An Expanded Synthetic Conversation Dataset for Multi-Turn Smishing Detection

• URL: http://arxiv.org/abs/2606.06879v1
• Date: Fri, 05 Jun 2026 03:46:36 GMT
• Title: An Expanded Synthetic Conversation Dataset for Multi-Turn Smishing Detection
• Abstract summary: We present COVA-X, an expanded dataset of 10,985 conversations spanning eight elder-targeted scam categories.<n>Longformer now surpasses XGBoost on all evaluation metrics, achieving 79.71% accuracy and 0.7786 macro F1 compared with 78.43% and 0.7563 for XGBoost.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Our prior work introduced COVA, a synthetically generated multi-turn conversational smishing dataset of 3,201 labeled conversations, establishing baseline detection benchmarks across eight models. While XGBoost with TF-IDF features achieved the best performance, with 72.5\% accuracy and 0.691 macro F1, transformer models underperformed, which was attributed to input truncation and insufficient training data. We present COVA-X, an expanded dataset of 10,985 conversations spanning eight elder-targeted scam categories, produced by an improved generation pipeline addressing contamination, label mismatch, stage-direction bleed, and prompt-design failures from the first iteration. Retraining all classifiers on the expanded dataset yields the central finding of this work: Longformer now surpasses XGBoost on all evaluation metrics, achieving 79.71\% accuracy and 0.7786 macro F1 compared with 78.43\% and 0.7563 for XGBoost. This directly confirms that transformer models require larger conversational corpora to realize their contextual advantages. We additionally document a quality life-cycle including a 12.7$\times$ improvement in label correction rate, from 49.8\% to 3.9\%, an architectural intervention reducing virtual-kidnapping artifact rates from 67.1\% to 46.5\%, and a per-scam-type outcome analysis showing that scam categories modulate results in mechanism-consistent ways. A pre/post-cleanup sensitivity analysis confirms that dataset refinement recovers genuine label-relevant signal across all three classifier architectures.

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