metadata
license: mit
tags:
- synthetic-data
- ranking
- recommender-systems
- coordinated-inauthentic-behavior
- ai-safety
pretty_name: OON Relief Synthetic Cohorts
OON Relief Synthetic Cohorts
Synthetic candidate data used in the OON Relief Simulation, a reference implementation of a velocity-gated out-of-network ranking relief mechanism.
This is illustrative synthetic data, not derived from any real platform telemetry.
Cohorts
- breaking_event (15 rows) — organic, accelerating engagement, low prior exposure
- normal_oon (15 rows) — steady-state out-of-network baseline content
- coordinated_burst (10 rows) — engineered to hit breaking-event-level raw velocity while impressions are already well past the macro-exposure ceiling, simulating a click-farm/botnet amplification pattern
Columns
| Column | Description |
|---|---|
| cohort | Which synthetic cohort the row belongs to |
| tweet_id | Synthetic identifier |
| age_minutes | Post age at time of scoring |
| fav_count / reply_count / repost_count / quote_count | Raw engagement counts |
| in_network | Whether the candidate is in-network (always False here) |
| is_policy_clean | Stand-in for content-policy classifier verdict |
| author_followers | Synthetic author follower count, used for tier normalization |
| network_impressions | Macro-exposure counter used by the impression-ceiling guard |
| engagement_velocity_raw | Engagement per minute, unnormalized |
| engagement_velocity_score | Velocity normalized against author-tier baseline, squashed to [0, 1] |
Generation
Generated with fixed random seeds (1, 2, 3) via synthetic_data.py in the companion Space's repository, so the data is fully reproducible.
Author
Alice (Kay) — HuggingFace · X