π Whatβs in v0.1? A few structured scam examples (text-based) Covers DeFi, crypto, phishing, and social engineering Initial labelling format for scam classification
β οΈ This is not a full dataset yet (samples are currently available). Just establishing the structure + getting feedback.
π Current Schema & Labelling Approach "instruction" β Task prompt (e.g., "Evaluate this message for scams") "input" β Source & message details (e.g., Telegram post, Tweet) "output" β Scam classification & risk indicators
ποΈ Current v0.1 Sample Categories Crypto Scams β Meme token pump & dumps, fake DeFi projects Phishing β Suspicious finance/social media messages Social Engineering β Manipulative messages exploiting trust
π Next Steps - Expanding datasets with more phishing & malware examples - Refining schema & annotation quality - Open to feedback, contributions, and suggestions
If this is something you might find useful, bookmark/follow/like the dataset repo <3
π¬ Thoughts, feedback, and ideas are always welcome! Drop a comment or DMs are open π€