| --- |
| annotations_creators: |
| - machine-generated |
| - expert-generated |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| multilinguality: monolingual |
| pretty_name: EventX |
| size_categories: |
| - 1M<n<10M |
| source_datasets: |
| - original |
| tags: |
| - prediction-markets |
| - social-media |
| - multimodal |
| - financial-nlp |
| - twitter |
| - polymarket |
| task_categories: |
| - text-classification |
| - tabular-classification |
| task_ids: |
| - multi-class-classification |
| --- |
| |
| # EventX |
|
|
| A multimodal benchmark linking 9M Twitter/X posts to 11,952 Polymarket prediction markets (2021--2026). |
|
|
| ## Dataset Description |
|
|
| EventX connects social media posts on Twitter/X to prediction market dynamics on Polymarket. It provides seven tasks spanning two tiers: |
|
|
| - **Resolution tier** (human-annotated): Post-to-Market Linking (T2), Evidence Grading (T3) |
| - **Forecast tier** (deterministic labels): Market Volume Prediction (T1), Market Movement Prediction (T4), Impact Persistence (T5), Cross-Market Propagation (T6) |
|
|
| ### Supported Tasks |
|
|
| | Config | Task | Description | |
| |--------------|----------|----------| |
| | `t1` | Market Volume Prediction | Predict eventual trading volume from pre-market tweets | |
| | `t2` | Post-to-Market Linking | Match a tweet to the correct prediction market | |
| | `t3` | Evidence Grading | Grade tweet relevance to a market (0-5) | |
| | `t4` | Market Movement Prediction | Predict price direction and magnitude at 2h horizon | |
| | `t5` | Volume & Price Impact | Predict price_impact and volume_multiplier (continuous) | |
| | `t6` | Cross-Market Propagation | Predict spillover to sibling markets | |
| | `t7` | Impact Persistence (Decay) | Classify decay: transient/sustained/reversal | |
| | `posts` | Tweet Metadata | Tweet IDs (text stripped for privacy) | |
| | `markets` | Market Metadata | Market questions, categories, resolution info | |
| | `ohlcv` | Market OHLCV | Price/volume time series | |
|
|
| ### Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load a task split |
| ds = load_dataset("mlsys-io/EventXBench", "t1") |
| train_df = ds["train"].to_pandas() |
| test_df = ds["test"].to_pandas() |
| |
| # Load market metadata |
| markets = load_dataset("mlsys-io/EventXBench", "markets") |
| |
| # Load OHLCV time series |
| ohlcv = load_dataset("mlsys-io/EventXBench", "ohlcv") |
| ``` |
|
|
| ## Data Fields |
|
|
| ### T1: Market Volume Prediction |
|
|
| - `event_group_id` (str): Cluster ID for the event group |
| - `condition_id` (str): Polymarket market condition ID |
| - `question` (str): Market question text |
| - `category` (str): Market category |
| - `tweet_count` (int): Number of tweets in the event cluster |
| - `unique_user_count` (int): Distinct authors |
| - `burst_duration_hours` (float): Duration of tweet burst |
| - `max_author_followers` (int): Max follower count in cluster |
| - `interest_label` (str): Target label -- `high` (>80th pctl), `moderate` (40th--80th), `low` (<40th) |
| - ... (see full schema in the dataset viewer) |
|
|
| ### T2: Post-to-Market Linking |
|
|
| - `tweet_id` (int): Twitter post ID |
| - `tweet_text` (str): Tweet text content |
| - `market_id` (str): Polymarket condition ID |
| - `market_question` (str): Market question text |
| - `embedding_score` (float): Semantic similarity score |
|
|
| ### T3: Evidence Grading |
|
|
| - `tweet_id` (int): Twitter post ID |
| - `condition_id` (str): Polymarket condition ID |
| - `tweet` (str): Tweet text |
| - `market` (str): Market metadata |
| - `question` (str): Market question |
| - `final_grade` (int): Evidence grade 0-5 |
| - `llm_grade` (int): LLM-assigned grade |
| - `llm_confidence` (float): LLM confidence score |
|
|
| ### T4: Market Movement Prediction |
|
|
| - `tweet_id` (int): Twitter post ID |
| - `condition_id` (str): Polymarket condition ID |
| - `price_t0` (float): Price at tweet publication time |
| - `delta_2h` (float): Absolute price change at 2h horizon |
| - `direction_label` (str): `up`, `down`, or `flat` |
| - `magnitude_bucket` (str): `small`, `medium`, or `large` |
| - `confound_flag` (bool): Whether confounding events were detected |
|
|
| ### T5: Volume and Price Impact |
|
|
| - `tweet_id` (int): Twitter post ID |
| - `condition_id` (str): Polymarket condition ID |
| - `price_impact_json` (dict): Max absolute deviation from p0 at horizons (15m, 30m, 1h, 2h, 6h) |
| - `volume_multiplier_json` (dict): Total volume / 24h baseline at multiple horizons |
| - `decay_class` (str): `transient`, `sustained`, or `reversal` |
| - `confound_flag` (bool): Whether confounding events were detected |
| - Metrics: Spearman rho for price_impact and volume_multiplier, decay macro-F1 |
|
|
| ### T6: Cross-Market Propagation |
|
|
| - `tweet_id` (int): Twitter post ID |
| - `primary_condition_id` (str): Primary market condition ID |
| - `label` (str): `no_effect`, `primary_mover`, `propagated_signal` |
| - `sibling_count` (int): Number of sibling markets |
| - `moved_sibling_count` (int): Number of siblings that moved |
| - `primary_delta_h` (float): Primary market price change |
| - `confound_flag` (bool): Whether confounding events were detected |
|
|
| ### Posts (Tweet Metadata) |
|
|
| - `tweet_id` (int): Twitter post ID |
| - `text` (null): Set to NULL for privacy -- use Twitter API for rehydration |
| - Additional metadata fields (timestamps, user IDs, etc.) |
|
|
| ## Privacy and Ethics |
|
|
| - **Tweet text**: Stripped from the public release per Twitter/X ToS. Tweet IDs are provided for authorized rehydration. |
| - **Market data**: Polymarket data is publicly available on-chain and included under fair use for research. |
| - **No PII**: User-level features are aggregated; no individual user profiles are released. |
|
|
| ## License |
|
|
| CC BY-NC 4.0 |
|
|
