---
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](https://github.com/mlsys-io/EventXBench); the primary dataset mirror is [mlsys-io/EventXBench](https://huggingface.co/datasets/mlsys-io/EventXBench).

## Quick Start

```bash
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](https://creativecommons.org/licenses/by-nc/4.0/)
- Tweet text excluded; use Twitter API for rehydration
- Polymarket data included under fair use for research