Researchers Expand Synthetic Dataset for Detecting Multi-Turn Smishing Scams Targeting Elders
Researchers have released COVA-X, an expanded synthetic dataset of 10,985 multi-turn conversational smishing (SMS phishing) examples spanning eight elder-targeted scam categories, improving upon their earlier 3,201-conversation COVA dataset. The new dataset enabled transformer-based model Longformer to outperform XGBoost for the first time, achieving 79.71% accuracy and 0.7786 macro F1 score. The work demonstrates that transformer models require larger conversational corpora to leverage their contextual advantages in detecting sophisticated text-based scams.
The study introduces COVA-X, a synthetically generated dataset of 10,985 labeled multi-turn conversations designed to train and evaluate automated detection of smishing — phishing attacks conducted via SMS. The dataset targets eight categories of scams commonly directed at elderly individuals and was produced by an improved generation pipeline that addressed several quality failures in the original COVA dataset, including label mismatches, prompt-design errors, and narrative artifacts. A key quality improvement was a 12.7-fold reduction in label correction rate, dropping from 49.8% to 3.9%, alongside architectural interventions that reduced virtual-kidnapping artifact rates from 67.1% to 46.5%. Retraining classifiers on the expanded dataset produced the study's central finding: the Longformer transformer model surpassed XGBoost across all evaluation metrics, reversing the prior dataset's results where XGBoost had led with 72.5% accuracy. The authors interpret this reversal as direct evidence that transformer models need larger conversational corpora to realize their contextual modeling advantages. A pre/post-cleanup sensitivity analysis further confirmed that dataset refinement recovered genuine label-relevant signal across all three classifier architectures tested. Per-scam-type analysis also showed that different scam categories modulate model performance in ways consistent with their underlying mechanisms.
What's missing
The dataset is entirely synthetic, and the study does not report validation against real-world smishing conversations, leaving open questions about how well findings generalize to actual scam interactions. The virtual-kidnapping artifact rate, while reduced, remains high at 46.5%, and the paper does not fully resolve what downstream impact this residual contamination has on model reliability. Additionally, the study does not address potential demographic or linguistic biases introduced by the synthetic generation pipeline.
What different sources said
- arXiv cs.CLCenter
An Expanded Synthetic Conversation Dataset for Multi-Turn Smishing Detection
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