 402 / NONE 141; validation LINK 1,179 / NONE 1,321; test LINK 1,198 / NONE 1,302
  • T4: flat 7,592; up 1,730; down 1,612 (direction)
  • T5: sustained 1,932; reversal 807; transient 300; (303 rows have no computable decay class)
  • T6: cross_market 2,796; no_effect 1,366; primary_only 421

Leakage Control

All forecast tasks (T1, T4, T5, T6) use event-disjoint temporal splits with an embargo ≥ the maximum label horizon. Features are computed as-of the decision time; labels are strictly in the future. T4/T5/T6 attach confound_flag when a different-author high-evidence post appears within the label window. T2's val/test sets are sealed human gold with zero instance, tweet, or exact-text overlap across splits.

Privacy

Raw tweet text is not included for non-T2 tasks; records carry tweet_ids for rehydration. T2's val/test include post text under the dataset's Data Usage Agreement (ID-only distribution otherwise). Market data from Polymarket is fully included.

Files 

data/
├── t1/    train.jsonl, test.jsonl
├── t2/    t2_train.jsonl, t2_val.jsonl, t2_test.jsonl
├── t4/    train.jsonl, validation.jsonl, test.jsonl
├── t5/    train.jsonl, validation.jsonl, test.jsonl
└── t6/    train.jsonl, validation.jsonl, test.jsonl

License

  • Data: CC BY-NC 4.0
  • Tweet text excluded; use Twitter API for rehydration
  • Polymarket data included under fair use for research