| --- |
| pretty_name: "SCAMBENCH: A Multi-Perspective Benchmark for Online Scam Communication" |
| language: |
| - en |
| license: cc-by-nc-4.0 |
|
|
| configs: |
| - config_name: scam |
| data_files: |
| - split: train |
| path: "Scam/*.csv" |
| features: |
| - name: description |
| dtype: string |
| - name: rewrite_description |
| dtype: string |
| - name: dollars_lost |
| dtype: float64 |
| - name: location |
| dtype: string |
| - name: date_reported |
| dtype: string |
| - name: business_name_used |
| dtype: string |
| - name: annotation |
| dtype: string |
| - name: category |
| dtype: string |
|
|
| - config_name: non_scam |
| data_files: |
| - split: train |
| path: "Non-scam/*.csv" |
| features: |
| - name: description |
| dtype: string |
| - name: category |
| dtype: string |
| --- |
| |
|
|
| # SCAMBENCH: A Multi-Perspective Benchmark for Online Scam Communication |
|
|
| SCAMBENCH is a dataset for studying online scam communication, scam detection, and model robustness. It contains real-world scam messages curated from victim-reported incidents, along with non-scam counterparts and structured annotations. |
|
|
| The dataset is introduced in our paper: |
| > SCAMBENCH: A Multi-Perspective Benchmark for Analyzing and Evaluating Online Scam Communication |
|
|
| --- |
|
|
| ## Dataset Overview |
|
|
| SCAMBENCH consists of: |
|
|
| - **1,836 scam messages** across six scam types |
| - **1,834 non-scam messages** from legitimate sources |
| - Multi-perspective annotations capturing: |
| - persuasion strategies (scammer perspective) |
| - red flags (user perspective) |
| - severity levels (risk and urgency) |
|
|
| In addition, each scam message includes a **rewritten version** where explicit scam cues are reduced, enabling robustness evaluation. |
|
|
| --- |
| ## Dataset Structure |
|
|
| ``` |
| ScamBench/ |
| ├── Scam/ |
| │ ├── Advance_fee_loan.csv |
| │ ├── Employment.csv |
| │ ├── Imposter.csv |
| │ ├── Tech_support.csv |
| │ ├── Lottery.csv |
| │ ├── Investment.csv |
| │ |
| ├── Non-scam/ |
| │ ├── Employment.csv |
| │ ├── Imposter.csv |
| │ ├── Tech_support.csv |
| │ ├── Lottery.csv |
| │ ├── Investment.csv |
| ``` |
| --- |
|
|
| ## Scam Data Format |
|
|
| Each CSV file in the `Scam/` folder contains the following fields: |
|
|
| | Field | Description | |
| |------|------------| |
| | `description` | Original scam message | |
| | `rewrite_description` | Rewritten version with reduced persuasion/red-flag cues | |
| | `dollars_lost` | Reported financial loss (if available) | |
| | `location` | Reported location (may be anonymized or missing) | |
| | `date_reported` | Report date | |
| | `business_name_used` | Name used by scammer (if present) | |
| | `annotation` | JSON object with structured labels | |
| | `category` | Six categories (advance fee loan, employment, imposter, investment, lottery, and tech support) | |
|
|
| --- |
|
|
| ## Non-Scam Data Format |
|
|
| Each CSV file in the `Non-scam/` folder contains the following fields: |
|
|
| | Field | Description | |
| |------|------------| |
| | `description` | Legitimate message | |
| | `category` | Five categories (employment, imposter, investment, lottery, and tech support) | |
|
|
| --- |
|
|
| ## Annotation Schema |
|
|
| The dataset provides three types of annotations: |
|
|
| ### 1. Persuasion Strategies (P1–P7) |
|
|
| Based on Cialdini’s persuasion principles: |
|
|
| - **P1 Reciprocity** – Creating obligation by offering help, rewards, gifts, or favors. |
| - **P2 Commitment & Consistency** – Starting with a small request and escalating to larger demands. |
| - **P3 Social Proof** – Claiming many others have benefited; using testimonials or group behavior. |
| - **P4 Authority** – Invoking titles, institutions, official seals, or legal threats; impersonating authority. |
| - **P5 Liking** – Using flattery, friendliness, shared interests, or emotional bonding. |
| - **P6 Scarcity** – Using urgency, limited-time offers, countdowns, or “last chance” framing. |
| - **P7 Unity** – Claiming shared identity (community, religion, nationality, ethnicity) to build trust. |
|
|
| --- |
|
|
| ### 2. Red Flags (R1–R9) |
|
|
| User-facing warning signals: |
|
|
| - **R1 Unsolicited contact / pitches** |
| - **R2 “Too good to be true” / guaranteed outcomes** |
| - **R3 Pressure to act now** |
| - **R4 Requests personal or financial information** |
| - **R5 Upfront fees / payments required** |
| - **R6 Payment via gift card / wire / crypto** |
| - **R7 Posing as government or official organization** |
| - **R8 Threats of arrest / legal action** |
| - **R9 Grammar / spelling / language issues** |
|
|
| --- |
|
|
| ### 3. Severity Levels |
|
|
| - Low |
| - Medium |
| - High |
| - Critical |
|
|
| These annotations allow analysis beyond binary scam classification. |
|
|
| --- |
|
|
| ## Use Cases |
|
|
| SCAMBENCH can be used for: |
|
|
| - Scam detection and classification |
| - Robustness evaluation (using rewritten messages) |
| - Explainable AI and risk modeling |
| - Human-centered security and scam education |
| - Analysis of scam communication patterns |
|
|
| --- |
|
|
| ## Ethical Considerations |
|
|
| - The dataset is derived from publicly available scam reports. |
| - Rewritten scam messages are generated for research purposes only, to evaluate detection robustness. |
| - This dataset should not be used to generate or facilitate scams. |
|
|
| --- |
| ## License |
|
|
| This dataset is released under the CC BY-NC 4.0 license. |
|
|
