FAQ

Is this a genocide/violence prediction tool? No. It classifies text against a harm taxonomy and tracks whether a source's rhetoric is trending toward more severe categories over time. It does not predict real-world violence, and the severity ladder it uses is a simplified, project-specific model inspired by general escalation research — not a validated academic or forecasting instrument. Treat its output as a prioritization signal for human review, not a verdict.

How is this different from Perspective API / other toxicity classifiers? Most toxicity APIs score a single message for general toxicity. This is narrower and more specific (it requires a named target group and a specific harm mechanism, not general rudeness), fully transparent (every decision has a plain-language rationale and a versioned taxonomy behind it, no black-box model), and adds a temporal dimension (escalation tracking across a source) that most single-message APIs don't attempt.

Why rules/regex instead of a machine-learning or LLM-based classifier? Transparency and reproducibility were prioritized over raw accuracy: every decision can be traced to a specific matched pattern and taxonomy row, and results are stable across time given the same taxonomy version. Negation, counter-speech, and obfuscated spelling are handled through explainable heuristics (a negation-cue window, a reporting+condemnation cue pair, a character-substitution map — see Contributing) rather than genuine language understanding, so they generalize only as far as those heuristics reach. An LLM-assisted or hybrid engine is on the roadmap for the cases that fall outside them, and contributions in that direction are welcome.

Can I use my own taxonomy or add new harm mechanisms? Yes — the taxonomy is just a YAML file (TAXONOMY_CONFIG_PATH to override it), and the pattern list per mechanism per language lives in data/patterns/<lang>.yaml. See Contributing.

Does it work in languages other than English? Yes, for 7 additional languages — Spanish, French, Russian, and Arabic at the same detection rigor as English; Igbo, Yoruba, and Hausa as smaller, explicitly experimental seed vocabularies (see Multilingual Support). Any other language falls back to English rather than silently misclassifying. None of the eight — including the "verified" tier — have had native-speaker domain-expert review yet.

What's a "dog-whistle" and why does the engine check for them separately? A coded term with a specific, documented bigoted meaning that looks innocuous without that context (e.g. "globalist" as an antisemitic conspiracy trope). It's not checked "separately" in the sense of a second decision path — it contributes a signal through the exact same scoring pipeline as a taxonomy row, still gated by the same identity-anchor requirement. See Dog-whistle Lexicon.

Does the counter-narrative guidance write a rebuttal for me? No — it's general, templated guidance for the matched harm mechanism (e.g. "lead with accurate statistics" for criminalization-framed content), not a custom-generated rebuttal for your specific input. Auto-generating bespoke rebuttal text is a harder, riskier problem (tone-deaf or factually wrong output) that's out of scope here. See Counter-Narrative Guidance.

What does the provenance hash actually protect against? It makes tampering with a stored observation detectable (recomputing the chain shows exactly which record no longer matches), not impossible — someone with direct database write access could still recompute the whole chain to hide an edit. It's a tamper-evidence mechanism, not encryption or access control; pair it with real database security for anything that matters. See Provenance & Tamper-Evidence.

Is the severity ladder / escalation model scientifically validated? No, and the docs and code comments say so deliberately. It's a simplified, explainable ordering inspired by general narrative-escalation research (see Why this exists), built so a human can audit why a trend was flagged — not a peer-reviewed forecasting model.