Impromptu Cybercrime Euphemism Detection

• URL: http://arxiv.org/abs/2412.01413v2
• Date: Tue, 03 Dec 2024 07:12:27 GMT
• Title: Impromptu Cybercrime Euphemism Detection
• Authors: Xiang Li, Yucheng Zhou, Laiping Zhao, Jing Li, Fangming Liu,
• Abstract summary: We introduce the Impromptu Cybercrime Euphemisms Detection dataset.<n>We propose a detection framework tailored to this problem.<n>Our approach achieves a remarkable 76-fold improvement compared to the previous state-of-the-art euphemism detector.
• Score: 20.969469059941545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting euphemisms is essential for content security on various social media platforms, but existing methods designed for detecting euphemisms are ineffective in impromptu euphemisms. In this work, we make a first attempt to an exploration of impromptu euphemism detection and introduce the Impromptu Cybercrime Euphemisms Detection (ICED) dataset. Moreover, we propose a detection framework tailored to this problem, which employs context augmentation modeling and multi-round iterative training. Our detection framework mainly consists of a coarse-grained and a fine-grained classification model. The coarse-grained classification model removes most of the harmless content in the corpus to be detected. The fine-grained model, impromptu euphemisms detector, integrates context augmentation and multi-round iterations training to better predicts the actual meaning of a masked token. In addition, we leverage ChatGPT to evaluate the mode's capability. Experimental results demonstrate that our approach achieves a remarkable 76-fold improvement compared to the previous state-of-the-art euphemism detector.

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