dokster/adaption-scam-dataset-ru-en-v1

Description

• Task: given one suspicious message, output a validated JSON object with a risk level, scam type, manipulation tactics, a compact "scam DNA" breakdown, a safest action, a message to forward to a trusted person, and a short summary.
• Languages: English (en) and Russian (ru).
• Nature: synthetic / template-generated suspicious messages and benign controls. These are not collected real scams (see Limitations).

	value
Rows	2,324 (1,200 en + 1,124 ru)
Unique base scenarios	1,540
Risk levels	dangerous 1,391 · safe 409 · needs_check 358 · suspicious 166
Scam types	14 fixed values (e.g. credential_theft, job_scam, tech_support, family_impersonation, prize_scam, marketplace_scam, investment_scam, government_impersonation, payment_request, none)
Avg. message length	~185 chars
Avg. completion length	~757 chars

Uses

Direct use

• Instruction fine-tuning to emit the JSON schema for scam-risk triage and prevent fraud.

Out-of-scope use

• Not evaluated for languages other than English and Russian
• Not a validated spam/scam filter for production email or SMS pipelines without additional evaluation on real-world traffic — this data is synthetic (see Limitations)
• Not legal, financial, or cybersecurity advice

Dataset Structure

One row = one (message, analysis) example in one language. CSV columns:

column	type	description
id	string	Stable case id. Russian rows reuse the English id with a _ru suffix.
language	string	en or ru.
system	string	The Jawbreaker system prompt: role, full JSON schema, risk-level rules, and safety rules.
user	string	The user turn: a /no_think wrapper around the suspicious message plus "Return only the JSON object."
assistant	string	Recommended training target. The gold JSON completion (see schema below). Clean, schema-valid, fixed vocabulary.
enhanced_prompt	string	An alternative, reworded single-turn prompt variant.
enhanced_completion	string	An LLM-rewritten variant of the completion. Free-form prose, not schema-consistent — do not use as a training target for JSON-output fine-tuning.
original_context	string	Copy of the system field, kept for provenance.

assistant JSON schema

{
  "risk_level": "dangerous | suspicious | needs_check | safe",
  "scam_type": "string (one of 14 fixed snake_case values)",
  "tactics": ["short string", "..."],
  "scam_dna": {
    "impersonates": "string",
    "pressure": "string",
    "ask": "string",
    "risk": "string"
  },
  "safest_action": "string",
  "trusted_person_message": "string",
  "summary": "string (< 18 words)"
}

What is translated vs. fixed by language. In Russian rows, the human-facing fields are Russian — the message (user), summary, safest_action, trusted_person_message, and the scam_dna value strings. The machine-facing labels stay in fixed English — risk_level, scam_type, and tactics — so downstream validation and metrics are language-independent. Brand names, URLs, and reference codes are preserved verbatim across languages.

How the Dataset Was Created

1. English generation. Suspicious messages and benign controls were produced by the deterministic generator across scam categories (delivery/credential phishing, bank/card fraud, tech-support, job/task, prize/lottery, marketplace, family-impersonation, investment/"wrong number", government/toll) plus everyday-safe and hard-negative "scary but safe" cases. Gold assistant labels come directly from the generator, so English labels are deterministic and schema-consistent.
2. Bilingual expansion. Because the data is template-generated, its translatable text collapses to a small set of unique strings (a few hundred completion phrases and input templates). These unique strings were translated EN→RU with a large language model under strict rules:
   • keep risk_level / scam_type / tactics in English;
   • translate only the natural-language fields into idiomatic Russian;
   • preserve brand names, URLs, and Ref/Note codes exactly. The translations were then mapped back onto every row, giving consistent, schema-valid Russian examples.

Evaluation Set

A separate, held-out "hard" suite is used for evaluation — it does not overlap with the training rows above and is not distributed as part of this dataset's CSV, but is documented here since it targets the same schema and category mix.

	value
File	eval_en.jsonl (632 English cases)
Bilingual subset	eval_ru_en.jsonl (316 en + 316 ru)
Focus	boundary/calibration scenarios — credential theft, family impersonation, tech support, job scams, marketplace fraud, "wrong number" investment scams, plus matched safe/benign controls

Bias, Risks, and Limitations

• Messages are synthetically generated, not observed in the wild; phrasing, scenario mix, and difficulty may not match real scams.
• Scenarios are US/English-first. Even the Russian rows describe US-flavored situations, so patterns common in Russian-speaking markets (fake bank-security calls, Gosuslugi, marketplace/delivery apps, SIM-swap, Telegram/messenger fraud) are under-represented.

Recommendations

• Models trained on this dataset should have their outputs reviewed by a human before acting on a dangerous/suspicious verdict.

Citation

@misc{scam_detection_ru_en_2026,
  title        = {Scam-Risk Analysis: A Bilingual Structured-Output Dataset for Scam Defense},
  author       = {Sultanov Danial},
  year         = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/datasets/dokster/adaption-scam-dataset-ru-en-v1}},
  note         = {AutoScientist Challenge 2026 — Personal-Finance Category}
}