voidly-contagion-chain-v1

Voidly Atlas Contagion-Chain Classifier v1

Version: v1.0 | Trained: 2026-05-21 | License: CC BY 4.0

Given a trigger event in country A, predict P(follower country B has an event within N days). Pairwise temporal XGBoost classifier — substitutes for full parametric Hawkes (data too sparse for parametric kernels).

Eval

Feature importance

• horizon_days (importance 0.1292)
• month_sin (importance 0.1107)
• global_30d_events (importance 0.1047)
• month_cos (importance 0.0977)
• follower_base_rate (importance 0.0954)
• follower_90d_events (importance 0.0922)
• follower_enc (importance 0.0868)
• days_since_follower_last (importance 0.0799)
• follower_30d_events (importance 0.0645)
• same_auth_cluster (importance 0.0357)
• pair_coevents_365d (importance 0.0326)
• trigger_enc (importance 0.0274)
• trigger_30d_events (importance 0.0273)
• same_region (importance 0.0161)

Honest caveats

• Pairwise temporal classifier substitutes for full Hawkes (data too sparse for parametric kernels)
• did_follow=1 is statistical co-event, not causal — use /v1/sentinel/attribute for causality
• Top-pair P~0.99 estimates have sparse support (n=2 held-out triggers)

Reproducibility

python3 scripts/build-pairwise-contagion-features.py

Citation

@misc{voidly_voidly_contagion_chain_v1,
  title  = {Voidly Atlas: voidly-contagion-chain-v1 (v1)},
  author = {Voidly},
  year   = {2026},
  url    = {https://huggingface.co/emperor-mew/voidly-contagion-chain-v1},
  note   = {Open censorship-research ML stack. CC BY 4.0.}
}