Researchers Expand Synthetic Dataset for Detecting Multi-Turn Smishing Scams Targeting Elders
Researchers have released COVA-X, an expanded synthetic dataset of nearly 11,000 multi-turn conversations for detecting smishing (SMS phishing) scams, tripling the size of their previous COVA dataset. The larger dataset enabled transformer models like Longformer to outperform traditional machine learning approaches like XGBoost for the first time, achieving 79.71% accuracy compared to XGBoost's 78.43%. The findings demonstrate that transformer models require substantial conversational data to leverage their contextual advantages in detecting sophisticated elder-targeted scams.
Researchers have published COVA-X, an expanded synthetic dataset containing 10,985 labeled multi-turn conversations across eight elder-targeted scam categories, building on their prior COVA dataset of 3,201 conversations. The expanded dataset was generated using an improved pipeline that addressed quality issues from the first iteration, including label contamination, mismatches, and prompt-design failures, resulting in a 12.7× improvement in label correction rates. When retraining classifiers on the larger dataset, the Longformer transformer model achieved 79.71% accuracy and 0.7786 macro F1 score, surpassing XGBoost's 78.43% accuracy and 0.7563 F1 score across all evaluation metrics. The research confirms that transformer models' contextual advantages require larger training corpora to manifest, while also documenting per-scam-type outcome variations and architectural improvements that reduced certain artifact rates. The dataset and findings are intended to advance detection capabilities for multi-turn conversational scams targeting vulnerable populations.
What's missing
The paper does not discuss whether COVA-X will be made publicly available for other researchers, the computational costs of training these models, or how the synthetic data compares to real-world smishing conversations in terms of linguistic authenticity and attacker sophistication.
What different sources said
- arXiv cs.CLCenter
An Expanded Synthetic Conversation Dataset for Multi-Turn Smishing Detection
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